├── spatial-to-pseudobulk.py
├── spatial-gene-set-scoring.py
├── single-cell
    ├── single-cell-scvi-integrate.py
    ├── single-cell-generate-c2l-celltype-reference.py
    ├── single-cell-quality-control.ipynb
    ├── single-cell-nmf-analysis.py
    └── single-cell-preprocessing.ipynb
├── README.md
├── spatial-c2l-cell-type-reference-mapping.py
├── spatial-qc-and-normalization.ipynb
├── spatial-region-ligand-receptor-signaling-analysis.py
├── spatial-dotplot-ar-basal-club-markers.py
├── spatial-dotplot-chemokine-expression.py
├── spatial_analysis_environment.yml
├── LICENSE
└── single_cell_analysis_environment.yml


/spatial-to-pseudobulk.py:
--------------------------------------------------------------------------------
 1 | # Author: Antti Kiviaho
 2 | # Date: 14.2.2024
 3 | # 
 4 | # Updated 1.3.2024
 5 | # Concatenating Visium sections into a pseudobulk for later testing
 6 | 
 7 | import os 
 8 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')
 9 | 
10 | import scanpy as sc
11 | import numpy as np
12 | import pandas as pd
13 | from tqdm import tqdm
14 | 
15 | from utils import load_from_pickle,get_sample_ids_reorder
16 | 
17 | 
18 | samples = get_sample_ids_reorder()
19 | # Create an object to save to
20 | pseudobulk_df = pd.DataFrame()
21 | 
22 | for s in tqdm(samples,desc='Processing samples'):
23 | 
24 |     # Copy the slide
25 |     slide = sc.read_h5ad('./data/visium_with_regions/'+s+'_with_regions.h5ad')
26 | 
27 |     # Calculate a sum over the spots to get to a pseudobulk
28 |     s_pseudobulk_df = pd.DataFrame(slide.layers['counts'].sum(axis=0).T,index=slide.var.index,columns=[s])
29 |     
30 |     # Merge to create a dataframe with samples as columns, genes as index
31 |     pseudobulk_df = pd.merge(pseudobulk_df,s_pseudobulk_df,left_index=True,right_index=True,how='outer')
32 | 
33 | # Fill empty
34 | pseudobulk_df = pseudobulk_df.fillna(0)
35 | 
36 | pseudobulk_df.to_csv('data/spatial_pseudobulk_unnormalized.csv')
37 | 


--------------------------------------------------------------------------------
/spatial-gene-set-scoring.py:
--------------------------------------------------------------------------------
 1 | import os 
 2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')
 3 | 
 4 | import scanpy as sc
 5 | import numpy as np
 6 | import pandas as pd
 7 | 
 8 | from tqdm import tqdm
 9 | from utils import get_sample_ids_reorder, save_to_pickle
10 | samples = get_sample_ids_reorder()
11 | 
12 | import warnings
13 | warnings.filterwarnings("ignore")
14 | 
15 | if __name__ == '__main__':
16 | 
17 | ######### Scanpy scoring method ###########
18 | 
19 |     gene_set_df = pd.read_excel('gene_sets_of_interest.xlsx',header=None).drop(columns=0).set_index(1).T
20 |     custom_gene_set_scanpy_scores = {}
21 | 
22 |     # Calculate the scanpy scores for custom gene sets
23 |     for sample in tqdm(samples, desc="Processing sample", unit="sample"):
24 |         slide = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')
25 | 
26 |         for col in gene_set_df.columns:
27 |             sc.tl.score_genes(slide,gene_set_df[col].dropna(),score_name=col,random_state=2531035)
28 | 
29 |         custom_gene_set_scanpy_scores[sample] = slide.obs[gene_set_df.columns].copy()
30 | 
31 |         # Save the dict object
32 |         save_to_pickle(custom_gene_set_scanpy_scores,'./data/spatial_scanpy_score_results.pkl')
33 | 
34 |     print('scoring done!')


--------------------------------------------------------------------------------
/single-cell/single-cell-scvi-integrate.py:
--------------------------------------------------------------------------------
 1 | from utils import load_from_pickle, save_to_pickle
 2 | import scanpy as sc
 3 | import anndata as ad
 4 | import scib
 5 | import scvi
 6 | import seaborn as sns
 7 | import torch
 8 | import os
 9 | from matplotlib import pyplot as plt
10 | from datetime import datetime
11 | 
12 | # Author: Antti Kiviaho
13 | # Date: 27.2.2023
14 | #
15 | # A script for running scvi integration on single cell datasets
16 | # Dong 2020, Chen 2021, Cheng 2022, Chen 2022, Song 2022, Wong 2022, Hirz 2023
17 | 
18 | if __name__ == "__main__":
19 | 
20 |     current_date = datetime.today().strftime('%Y%m%d')
21 | 
22 |     ###############
23 | 
24 |     # Load the data
25 |     adata = load_from_pickle('./sc-reference/normalized_sc_7_datasets.pickle')
26 |     adata = ad.concat(adata)
27 |     adata.obs_names_make_unique() # Some duplicate indices persists
28 | 
29 |     # Preprocess and scale
30 |     adata.obs.dataset = adata.obs.dataset.astype('category')
31 |     scib.preprocessing.scale_batch(adata,batch='dataset')
32 |     print('Scaling done...')
33 | 
34 |     adata.raw = adata
35 | 
36 |     adata = scib.preprocessing.hvg_batch(adata,batch_key='dataset',target_genes=2000,flavor='seurat',adataOut=True)
37 |     print('HVGs calculated...')
38 | 
39 |     print('CUDA is available: ' + str(torch.cuda.is_available()))
40 |     print('GPUs available: ' + str(torch.cuda.device_count()))
41 | 
42 |     print('Initiating training on GPU ...')
43 |     scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="dataset")
44 |     vae = scvi.model.SCVI(adata, n_layers=2, n_latent=30, gene_likelihood="nb")
45 | 
46 |     vae.train(use_gpu=True)
47 | 
48 |     vae.save('scvi_model_'+current_date)
49 |     adata.obsm["X_scVI"] = vae.get_latent_representation()
50 | 
51 |     sc.pp.neighbors(adata, use_rep="X_scVI",random_state=745634)
52 |     sc.tl.umap(adata,random_state=745634)
53 |     sc.tl.leiden(adata, key_added="VI_clusters")
54 |     print('NN graph, UMAP & Leiden ready...')
55 | 
56 |     save_to_pickle(adata,'scvi_integrated_7_sc_datasets_'+current_date+'.pkl')
57 |     print('SCVI integration saved...')
58 | 


--------------------------------------------------------------------------------
/single-cell/single-cell-generate-c2l-celltype-reference.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial')
 3 | 
 4 | import scanpy as sc
 5 | import numpy as np
 6 | import matplotlib.pyplot as plt
 7 | import matplotlib as mpl
 8 | 
 9 | 
10 | os.environ["THEANO_FLAGS"] = 'device=cuda,floatX=float32,force_device=True'
11 | import cell2location
12 | 
13 | from matplotlib import rcParams
14 | rcParams['pdf.fonttype'] = 42 # enables correct plotting of text for PDFs
15 | 
16 | 
17 | if __name__ == '__main__':
18 |     
19 |     #########
20 | 
21 |     # Change model setup in terms of layer & labels !
22 | 
23 |     # Change these paths when re-running
24 |     results_folder = './c2l-results/cell2location_map_20230908'
25 |     adata_ref_path = './single_cell_reference_with_nmf_derived_annotations_20230908.h5ad'
26 |     ########
27 | 
28 |     # create paths and names to results folders for reference regression and cell2location models
29 |     ref_run_name = results_folder + 'reference_signatures/'
30 | 
31 | 
32 |     # Check if paths exist, and create them if not
33 |     if not os.path.exists(results_folder):
34 |         os.makedirs(results_folder)
35 |     if not os.path.exists(ref_run_name):
36 |         os.makedirs(ref_run_name)
37 | 
38 |     adata_ref = sc.read_h5ad(adata_ref_path)  
39 | 
40 |     # prepare anndata for the regression model
41 |     cell2location.models.RegressionModel.setup_anndata(adata=adata_ref,
42 |                         #    layer='counts',
43 |                             # 10X reaction / sample / batch
44 |                             batch_key='dataset',
45 |                             # cell type, covariate used for constructing signatures
46 |                             labels_key='final_annotation',
47 |                             categorical_covariate_keys=['sample']
48 |                         )
49 | 
50 | 
51 |     # create the regression model
52 |     from cell2location.models import RegressionModel
53 |     mod = RegressionModel(adata_ref)
54 | 
55 |     # view anndata_setup as a sanity check
56 |     mod.view_anndata_setup()
57 |     mod.train(max_epochs=250, use_gpu=True)
58 | 
59 |     # In this section, we export the estimated cell abundance (summary of the posterior distribution).
60 |     adata_ref = mod.export_posterior(
61 |         adata_ref, sample_kwargs={'num_samples': 1000, 'batch_size': 2500, 'use_gpu': True}
62 |     )
63 | 
64 |         # Save model
65 |     mod.save(ref_run_name, overwrite=True)
66 | 
67 |     # Save anndata object with results
68 |     adata_file = ref_run_name + 'sc_reference_signatures.h5ad'
69 |     adata_ref.write(adata_file)
70 |     adata_file
71 | 
72 |     mod.plot_history(20)
73 |     plt.savefig(results_folder+'c2l_single_cell_reference_training_history.png')
74 |     plt.clf()
75 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | ### Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer
 2 | 
 3 | [Link to the study](https://doi.org/10.1038/s41467-024-54364-1)
 4 | 
 5 | **Author:** Antti Kiviaho
 6 | 
 7 | **Email:** antti.kiviaho@tuni.fi
 8 | 
 9 | **Last modified:** 29.11.2024
10 | 
11 | This repository contains necessary code to reproduce results presented in the manuscript.
12 | 
13 | ### Package versions:
14 | - **single_cell_analysis_environment.yml** – Containts package versions used in the analysis of spatial transcriptomics data.
15 | - **spatial_analysis_environment.yml** – Containts package versions used in the analysis of single-cell data.
16 | 
17 | ## Spatial Transcriptomics data
18 | 
19 | Files related to ST data analysis
20 | 
21 | ### Data preprocessing and computations
22 | 
23 | - **spatial-qc-and-normalization.ipynb** – Quality control and preprocessing of spatial transcriptomics data.
24 | 
25 | - **spatial-c2l-cell-type-reference-mapping.py** – Spatial transcriptomics data deconvolution using the cell type reference created from single-cell data.
26 | 
27 | - **spatial-post-c2l-cell-type-mapping.ipynb** – Division of spatial transcriptomics data into single-cell mapping-based (SCM) regions.
28 | 
29 | - **spatial-gene-set-scoring.py** – Gene set scoring on spatial data.
30 | 
31 | - **spatial-region-ligand-receptor-signaling-analysis.py** – Ligand-receptor interaction analysis.
32 | 
33 | - **spatial-to-pseudobulk.py** – Generating pseudobulk expression data from spatial transcriptomics data.
34 | 
35 | ### Data analysis and plotting results
36 | 
37 | - **spatial-gene-expression-analysis.ipynb** – Gene expression analysis and plots of spatial transcriptomics data. (Figures 1c, 2d)
38 | 
39 | - **spatial-gene-set-score-analysis.ipynb** – Gene set scoring-based plotting (Figures 3a, 3b, 3c, 4a, 4b, 4c, 4d).
40 | 
41 | - **spatial-dotplot-ar-basal-club-markers.py** – Plot 2c generation.
42 | 
43 | - **spatial-dotplot-chemokine-expression.py** – Plot 3d generation.
44 | 
45 | - **multiplex_ihc_staining_analysis.ipynb** – Plots from mIHC cell classifier results (Figures 4h, 4i)
46 | 
47 | - **spatial-mapping-based-clusters-receptor-ligand-analyses.ipynb** – Ligand-receptor interaction analysis-based plotting (Supplementary Figure 8).
48 | 
49 | - **spatial-metastatic-tumor-sample-analysis.ipynb.ipynb** – Analysis of metastatic prostate cancer spatial transcriptomics samples (Figures 5d, 5e).
50 | 
51 | - **spatial-pseudobulk-data-analysis.ipynb** – Pseudobulk spatial transcriptomics data analysis and plots (Figures 5f, 5i).
52 | 
53 | - **public-sc-and-bulk-data-analysis.ipynb** – Analysis of public data He et al. 2021 + TCGA and SU2C gene expression data analyses (Figures 5a, 5b, 5g, 5h).
54 | 
55 | ## Single-cell data analysis 
56 | 
57 | Files related to single-cell data used in the article
58 | 
59 | - **single-cell-preprocessing.ipynb** – Preprocessing of single-cell datasets to attain uniform format.
60 | 
61 | - **single-cell-quality-control.ipynb** – Gene filtering, doublet removal, and normalization steps carried out on each dataset individually.
62 | 
63 | - **single-cell-scvi-integrate.py** – scVI-based integration of 7 preprocessed single-cell datasets to find a common embedding.
64 | 
65 | - **single-cell-post-integration.ipynb** – Gene marker-based cell type annotation on the integrated dataset. Removal of sample-specfic clusters.
66 | 
67 | - **single-cell-nmf-analysis.py** – Non-negative matrix factorization-based annotation of cell type-specific gene expression modules.
68 | 
69 | - **single-cell-immune-celltypist.ipynb** – Celltypist-based annotation of cells annotated as immune cells on the previous round (broad annotation).
70 | 
71 | - **single-cell-gene-module-based-annotation.ipynb** – Non-immune cell subtype annotation based on the NMF gene expression modules.
72 | 
73 | - **single-cell-generate-c2l-celltype-reference.py** – Annotation-based regression of cell2location-compatible cell type signatures.
74 | 


--------------------------------------------------------------------------------
/spatial-c2l-cell-type-reference-mapping.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial')
  3 | 
  4 | import scanpy as sc
  5 | import numpy as np
  6 | import matplotlib.pyplot as plt
  7 | import matplotlib as mpl
  8 | 
  9 | 
 10 | os.environ["THEANO_FLAGS"] = 'device=cuda,floatX=float32,force_device=True'
 11 | import cell2location
 12 | 
 13 | from matplotlib import rcParams
 14 | rcParams['pdf.fonttype'] = 42 # enables correct plotting of text for PDFs
 15 | 
 16 | from utils import load_from_pickle
 17 | 
 18 | results_folder = './c2l-results/'
 19 | date = '20240125'
 20 | # create paths and names to results folders for reference regression and cell2location models
 21 | run_name = results_folder + 'cell2location_map_'+ date + '/'
 22 | 
 23 | 
 24 | 
 25 | if __name__ == '__main__':
 26 | 
 27 |     # Load the single-cell cell type reference: export estimated expression in 'cell type'
 28 |     # cell2location_map_20230908/reference_signatures is the correct one
 29 |     adata_ref = sc.read_h5ad('c2l-results/cell2location_map_20230908/reference_signatures/sc_reference_signatures.h5ad')
 30 | 
 31 |     if 'means_per_cluster_mu_fg' in adata_ref.varm.keys():
 32 |         inf_aver = adata_ref.varm['means_per_cluster_mu_fg'][[f'means_per_cluster_mu_fg_{i}'
 33 |                                         for i in adata_ref.uns['mod']['factor_names']]].copy()
 34 |     else:
 35 |         inf_aver = adata_ref.var[[f'means_per_cluster_mu_fg_{i}'
 36 |                                         for i in adata_ref.uns['mod']['factor_names']]].copy()
 37 |     inf_aver.columns = adata_ref.uns['mod']['factor_names']
 38 |     
 39 |     del adata_ref
 40 | 
 41 | 
 42 |     # Load visium data and set it up properly (raw, unnormalized counts)
 43 |     adata_vis_individually = load_from_pickle('./data/normalized_no_pathology_filter_visium_data.pkl')
 44 | 
 45 |     # ADDED 25.1.2024 – concatenate also the available ARNEO samples
 46 |     adata_vis_arneo = load_from_pickle('./arneo/data/normalized_arneo_visium_data.pkl')
 47 |     adata_vis_individually.update(adata_vis_arneo)
 48 | 
 49 |     adata_vis = sc.concat(adata_vis_individually)
 50 |     del adata_vis_individually
 51 |     
 52 |     # find shared genes and subset both anndata and reference signatures
 53 |     intersect = np.intersect1d(adata_vis.var_names, inf_aver.index)
 54 |     adata_vis = adata_vis[:, intersect].copy()
 55 |     inf_aver = inf_aver.loc[intersect, :].copy()
 56 | 
 57 |     # prepare anndata for cell2location model
 58 |     cell2location.models.Cell2location.setup_anndata(adata=adata_vis,
 59 |                                                       batch_key='sample_id',
 60 |                                                       layer='counts')
 61 | 
 62 |     print('adata_vis prior to training:')
 63 |     adata_vis
 64 | 
 65 |     # create and train the model
 66 |     mod = cell2location.models.Cell2location(
 67 |         adata_vis, cell_state_df=inf_aver,
 68 |         # the expected average cell abundance: tissue-dependent
 69 |         # hyper-prior which can be estimated from paired histology:
 70 |         N_cells_per_location=21,
 71 |         # hyperparameter controlling normalisation of
 72 |         # within-experiment variation in RNA detection:
 73 |         detection_alpha=20
 74 |     )
 75 |     mod.view_anndata_setup()
 76 | 
 77 |     # Train the model
 78 |     mod.train(max_epochs=30000,
 79 |           # train using full data (batch_size=None)
 80 |           batch_size=30851, # this number is for Tampere + ARNEO ADT/APA 4 batches, three batches Tampere only 34000
 81 |           # use all data points in training because
 82 |           # we need to estimate cell abundance at all locations
 83 |           train_size=1,
 84 |           use_gpu=True,
 85 |          )
 86 | 
 87 |     # In this section, we export the estimated cell abundance (summary of the posterior distribution).
 88 |     adata_vis = mod.export_posterior(
 89 |         adata_vis, sample_kwargs={'num_samples': 1000, 'batch_size': mod.adata.n_obs, 'use_gpu': True}
 90 |     )
 91 | 
 92 |     # Save anndata object with results
 93 |     adata_file = 'visium_adata_with_c2l_mapping_'+date+'.h5ad'
 94 |     adata_vis.write(adata_file)
 95 |     adata_file
 96 | 
 97 |     # Save model
 98 |     mod.save(run_name, overwrite=True)
 99 | 
100 |     ## PLOTTING ##
101 |     # plot ELBO loss history during training, removing first 100 epochs from the plot
102 |     mod.plot_history(1000)
103 |     plt.legend(labels=['full data training'])
104 |     plt.savefig('c2l_mapping_model_training_ELBO_'+date+'.png')
105 |     plt.clf()
106 | 
107 | 


--------------------------------------------------------------------------------
/single-cell/single-cell-quality-control.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# Author: Antti Kiviaho\n",
 10 |     "# Date: 22.2.2023\n",
 11 |     "# Notebook for running dataset normalization and integration\n",
 12 |     "# Uses the scib integration environment and pipeline:\n",
 13 |     "#\n",
 14 |     "# 1. Cell and gene QC filtering\n",
 15 |     "# 2. scran normalization through scib\n",
 16 |     "# 3. batch-aware scaling with scib (implemented in single-cell-scvi-integrate.py)\n",
 17 |     "# 4. batch-aware HVGs with scib (implemented in single-cell-scvi-integrate.py)\n",
 18 |     "# 5. scvi-integration to find a shared latent space"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": null,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "import os\n",
 28 |     "os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')\n",
 29 |     "import numpy as np\n",
 30 |     "import anndata as ad\n",
 31 |     "import scanpy as sc\n",
 32 |     "import pandas as pd\n",
 33 |     "import seaborn as sns\n",
 34 |     "import scib\n",
 35 |     "import matplotlib.pyplot as plt\n",
 36 |     "from scipy import sparse\n",
 37 |     "from pathlib import Path\n",
 38 |     "from scripts.utils import load_from_pickle, save_to_pickle\n",
 39 |     "import warnings\n",
 40 |     "warnings.filterwarnings('ignore')"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": null,
 46 |    "metadata": {},
 47 |    "outputs": [],
 48 |    "source": [
 49 |     "datasets = ['dong_2020','chen_2021','cheng_2022','chen_2022','song_2022','wong_2022','hirz_2023']\n",
 50 |     "adata_dict = {}\n",
 51 |     "for dataset_id in datasets:\n",
 52 |     "    adata = sc.read_h5ad('./sc-reference/'+dataset_id+'/adata_obj.h5ad')\n",
 53 |     "    adata_dict[dataset_id] = adata"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": null,
 59 |    "metadata": {},
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "def qc_filters(adata, remove_doublets=True):\n",
 63 |     "    # requires scib-pipline-R4.0 conda environment !\n",
 64 |     "    # import scib\n",
 65 |     "    # Filter out cells by using a hybrid of the original publications thresholds\n",
 66 |     "    sc.pp.filter_cells(adata, min_counts=600)\n",
 67 |     "    sc.pp.filter_cells(adata, min_genes = 300)\n",
 68 |     "    sc.pp.filter_genes(adata, min_counts= 10)\n",
 69 |     "    # Leave out cells with > 20% mitochondrial reads\n",
 70 |     "    adata = adata[adata.obs.pct_counts_mt < 20, :]\n",
 71 |     "    if remove_doublets:\n",
 72 |     "        sc.external.pp.scrublet(adata)\n",
 73 |     "        adata = adata[adata.obs['predicted_doublet']==False]\n",
 74 |     "    \n",
 75 |     "\n",
 76 |     "    return adata"
 77 |    ]
 78 |   },
 79 |   {
 80 |    "cell_type": "code",
 81 |    "execution_count": null,
 82 |    "metadata": {},
 83 |    "outputs": [],
 84 |    "source": [
 85 |     "for dset in datasets:\n",
 86 |     "    adata = adata_dict[dset].copy()\n",
 87 |     "    if not sparse.issparse(adata.X):\n",
 88 |     "        adata.X = sparse.csr_matrix(adata.X)\n",
 89 |     "    \n",
 90 |     "    adata = qc_filters(adata)\n",
 91 |     "        \n",
 92 |     "    scib.preprocessing.normalize(adata,precluster=False, sparsify=False)\n",
 93 |     "    # add ids to the data for use after data concatenation\n",
 94 |     "    adata.obs['dataset'] = dset\n",
 95 |     "    adata_dict[dset] = adata\n",
 96 |     "    del adata"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": null,
102 |    "metadata": {},
103 |    "outputs": [],
104 |    "source": [
105 |     "save_to_pickle(adata_dict,'./sc-reference/normalized_sc_7_datasets.pickle')"
106 |    ]
107 |   }
108 |  ],
109 |  "metadata": {
110 |   "kernelspec": {
111 |    "display_name": "Python 3 (ipykernel)",
112 |    "language": "python",
113 |    "name": "python3"
114 |   },
115 |   "language_info": {
116 |    "codemirror_mode": {
117 |     "name": "ipython",
118 |     "version": 3
119 |    },
120 |    "file_extension": ".py",
121 |    "mimetype": "text/x-python",
122 |    "name": "python",
123 |    "nbconvert_exporter": "python",
124 |    "pygments_lexer": "ipython3",
125 |    "version": "3.7.12"
126 |   },
127 |   "orig_nbformat": 4,
128 |   "vscode": {
129 |    "interpreter": {
130 |     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
131 |    }
132 |   }
133 |  },
134 |  "nbformat": 4,
135 |  "nbformat_minor": 2
136 | }
137 | 


--------------------------------------------------------------------------------
/spatial-qc-and-normalization.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# Author: Antti Kiviaho\n",
 10 |     "# Date: 20.1.2023\n",
 11 |     "# A script for running normalization and sample integration clustering.\n",
 12 |     "# Uses the scbi integration environment and pipeline:\n",
 13 |     "#\n",
 14 |     "#\n",
 15 |     "# 1. Cell and gene filtering\n",
 16 |     "# 2. scran normalization through R interface using\n"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": null,
 22 |    "metadata": {},
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "import os\n",
 26 |     "os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')\n",
 27 |     "import numpy as np\n",
 28 |     "import anndata as ad\n",
 29 |     "import scanpy as sc\n",
 30 |     "import pandas as pd\n",
 31 |     "import seaborn as sns\n",
 32 |     "import scib\n",
 33 |     "\n",
 34 |     "import matplotlib.pyplot as plt\n",
 35 |     "\n",
 36 |     "# Added spot exclusion information 28.2.2024\n",
 37 |     "from scripts.utils import get_sample_ids_reorder, save_to_pickle, get_include_exclude_info"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": null,
 43 |    "metadata": {},
 44 |    "outputs": [],
 45 |    "source": [
 46 |     "def qc_and_normalize(adata):\n",
 47 |     "    # QC and normalize\n",
 48 |     "    sc.pp.filter_genes(adata, min_cells=5)\n",
 49 |     "    sc.pp.filter_cells(adata, min_counts=500)\n",
 50 |     "    scib.preprocessing.normalize(adata,precluster=False)\n",
 51 |     "    return adata\n"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "# This non-pathology-filtered dictionary was used for cell2location\n",
 61 |     "# As the annotation wasn't available at the time of mapping\n",
 62 |     "\n",
 63 |     "samples = get_sample_ids_reorder(['BPH','untreated','bicalutamide','goserelin','CRPC'])\n",
 64 |     "samples_dict = {} # A data structure for saving data\n",
 65 |     "for sample_id in samples:\n",
 66 |     "  adata_sample = sc.read_visium('./results/'+sample_id+'/outs/',library_id=sample_id)\n",
 67 |     "  adata_sample.var_names_make_unique()\n",
 68 |     "  adata_sample.obs_names = sample_id + '_' + adata_sample.obs_names # add ids to the data for use after data concatenation\n",
 69 |     "  adata_sample = qc_and_normalize(adata_sample) # QC and normalize – this filters out more spots\n",
 70 |     "  samples_dict[sample_id] = adata_sample.copy()\n",
 71 |     "  print(sample_id + ' done')\n",
 72 |     "  del adata_sample\n",
 73 |     "\n",
 74 |     "save_to_pickle(samples_dict,'./data/normalized_no_pathology_filter_visium_data.pkl')\n",
 75 |     "\n"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": null,
 81 |    "metadata": {},
 82 |    "outputs": [],
 83 |    "source": [
 84 |     "# Revised on 28.2.2024:\n",
 85 |     "# Remove regions with exclude/include annotation (exclude_info)\n",
 86 |     "# Save each sample separately for better memory management\n",
 87 |     "\n",
 88 |     "samples = get_sample_ids_reorder()\n",
 89 |     "exclude_info = get_include_exclude_info()\n",
 90 |     "\n",
 91 |     "\n",
 92 |     "obs_data_list = [] # A data structure for saving info on valid spots\n",
 93 |     "for sample_id in samples:\n",
 94 |     "    \n",
 95 |     "  adata_sample = sc.read_visium('./results/'+sample_id+'/outs/',library_id=sample_id)\n",
 96 |     "  adata_sample.var_names_make_unique()\n",
 97 |     "\n",
 98 |     "  # add ids to the data for use after data concatenation\n",
 99 |     "  adata_sample.obs_names = sample_id + '_' + adata_sample.obs_names\n",
100 |     "\n",
101 |     "  # Only use this with Tampere cohort samples\n",
102 |     "  # Subset spots from a single sample\n",
103 |     "  sample_exclude_info = exclude_info.loc[adata_sample.obs_names].copy()\n",
104 |     "  sample_spots_to_keep =  sample_exclude_info[~sample_exclude_info['Pathology'].isin(['Exclude','Lumen'])].index\n",
105 |     "  # Subset the sample with spots to keep\n",
106 |     "  adata_sample = adata_sample[sample_spots_to_keep]\n",
107 |     "  \n",
108 |     "  # QC and normalize – this filters out more spots\n",
109 |     "  adata_sample = qc_and_normalize(adata_sample)\n",
110 |     "\n",
111 |     "  # Save the object\n",
112 |     "  adata_sample.write_h5ad('data/normalized_visium/'+sample_id+'_normalized.h5ad')\n",
113 |     "  \n",
114 |     "  # Save the obs data to a list\n",
115 |     "  obs_data_list.append(adata_sample.obs)\n",
116 |     "\n",
117 |     "  print(sample_id + ' done')\n",
118 |     "  del adata_sample\n",
119 |     "\n",
120 |     "# Save the ids of all spots that passed the qc (110681)\n",
121 |     "pd.DataFrame(index=pd.concat(obs_data_list).index).to_csv('./data/post_qc_and_pathology_annot_valid_spots.csv')\n",
122 |     "\n"
123 |    ]
124 |   }
125 |  ],
126 |  "metadata": {
127 |   "kernelspec": {
128 |    "display_name": "Python 3",
129 |    "language": "python",
130 |    "name": "python3"
131 |   },
132 |   "language_info": {
133 |    "codemirror_mode": {
134 |     "name": "ipython",
135 |     "version": 3
136 |    },
137 |    "file_extension": ".py",
138 |    "mimetype": "text/x-python",
139 |    "name": "python",
140 |    "nbconvert_exporter": "python",
141 |    "pygments_lexer": "ipython3",
142 |    "version": "3.7.12"
143 |   },
144 |   "orig_nbformat": 4
145 |  },
146 |  "nbformat": 4,
147 |  "nbformat_minor": 2
148 | }
149 | 


--------------------------------------------------------------------------------
/spatial-region-ligand-receptor-signaling-analysis.py:
--------------------------------------------------------------------------------
  1 | import os 
  2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')
  3 | 
  4 | import scanpy as sc
  5 | import numpy as np
  6 | import pandas as pd
  7 | import anndata as ad
  8 | import squidpy as sq
  9 | 
 10 | from tqdm import tqdm
 11 | 
 12 | import matplotlib.pyplot as plt
 13 | from utils import load_from_pickle, get_sample_ids_reorder, get_sample_crop_coords, save_to_pickle
 14 | 
 15 | import seaborn as sns
 16 | sns.set_theme(style='white')
 17 | 
 18 | import warnings
 19 | warnings.filterwarnings("ignore")
 20 | 
 21 | samples = get_sample_ids_reorder()
 22 | sample_crop_coord = get_sample_crop_coords()
 23 | 
 24 | color_dict = {
 25 |  'Tumor': '#fc8d62',
 26 |  'Luminal epithelium': '#8da0cb',
 27 |  'Basal epithelium': '#66c2a5',
 28 |  'Club epithelium': '#ffd92f',
 29 |  'Immune': '#a6d854',
 30 |  'Endothelium': '#e78ac3',
 31 |  'Fibroblast': '#e5c494',
 32 |  'Muscle': '#b3b3b3'
 33 |  }
 34 | 
 35 | regions = list(color_dict.keys())
 36 | region_colors = list(color_dict.values())
 37 | 
 38 | ## Define source and target regions
 39 | source = 'Club epithelium'
 40 | target = 'Muscle' # Iterate over the other regions:  'Tumor', 'Luminal epithelium', 'Basal epithelium', 'Immune', 'Endothelium', 'Fibroblast', 'Muscle'
 41 | 
 42 | # Define functions
 43 | 
 44 | def get_spot_interfaces(dat, cluster_of_interest, interaction_cluster, annotation_key='predicted_region', added_key='proximity_analysis'):
 45 | 
 46 |     # Create an observation column for spatial segmentation
 47 |     dat.obs[added_key] = np.nan
 48 |     distance_mat = dat.obsp['spatial_distances'].todense()
 49 | 
 50 |     for idx, obs_name in enumerate(dat.obs_names):
 51 |         cl = dat.obs[annotation_key][idx]
 52 | 
 53 |         if cl in [cluster_of_interest, interaction_cluster]:
 54 |             first_nhbor_idxs = np.where(distance_mat[:, idx] == 1.0)[0]  # Get first-term neighbor indices
 55 | 
 56 |             try:
 57 |                 n_cl_neighbors = dat[first_nhbor_idxs].obs[annotation_key].value_counts()[cl]  # find first-term neighbor cluster annotations
 58 |                 all_nhbor_indices = np.where(distance_mat[:, idx] != 0)[0]
 59 | 
 60 |                 if cl == cluster_of_interest:
 61 |                     if (n_cl_neighbors >= 0) & (sum(dat.obs[annotation_key][all_nhbor_indices] == interaction_cluster) >= 2):
 62 |                         dat.obs.at[obs_name, added_key] = cl
 63 | 
 64 |                 elif cl == interaction_cluster:
 65 |                     if (n_cl_neighbors >= 0) & (sum(dat.obs[annotation_key][all_nhbor_indices] == cluster_of_interest) >= 2):
 66 |                         dat.obs.at[obs_name, added_key] = cl
 67 | 
 68 |             except:
 69 |                 continue
 70 | 
 71 |     # Modify the colors to maintain the original cluster color
 72 |     dat.obs[added_key] = dat.obs[added_key].astype('category')
 73 | 
 74 |     return(dat)
 75 | 
 76 | if __name__ == '__main__':
 77 | 
 78 | 
 79 |     # Download all samples into a dict structure
 80 |     adata_slides = {}
 81 |     for sample in tqdm(samples, unit='sample'):
 82 |         adata_slides[sample] = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')
 83 | 
 84 |     it=0
 85 |     valid_samples = []
 86 | 
 87 |     fig, axs = plt.subplots(8, 6, figsize=(18, 24),dpi=120)
 88 | 
 89 |     for i in range(8):
 90 |         for j in range(6):
 91 | 
 92 |             if it < len(samples) :
 93 |                 
 94 |                 sample_name = samples[it]
 95 | 
 96 |                 slide = adata_slides[sample_name].copy()
 97 |                 slide = get_spot_interfaces(slide, source, target)
 98 | 
 99 |                 # Qualify sample only if there are 10 or more of both source and target spots
100 |                 if not (slide.obs['proximity_analysis'].isna().all()):
101 |                     if (slide.obs['proximity_analysis'].str.contains(source).sum() >= 10) & (slide.obs['proximity_analysis'].str.contains(target).sum() >= 10):
102 |                     
103 |                         ## Plotting ##
104 |                         slide.uns['proximity_analysis_colors'] = [color_dict[cat] for cat in slide.obs['proximity_analysis'].cat.categories]
105 | 
106 |                         # create spatial plot
107 |                         if 'P320' not in sample_name:
108 |                             sc.pl.spatial(slide,color='proximity_analysis',title=sample_name,
109 |                                                     crop_coord=sample_crop_coord[sample_name],
110 |                                                     size=1.5, alpha_img=0, legend_loc=None,na_color='whitesmoke',
111 |                                                     ax=axs[i,j],show=False
112 |                                                     )
113 | 
114 |                         else:
115 |                             sc.pl.spatial(slide,color='proximity_analysis',title=sample_name,
116 |                                                     size=1.5, alpha_img=0, legend_loc=None,na_color='whitesmoke',
117 |                                                     ax=axs[i,j],show=False
118 |                                                     )
119 | 
120 |                         # Remove labels
121 |                         axs[i,j].set_xlabel(None)
122 |                         axs[i,j].set_ylabel(None)
123 | 
124 |                         # Append this sample to the list
125 |                         valid_samples.append(sample_name)
126 |                     else:
127 |                         axs[i,j].set_visible(False)
128 |                 else:
129 |                     axs[i,j].set_visible(False)
130 |             else:
131 |                 axs[i,j].set_visible(False)
132 |             
133 |             it+=1
134 | 
135 | 
136 |     plt.tight_layout()
137 |     plt.savefig('./plots/receptor_ligand_interaction_analysis/'+source+'_'+target+'_proximity_regions.pdf')
138 |     plt.clf()
139 | 
140 |     #### Second part, using valid_samples to do ligrec #####
141 | 
142 |     ligrec_dict = {}
143 |     for sample_name in valid_samples:
144 | 
145 |         slide = adata_slides[sample_name].copy()
146 |         
147 |         # Fill NaN's in as string as ligrec won't run otherwise
148 |         slide.obs['proximity_analysis'] = slide.obs['proximity_analysis'].cat.add_categories(['NA'])
149 |         slide.obs['proximity_analysis'] = slide.obs['proximity_analysis'].fillna('NA')
150 | 
151 |         if (len(slide.obs['proximity_analysis'].cat.categories.tolist()) == 3):
152 |         
153 |             ligrec_res = sq.gr.ligrec(
154 |                 slide,
155 |                 cluster_key='proximity_analysis',
156 |                 clusters = [source,target],
157 |                 complex_policy='all',
158 |                 show_progress_bar = False,
159 |                 n_perms=1000,
160 |                 seed=4359345,
161 |                 copy=True,
162 |                 use_raw=False
163 |             )
164 | 
165 |             ligrec_dict[sample_name] = ligrec_res
166 | 
167 |     # Save the proximal spot annotations
168 |     proximity_spot_annots = pd.DataFrame()
169 |     for s in valid_samples:
170 |         proximity_spot_annots = pd.concat([
171 |             proximity_spot_annots,
172 |             adata_slides[s].obs.copy()],
173 |             axis=0)
174 | 
175 |     # Format the results
176 |     proximity_spot_annots = proximity_spot_annots[['sample_id','predicted_region','proximity_analysis']]
177 |     proximity_spot_annots = proximity_spot_annots.rename(columns={'proximity_analysis':'{}_{}_proximity'.format(source,target)})
178 | 
179 |     # Save the results
180 |     proximity_spot_annots.to_csv('./data/proximity_spot_ids/{}_to_{}_spot_annotation.csv'.format(source,target))
181 |     save_to_pickle(ligrec_dict,'./data/region_ligrec_analysis/'+source+'_'+target+'_slides_with_ligrec.pkl')
182 |     print('{} to {} ligand-receptor interaction analysis has been saved!'.format(source,target))


--------------------------------------------------------------------------------
/spatial-dotplot-ar-basal-club-markers.py:
--------------------------------------------------------------------------------
  1 | import os 
  2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')
  3 | 
  4 | import scanpy as sc
  5 | import numpy as np
  6 | import pandas as pd
  7 | 
  8 | import matplotlib.pyplot as plt
  9 | import matplotlib.colors as colors
 10 | from scripts.utils import get_sample_ids_reorder, get_sample_crop_coords, get_sample_id_mask
 11 | from datetime import datetime   
 12 | 
 13 | import seaborn as sns
 14 | sns.set_theme(style='white')
 15 | 
 16 | #from statsmodels.stats.multitest import fdrcorrection
 17 | from tqdm import tqdm
 18 | 
 19 | from scipy.stats import zscore
 20 | 
 21 | import warnings
 22 | warnings.filterwarnings("ignore")
 23 | 
 24 | if __name__ == '__main__':
 25 | 
 26 |     samples = get_sample_ids_reorder()
 27 |     sample_crop_coord = get_sample_crop_coords()
 28 |     sample_id_masks = get_sample_id_mask()
 29 | 
 30 | 
 31 |     color_dict = {
 32 |     'Tumor': '#fc8d62',
 33 |     'Luminal epithelium': '#8da0cb',
 34 |     'Basal epithelium': '#66c2a5',
 35 |     'Club epithelium': '#ffd92f',
 36 |     'Immune': '#a6d854',
 37 |     'Endothelium': '#e78ac3',
 38 |     'Fibroblast': '#e5c494',
 39 |     'Muscle': '#b3b3b3'
 40 |     }
 41 | 
 42 |     regions = list(color_dict.keys())
 43 |     region_colors = list(color_dict.values())
 44 | 
 45 | 
 46 |     # Fetch normalized expression and percentage of spots that express any given gene
 47 | 
 48 |     def fetch_region_normalized_expression_and_percentage(gene_markers, sample_list, group_id, regions_list = regions, use_unnormalized = True):
 49 |         '''
 50 |         Returns the per-sample mean expression of genes of interest across all samples that are defined in sample_list
 51 |         inside a region defined by region parameter.
 52 |         '''
 53 | 
 54 |         final_expression_df = pd.DataFrame()
 55 |         final_percentage_df = pd.DataFrame()
 56 |         for region in regions_list:
 57 | 
 58 |             print('Region: ' + region)
 59 |             region_expression_df = pd.DataFrame()
 60 |             #region_pct_df = pd.DataFrame()
 61 | 
 62 |             for sample in tqdm(sample_list, desc="Processing samples", unit="sample"):
 63 |                 
 64 |                 slide = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')
 65 |                 slide_subs = slide[slide.obs['predicted_region']==region].copy()
 66 | 
 67 |                 # Control for the number of data points belonging to a class
 68 |                 if slide_subs.shape[0] >= 10:
 69 |                     
 70 |                     present_genes = [g for g in gene_markers if g in slide_subs.var_names]
 71 |                     missing_genes = [g for g in gene_markers if g not in slide_subs.var_names]
 72 | 
 73 |                     genes_all_arr_order_match = present_genes + missing_genes
 74 | 
 75 |                     #if use_unnormalized:
 76 |                     expr_without_missing_genes = slide_subs[:,present_genes].layers['counts'].copy().todense()
 77 |                     #else:
 78 |                         #expr_without_missing_genes = slide_subs[:,present_genes].X.copy()
 79 | 
 80 | 
 81 |                     expr_all_spots = np.concatenate((expr_without_missing_genes,np.full((expr_without_missing_genes.shape[0],len(missing_genes)), np.nan)),axis=1)
 82 | 
 83 |                     expr_as_df = pd.DataFrame(data=expr_all_spots.T,index=genes_all_arr_order_match,columns=slide_subs.obs_names)
 84 |                     expr_as_df = expr_as_df.loc[gene_markers]
 85 |                     
 86 |                     # Concatenate the counts from a single sample into a dataframe with all the spots
 87 |                     region_expression_df = pd.concat([region_expression_df,expr_as_df],axis=1)
 88 | 
 89 |                 del slide, slide_subs
 90 | 
 91 |             # Added on 4.3.2024 prior to plotting the whole thing
 92 |             # Previously NaN's inflated the percentage, as they were non-zero
 93 |             region_expression_df = region_expression_df.fillna(0)
 94 |             # Put the "percentage of spots expressed in" information into a dataframe
 95 |             region_pct_df = pd.DataFrame((region_expression_df != 0).sum(axis=1)/region_expression_df.shape[1],columns=[region])
 96 | 
 97 |             
 98 |             # Here you concatenate the mean of all valid spots into a dataframe
 99 |             final_expression_df = pd.concat([final_expression_df,region_expression_df.mean(axis=1)],axis=1)
100 |             final_percentage_df = pd.concat([final_percentage_df,region_pct_df],axis=1)
101 |         
102 | 
103 |         final_expression_df.columns = [r + ' ' + group_id for r in regions_list]
104 |         final_percentage_df.columns = [r + ' ' + group_id for r in regions_list]
105 |         return(final_expression_df, final_percentage_df)
106 | 
107 | 
108 | 
109 | 
110 |     # Get lists of samples in corresponding groupings
111 |     #normal_samples = get_sample_ids_reorder(['BPH'])
112 |     unt_samples = get_sample_ids_reorder(['untreated'])
113 |     trt_samples = get_sample_ids_reorder(['bicalutamide','goserelin','degarelix','degarelix_apalutamide'])
114 |     #crpc_samples = get_sample_ids_reorder(['CRPC'])
115 | 
116 |     ar_signaling_genes = ['AR','ABCC4','FKBP5','KLK3','MAF','NKX3-1','PMEPA1','div1', # AR regulated genes
117 |     'KRT5','KRT15','TP63','div2', # Canonical basal markers
118 |     'MMP7','PIGR','LTF','SCGB1A1','SCGB3A1','div3', # Club-like markers
119 |     #'KRT4','PSCA','WFDC2','CYP2F1','TSPAN8','CLU','KRT19','KLF5','ANXA3','PPP1R1B','S100A11','KRT8','ATP1B1', # All progenitor markers (Baures et al. Cancers 2022)
120 |     'S100A11','WFDC2','CLU','KRT19','KLF5','ATP1B1','KRT4', # Select representative progenitor markers for main plot
121 |     'EGFR','MET' # Putative stemness-type receptors #,'IGF1R','ERBB4','ERBB2'
122 |     ] 
123 | 
124 |     expr_unt, pct_unt = fetch_region_normalized_expression_and_percentage(ar_signaling_genes, unt_samples,group_id='(TRNA)',regions_list=regions[:4])
125 |     expr_trt, pct_trt = fetch_region_normalized_expression_and_percentage(ar_signaling_genes, trt_samples,group_id='(NEADT)',regions_list=regions[:4])
126 | 
127 |     
128 | 
129 |     #plot_df = pd.concat([normal_ar,untreated_ar,treated_ar,crpc_ar],axis=1).T
130 |     expr_df = pd.concat([expr_unt,expr_trt],axis=1).T
131 |     pct_df = pd.concat([pct_unt,pct_trt],axis=1).T
132 |     expr_df = expr_df.apply(lambda x: zscore(x, nan_policy='omit'))
133 |     regions_mod = expr_df.index.tolist()
134 | 
135 |     # Format to long
136 |     plot_df = expr_df.melt(ignore_index=False)
137 |     plot_df.columns = ['gene','expression']
138 | 
139 |     # Format to long
140 |     pct_df = pct_df.melt(ignore_index=False)
141 |     pct_df.columns = ['gene','percentage']
142 | 
143 |     plot_df['percentage'] = pct_df['percentage'].copy()
144 | 
145 |     plot_df = plot_df.reset_index(names='region')
146 | 
147 | 
148 |     # Create the dotplot
149 |     sns.set_theme(style='white')
150 | 
151 |     width = 12
152 |     height = 4
153 | 
154 |     fig, ax = plt.subplots(figsize=(width, height))
155 |     yticks_list = list(np.arange(2,(len(regions_mod)*2)+2,2)[::-1])
156 | 
157 | 
158 |     # Get control over interactions order and gap
159 |     plot_df['region_y'] = plot_df['region'].map(dict(zip(regions_mod,yticks_list)))
160 |     sns.scatterplot(x='gene', y='region_y', size='percentage', hue='expression', 
161 |                     hue_norm=(-2,2),data=plot_df, sizes=(30, 300), palette='bwr', ax=ax,legend=True,
162 |                     )
163 | 
164 |     plt.ylim(0,yticks_list[0]+2)
165 |     plt.yticks(yticks_list,regions_mod)
166 |     plt.xticks(rotation=45)
167 |     plt.legend(loc='center left',handlelength=1.5, handleheight=1.5, bbox_to_anchor=(1.05, 0.5))
168 |     plt.tight_layout()
169 | 
170 |     #plt.savefig('plots/normalized_gene_expression_heatmaps/ar_club_markers_expression_nan_fixed.pdf')
171 | 
172 |     # Save the plot source data
173 |     plot_df.to_excel('./source_data/figure_2c.xlsx',index=False)
174 | 


--------------------------------------------------------------------------------
/spatial-dotplot-chemokine-expression.py:
--------------------------------------------------------------------------------
  1 | import os 
  2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')
  3 | 
  4 | import scanpy as sc
  5 | import numpy as np
  6 | import pandas as pd
  7 | 
  8 | import matplotlib.pyplot as plt
  9 | import matplotlib.colors as colors
 10 | from utils import get_sample_ids_reorder, get_sample_crop_coords, get_sample_id_mask
 11 | from datetime import datetime   
 12 | 
 13 | import seaborn as sns
 14 | sns.set_theme(style='white')
 15 | 
 16 | #from statsmodels.stats.multitest import fdrcorrection
 17 | from tqdm import tqdm
 18 | 
 19 | from scipy.stats import zscore
 20 | 
 21 | import warnings
 22 | warnings.filterwarnings("ignore")
 23 | 
 24 | if __name__ == '__main__':
 25 | 
 26 |     samples = get_sample_ids_reorder()
 27 |     sample_crop_coord = get_sample_crop_coords()
 28 |     sample_id_masks = get_sample_id_mask()
 29 | 
 30 | 
 31 |     color_dict = {
 32 |     'Tumor': '#fc8d62',
 33 |     'Luminal epithelium': '#8da0cb',
 34 |     'Basal epithelium': '#66c2a5',
 35 |     'Club epithelium': '#ffd92f',
 36 |     'Immune': '#a6d854',
 37 |     'Endothelium': '#e78ac3',
 38 |     'Fibroblast': '#e5c494',
 39 |     'Muscle': '#b3b3b3'
 40 |     }
 41 | 
 42 |     regions = list(color_dict.keys())
 43 |     region_colors = list(color_dict.values())
 44 | 
 45 | 
 46 |     # Fetch normalized expression and percentage of spots that express any given gene
 47 | 
 48 |     def fetch_region_normalized_expression_and_percentage(gene_markers, sample_list, group_id, regions_list = regions, use_unnormalized = True):
 49 |         '''
 50 |         Returns the per-sample mean expression of genes of interest across all samples that are defined in sample_list
 51 |         inside a region defined by region parameter.
 52 |         '''
 53 | 
 54 |         final_expression_df = pd.DataFrame()
 55 |         final_percentage_df = pd.DataFrame()
 56 |         for region in regions_list:
 57 | 
 58 |             print('Region: ' + region)
 59 |             region_expression_df = pd.DataFrame()
 60 |             #region_pct_df = pd.DataFrame()
 61 | 
 62 |             for sample in tqdm(sample_list, desc="Processing samples", unit="sample"):
 63 |                 
 64 |                 slide = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')
 65 |                 slide_subs = slide[slide.obs['predicted_region']==region].copy()
 66 | 
 67 |                 # Control for the number of data points belonging to a class
 68 |                 if slide_subs.shape[0] >= 10:
 69 |                     
 70 |                     present_genes = [g for g in gene_markers if g in slide_subs.var_names]
 71 |                     missing_genes = [g for g in gene_markers if g not in slide_subs.var_names]
 72 | 
 73 |                     genes_all_arr_order_match = present_genes + missing_genes
 74 | 
 75 |                     #if use_unnormalized:
 76 |                     expr_without_missing_genes = slide_subs[:,present_genes].layers['counts'].copy().todense()
 77 |                     #else:
 78 |                         #expr_without_missing_genes = slide_subs[:,present_genes].X.copy()
 79 | 
 80 | 
 81 |                     expr_all_spots = np.concatenate((expr_without_missing_genes,np.full((expr_without_missing_genes.shape[0],len(missing_genes)), np.nan)),axis=1)
 82 | 
 83 |                     expr_as_df = pd.DataFrame(data=expr_all_spots.T,index=genes_all_arr_order_match,columns=slide_subs.obs_names)
 84 |                     expr_as_df = expr_as_df.loc[gene_markers]
 85 |                     
 86 |                     # Concatenate the counts from a single sample into a dataframe with all the spots
 87 |                     region_expression_df = pd.concat([region_expression_df,expr_as_df],axis=1)
 88 | 
 89 |                 del slide, slide_subs
 90 | 
 91 |             # Added on 4.3.2024 prior to plotting the whole thing
 92 |             # Previously NaN's inflated the percentage, as they were non-zero
 93 |             region_expression_df = region_expression_df.fillna(0)
 94 |             # Put the "percentage of spots expressed in" information into a dataframe
 95 |             region_pct_df = pd.DataFrame((region_expression_df != 0).sum(axis=1)/region_expression_df.shape[1],columns=[region])
 96 | 
 97 |             
 98 |             # Here you concatenate the mean of all valid spots into a dataframe
 99 |             final_expression_df = pd.concat([final_expression_df,region_expression_df.mean(axis=1)],axis=1)
100 |             final_percentage_df = pd.concat([final_percentage_df,region_pct_df],axis=1)
101 |         
102 | 
103 |         final_expression_df.columns = [r + ' ' + group_id for r in regions_list]
104 |         final_percentage_df.columns = [r + ' ' + group_id for r in regions_list]
105 |         return(final_expression_df, final_percentage_df)
106 | 
107 | 
108 | 
109 | 
110 |     # Get lists of samples in corresponding groupings
111 |     unt_samples = get_sample_ids_reorder(['untreated'])
112 |     trt_samples = get_sample_ids_reorder(['bicalutamide','goserelin','degarelix','degarelix_apalutamide'])
113 |  
114 | 
115 |     chemokine_marker_genes = [
116 |                         'CEBPB','NFKB1','IL1RN','CD68','PLAUR','div0',
117 |                         'CXCL1','CXCL2','CXCL3','CXCL5','CXCL6','CXCL8','CXCR2','div1', # Left out 'CXCR1',
118 |                         'CXCL16','CXCR6','div2',
119 |                         'CCL20','CCR6','div3',
120 |                         'CCL2','CCL3','CCL4','CCL5','CCR2','CCR5','div4',
121 |                         'CXCL9','CXCL10','CXCL11','CXCR3','div5',
122 |                         'CCL17','CCL22','CCR4','div6',
123 |                         'CCL19','CCL21','CCR7','div7',
124 |                         'CXCL12','CXCR4'
125 |                         ]
126 | 
127 | 
128 |     # Fetch the relevant expression
129 |     expr_unt, pct_unt = fetch_region_normalized_expression_and_percentage(chemokine_marker_genes, unt_samples,group_id='(TRNA)',regions_list=regions)
130 |     expr_trt, pct_trt = fetch_region_normalized_expression_and_percentage(chemokine_marker_genes, trt_samples,group_id='(NEADT)',regions_list=regions)
131 | 
132 | 
133 |     #plot_df = pd.concat([normal_ar,untreated_ar,treated_ar,crpc_ar],axis=1).T
134 |     expr_df = pd.concat([expr_unt,expr_trt],axis=1).T#.fillna(0) ## Added on 4.3.2024 as CXCR1 expression in treated was invisible
135 |     pct_df = pd.concat([pct_unt,pct_trt],axis=1).T#.fillna(0) ## Added on 4.3.2024 as CXCR1 expression in treated was invisible
136 |     
137 |     expr_df.to_csv('expr_df.csv')
138 |     pct_df.to_csv('pct_df.csv')
139 | 
140 |     expr_df = expr_df.apply(lambda x: zscore(x, nan_policy='omit'))
141 |     regions_mod = expr_df.index.tolist()
142 | 
143 |     # Format to long
144 |     plot_df = expr_df.melt(ignore_index=False)
145 |     plot_df.columns = ['gene','expression']
146 | 
147 |     # Format to long
148 |     pct_df = pct_df.melt(ignore_index=False)
149 |     pct_df.columns = ['gene','percentage']
150 | 
151 |     plot_df['percentage'] = pct_df['percentage'].copy()
152 | 
153 |     plot_df = plot_df.reset_index(names='region')
154 | 
155 | 
156 |     # Create the dotplot
157 |     sns.set_theme(style='white')
158 | 
159 |     width = 15
160 |     height = 5.5
161 | 
162 |     fig, ax = plt.subplots(figsize=(width, height))
163 |     yticks_list = list(np.arange(2,(len(regions_mod)*2)+2,2)[::-1])
164 | 
165 | 
166 |     # Get control over interactions order and gap
167 |     plot_df['region_y'] = plot_df['region'].map(dict(zip(regions_mod,yticks_list)))
168 |     sns.scatterplot(x='gene', y='region_y', size='percentage', hue='expression', 
169 |                     hue_norm=(-2,2),data=plot_df, sizes=(30, 300), palette='bwr', ax=ax,legend=True,
170 |                     )
171 | 
172 |     plt.ylim(0,yticks_list[0]+2)
173 |     plt.yticks(yticks_list,regions_mod)
174 |     plt.xticks(rotation=45)
175 |     plt.legend(loc='center left',handlelength=1.5, handleheight=1.5, bbox_to_anchor=(1.05, 0.5))
176 |     plt.tight_layout()
177 | 
178 |     plot_df.to_excel('./supplementary_tables/source_data_chemokine_dotplot.xlsx')
179 | 
180 |     plt.savefig('plots/normalized_gene_expression_heatmaps/chemokine_markers_expression_dotplot.pdf',transparent=True)
181 | 
182 |     # Save the plot source data
183 |     plot_df.to_excel('./source_data/figure_3d.xlsx',index=False)
184 | 


--------------------------------------------------------------------------------
/single-cell/single-cell-nmf-analysis.py:
--------------------------------------------------------------------------------
  1 | import os 
  2 | os.chdir('/lustre/scratch/kiviaho/prostate_spatial')
  3 | import numpy as np
  4 | import pandas as pd
  5 | import scanpy as sc
  6 | from itertools import product
  7 | import nimfa
  8 | import argparse
  9 | 
 10 | 
 11 | def save_to_pickle(obj,filename):
 12 |     import pickle
 13 |     with open(filename, 'wb') as handle:
 14 |         pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL)
 15 | 
 16 | def nmf_preprocessing(dat,n_var_genes=2000):
 17 | 
 18 |     dat.X = dat.layers['counts'].copy()
 19 |     sc.pp.filter_genes(dat,min_counts=10)
 20 |     sc.pp.normalize_total(dat)
 21 |     sc.pp.scale(dat)
 22 |     sc.pp.highly_variable_genes(dat,n_top_genes=n_var_genes,flavor='seurat_v3',
 23 |                                 subset=True, layer='counts')
 24 | 
 25 |     # Replace negative entries with zero
 26 |     dat.X[dat.X < 0] = 0
 27 | 
 28 |     return(dat)
 29 | 
 30 | def calculate_matrix_orders(arr):
 31 |     # get the indices that would sort each row of the array
 32 |     sort_indices = np.argsort(-arr, axis=1)
 33 | 
 34 |     # create an array to mark the sorted order
 35 |     sorted_order = np.empty_like(sort_indices)
 36 |     rows, cols = np.indices(arr.shape)
 37 |     sorted_order[rows, sort_indices] = cols
 38 | 
 39 |     # replace each entry in the original array with its index in the sorted order
 40 |     result = sorted_order.astype(int)
 41 | 
 42 |     return result
 43 | 
 44 | 
 45 | # Helper function to calculate the Jaccard index of two lists
 46 | def jaccard(list1, list2):
 47 |     set1 = set(list1)
 48 |     set2 = set(list2)
 49 |     intersection = set1.intersection(set2)
 50 |     union = set1.union(set2)
 51 |     return len(intersection)/len(union)
 52 | 
 53 | 
 54 | ################# Params ########################
 55 | 
 56 | parser = argparse.ArgumentParser()
 57 | parser.add_argument('--filename', type=str, help='Name of the file to be used')
 58 | parser.add_argument('--n_var_genes', type=int,default=2000, help='The target number of HVGs')
 59 | parser.add_argument('--min_genes_in_initial_module', type=int,default=5, help='The number of genes required in the initial NMF module')
 60 | parser.add_argument('--required_jaccard_index', type=float,default=0.05, help='The required Jaccard overlap for two gene modules')
 61 | args = parser.parse_args()
 62 | 
 63 | filename = args.filename
 64 | n_hvgs = args.n_var_genes
 65 | min_genes_in_initial_module = args.min_genes_in_initial_module
 66 | required_jaccard_index = args.required_jaccard_index
 67 | min_cells = 100
 68 | nmf_comp_range = np.arange(5,11)[::-1] # Changed in the latest run
 69 | 
 70 | if __name__ == '__main__':
 71 | 
 72 |     # Download data
 73 |     ctype_dat = sc.read_h5ad(filename)
 74 | 
 75 |     # Initialize dict objects
 76 |     nmf_sample_dict = {}
 77 |     dq_samples = {}
 78 | 
 79 |     ################# First part: calculating NMFs ########################
 80 | 
 81 |     for s in np.unique(ctype_dat.obs['sample']):
 82 | 
 83 |         if len(ctype_dat.obs[ctype_dat.obs['sample']==s]) >= min_cells:
 84 | 
 85 |             # Subset the data to only include a single sample
 86 |             dat = ctype_dat[ctype_dat.obs['sample'] == s]
 87 |             
 88 |             # Do a sample-specific preprocessing
 89 |             dat = nmf_preprocessing(dat,n_hvgs)
 90 | 
 91 |             for n_comps in nmf_comp_range:
 92 | 
 93 |                 # Perform non-negative matrix factorization using nsNMF
 94 |                 f = nimfa.Nsnmf(dat.X, rank=n_comps)
 95 |                 f_fit = f()
 96 |                     
 97 |                 # Extract the resulting matrices into variables W and H
 98 |                 W = np.array(f_fit.basis())
 99 |                 H = np.array(f_fit.coef())
100 | 
101 |                 res = {'samples':dat.obs,'genes':dat.var,'sample_weights':W,'gene_weights':H}
102 | 
103 |                 gene_w = res['gene_weights'].T
104 |                 genes = list(res['genes'].index)
105 | 
106 |                 mat_1 = calculate_matrix_orders(gene_w.T).T
107 |                 mat_2 = calculate_matrix_orders(gene_w)
108 | 
109 |                 # Iterate through the factors (columns)
110 |                 genes_by_factors = {}
111 |                 for factor in range(gene_w.shape[1]):
112 |                     valid_genes = list()
113 |                     # Iterate through the genes, starting from the highest weighted gene of this factor
114 |                     for i in np.argsort(mat_1[:,factor]): 
115 |                         if mat_2[i,factor] ==0: # Is this the factor the gene effects the most?
116 |                             
117 |                             # If yes, add it to the list of genes
118 |                             valid_genes.append(genes[i])
119 |                         else:
120 |                             # if not, stop adding genes into the list, move on to the next factor
121 |                             break 
122 |                     # Append the list of valid genes into a dictionary under the appropriate key
123 |                     genes_by_factors['factor'+str(factor)] = valid_genes
124 | 
125 |                 
126 |                 all_lists_have_at_least = True
127 | 
128 |                 for lst in genes_by_factors.values():
129 |                     if len(lst) < min_genes_in_initial_module:
130 |                         all_lists_have_at_least = False
131 |                         break
132 | 
133 |                 if all_lists_have_at_least:
134 |                     print(s+": valid factors (min. "+str(min_genes_in_initial_module)+" genes) when using n_comps="+str(n_comps))
135 |                     nmf_sample_dict[s] = genes_by_factors
136 |                     break
137 |         else:
138 |             dq_samples[s] = len(ctype_dat.obs[ctype_dat.obs['sample']==s])
139 |         
140 |     for k in list(dq_samples.keys()):
141 |         print(k + ' not processed for too few cells (' + str(dq_samples[k])+')')
142 | 
143 |     ################# Second part: Observing module overlaps ########################
144 | 
145 |     # A dictionary to keep track of overlapping factors
146 |     overlapping_factors = {}
147 | 
148 |     # Go through each sample factor combination
149 |     for (sample1, factor1), (sample2, factor2) in product([(sample, factor) for sample in nmf_sample_dict for factor in nmf_sample_dict[sample]], repeat=2):
150 |         # Ignore if it's the same sample and factor combination
151 |         if sample1 == sample2 and factor1 == factor2:
152 |             continue
153 |         # Calculate the Jaccard index of the two lists
154 |         jaccard_index = jaccard(nmf_sample_dict[sample1][factor1], nmf_sample_dict[sample2][factor2])
155 |         # If the overlap is more than 5%, add it to the overlapping_factors dictionary
156 |         if jaccard_index > required_jaccard_index:
157 |             overlapping_factors.setdefault((sample1, factor1), set()).add((sample2, factor2))
158 |             
159 |     # A set to keep track of factors with more than two overlaps
160 |     valid_factors = set()
161 | 
162 |     # Go through each factor
163 |     for key, value in overlapping_factors.items():
164 |         # If the factor overlaps with at least one other factors, add it to the valid_factors set
165 |         if len(value) >= 2:
166 |             valid_factors.add(key)
167 | 
168 |     # A dictionary to keep track of how many overlapping factors each gene has
169 |     gene_overlaps = {}
170 | 
171 |     # Go through each valid factor
172 |     for factor in valid_factors:
173 |         # Get the sample and factor
174 |         sample, factor_name = factor
175 |         # Go through each gene in the factor
176 |         for gene in nmf_sample_dict[sample][factor_name]:
177 |             # Add one to the gene's overlaps count
178 |             gene_overlaps[gene] = gene_overlaps.get(gene, 0) + 1
179 |             
180 |     # Create an empty 2D array to serve as the adjacency matrix
181 |     adj_matrix = [[0 for gene2 in gene_overlaps.keys()] for gene1 in gene_overlaps.keys()]
182 | 
183 |     # Loop through each valid factor
184 |     for factor in valid_factors:
185 |         # Get the sample and factor
186 |         sample, factor_name = factor
187 |         
188 |         # Loop through each gene in the factor
189 |         for gene1 in nmf_sample_dict[sample][factor_name]:
190 |             # Loop through each other gene in the same factor
191 |             for gene2 in nmf_sample_dict[sample][factor_name]:
192 |                 # Ignore if it's the same gene
193 |                 if gene1 == gene2:
194 |                     continue
195 |                 # Increment the value for this pair of genes in the adjacency matrix
196 |                 adj_matrix[list(gene_overlaps.keys()).index(gene1)][list(gene_overlaps.keys()).index(gene2)] += 1
197 | 
198 |     adj_df = pd.DataFrame(adj_matrix,columns=list(gene_overlaps.keys()),index=list(gene_overlaps.keys()))
199 | 
200 |     # Save the adjacency matrix to a csv
201 |     save_file = filename.replace('.h5ad','_nmf_derived_gene_adjacencies_mod_'+
202 |                                  str(min_genes_in_initial_module)+'_jac'
203 |                                  +str(required_jaccard_index)+'.csv')
204 |     adj_df.to_csv(save_file)
205 | 
206 | 
207 | 


--------------------------------------------------------------------------------
/single-cell/single-cell-preprocessing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import pandas as pd\n",
 11 |     "import scanpy as sc\n",
 12 |     "import anndata as ad\n",
 13 |     "from pathlib import Path\n",
 14 |     "import glob\n",
 15 |     "import warnings\n",
 16 |     "warnings.filterwarnings('ignore')\n",
 17 |     "\n",
 18 |     "import os\n",
 19 |     "os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "markdown",
 24 |    "metadata": {},
 25 |    "source": [
 26 |     "### Formatting Dong et al. 2020 data"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": null,
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "sc_files = glob.glob('sc-reference/dong_2020/*txt') \n",
 36 |     "dong_annot = pd.read_csv('./sc-reference/dong_2020/dong_2020_annot.csv',sep=';',index_col=0)\n",
 37 |     "dong_annot = dong_annot.rename(columns={'cells':'celltype_orig'})\n",
 38 |     "\n",
 39 |     "# Download the files into a list and concatenate together\n",
 40 |     "adata_list = []\n",
 41 |     "for file in sc_files:\n",
 42 |     "    s_abbr = '_'.join(file.split('/')[2].split('_')[0:2])\n",
 43 |     "    \n",
 44 |     "    with open(file) as x:\n",
 45 |     "        ncols = len(x.readline().split('\\t'))\n",
 46 |     "\n",
 47 |     "    df = pd.read_csv(file, usecols=range(1,ncols),delimiter='\\t',index_col=0)\n",
 48 |     "    adata = ad.AnnData(df).T\n",
 49 |     "\n",
 50 |     "    #### ADDING METADATA ####\n",
 51 |     "    adata.obs_names = s_abbr + '_' + adata.obs_names\n",
 52 |     "    meta = adata.obs.copy()\n",
 53 |     "    meta['sample'] = s_abbr\n",
 54 |     "    meta['patient'] = s_abbr\n",
 55 |     "    meta = meta.merge(dong_annot,how='left',left_index=True,right_index=True)\n",
 56 |     "    meta['phenotype'] = 'CRPC'\n",
 57 |     "    meta['dataset'] = 'dong_2020'\n",
 58 |     "\n",
 59 |     "    adata.obs = meta.copy()\n",
 60 |     "    ##########\n",
 61 |     "    adata.obs_names_make_unique()\n",
 62 |     "\n",
 63 |     "    # Since the genes were originally named with ENSEMBL ID, we have to make them unique.\n",
 64 |     "    adata.var_names_make_unique()\n",
 65 |     "    adata_list.append(adata)\n",
 66 |     "    \n",
 67 |     "adata_concat = ad.concat(adata_list, join='outer', fill_value=0)\n",
 68 |     "\n",
 69 |     "adata_concat.obs\n",
 70 |     "adata_concat.write('sc-reference/dong_2020/adata_obj.h5ad')"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "markdown",
 75 |    "metadata": {},
 76 |    "source": [
 77 |     "### Formatting Chen et al. 2021 data"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": null,
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "\n",
 87 |     "adata = sc.read_csv('sc-reference/chen_2021/GSM4203181_data.raw.matrix.txt',delimiter='\\t')\n",
 88 |     "adata\n",
 89 |     "adata = adata.T\n",
 90 |     "\n",
 91 |     "chen_obs = adata.obs\n",
 92 |     "#### ADDING METADATA ####\n",
 93 |     "\n",
 94 |     "chen_obs['sample'] = ['chen_'+s.split('-')[1] for s in chen_obs.index]\n",
 95 |     "chen_obs['patient'] = chen_obs['sample']\n",
 96 |     "chen_obs['celltype_orig'] = 'unknown'\n",
 97 |     "chen_obs['phenotype'] = 'PCa'\n",
 98 |     "chen_obs['dataset'] = 'chen_2021'\n",
 99 |     "\n",
100 |     "##########\n",
101 |     "\n",
102 |     "\n",
103 |     "if (chen_obs.index == adata.obs_names).all():\n",
104 |     "    adata.obs = chen_obs\n",
105 |     "adata.obs_names = adata.obs['sample'] + '_' + [s.split('-')[0] for s in adata.obs_names] + '.1'\n",
106 |     "adata.obs.index = adata.obs.index.set_names(['cell'])\n",
107 |     "adata.obs\n",
108 |     "\n",
109 |     "adata.write('sc-reference/chen_2021/adata_obj.h5ad')"
110 |    ]
111 |   },
112 |   {
113 |    "attachments": {},
114 |    "cell_type": "markdown",
115 |    "metadata": {},
116 |    "source": [
117 |     "### Formatting Song et al. 2022 data"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": null,
123 |    "metadata": {},
124 |    "outputs": [],
125 |    "source": [
126 |     "# This information is from supplementary file 1 of the article Song et al. 2022 Nature Comms\n",
127 |     "# The cell type annotations are available without cell IDs, so merging isn't possible.\n",
128 |     "# Number of analysed cells is 21743\n",
129 |     "song_samples = ['AUG_PB1A', 'AUG_PB1B','MAY_PB1A','MAY_PB1B', 'MAY_PB2A','MAY_PB2B', # BIOPSIES\n",
130 |     "                'PR5186','PR5196','PR5199','PR5269', # UNPAIRED RPs\n",
131 |     "                'PR5249_N','PR5249_T', # NORMAL PAIRED RPs\n",
132 |     "                'PR5251_N','PR5251_T', # NORMAL PAIRED RPs\n",
133 |     "                'PR5254_N','PR5254_T', # NORMAL PAIRED RPs\n",
134 |     "                'PR5261_N','PR5261_T'] # NORMAL PAIRED RPs\n",
135 |     "\n",
136 |     "song_patients = ['P1','P1','P2','P2','P3','P3',\n",
137 |     "                 'P4','P5','P6','P7',\n",
138 |     "                 'P8','P8',\n",
139 |     "                 'P9','P9',\n",
140 |     "                 'P10','P10',\n",
141 |     "                 'P11','P11']\n",
142 |     "song_phenotype = list(np.repeat('PCa',10)) + ['normal','PCa','normal','PCa','normal','PCa','normal','PCa']\n",
143 |     "\n",
144 |     "\n",
145 |     "# Replace some of the idents with those matching file names\n",
146 |     "song_file_names = song_samples.copy()\n",
147 |     "song_file_names[:] = ['AUG_PB_1A' if x=='AUG_PB1A' else x for x in song_file_names]\n",
148 |     "song_file_names[:] = ['AUG_PB_1B' if x=='AUG_PB1B' else x for x in song_file_names]\n",
149 |     "\n",
150 |     "song_file_names[:] = ['PB1A' if x=='MAY_PB1A' else x for x in song_file_names]\n",
151 |     "song_file_names[:] = ['PB1B' if x=='MAY_PB1B' else x for x in song_file_names]\n",
152 |     "\n",
153 |     "song_file_names[:] = ['PB2A' if x=='MAY_PB2A' else x for x in song_file_names]\n",
154 |     "song_file_names[:] = ['PB2B' if x=='MAY_PB2B' else x for x in song_file_names]\n",
155 |     "\n",
156 |     "\n",
157 |     "adata_samples_list = []\n",
158 |     "for idx,file_abbr in enumerate(song_file_names):\n",
159 |     "\n",
160 |     "    # Find all files generated from one sample\n",
161 |     "    file_name_list = glob.glob('sc-reference/song_2022/*'+file_abbr+'*')\n",
162 |     "    sample_abbr = song_samples[idx]\n",
163 |     "    patient_abbr = song_patients[idx]\n",
164 |     "    phenot_abbr = song_phenotype[idx]\n",
165 |     "\n",
166 |     "    sample_adata = []\n",
167 |     "\n",
168 |     "    # Read in each file and append them to a sample-specific list\n",
169 |     "    for f in file_name_list:\n",
170 |     "        # exp_abbr = f.split('/')[2].split('_')[0]\n",
171 |     "        adata = sc.read_csv(f,dtype=np.int16,delimiter='\\t').T\n",
172 |     "        adata.obs_names = sample_abbr +'_'+ adata.obs_names + '-1'\n",
173 |     "        sample_adata.append(adata)\n",
174 |     "    adata_concat_one_sample = ad.concat(sample_adata, join='outer', fill_value=0)\n",
175 |     "\n",
176 |     "    # Concatenate together data from the same sample but from different sequencing runs.\n",
177 |     "    adata_concat_one_sample.obs['sample'] = sample_abbr\n",
178 |     "    adata_concat_one_sample.obs['patient'] = 'song_'+patient_abbr\n",
179 |     "    adata_concat_one_sample.obs['celltype_orig'] = 'unknown'\n",
180 |     "    adata_concat_one_sample.obs['phenotype'] = phenot_abbr\n",
181 |     "    adata_concat_one_sample.obs['dataset'] = 'song_2022'\n",
182 |     "    adata_samples_list.append(adata_concat_one_sample)\n",
183 |     "\n",
184 |     "adata_concat_all = ad.concat(adata_samples_list, join='outer', fill_value=0)\n",
185 |     "adata_concat_all.obs\n",
186 |     "\n",
187 |     "adata_concat_all.write('sc-reference/song_2022/adata_obj.h5ad')\n"
188 |    ]
189 |   },
190 |   {
191 |    "cell_type": "markdown",
192 |    "metadata": {},
193 |    "source": [
194 |     "### Formatting Cheng et al. 2022 data "
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": null,
200 |    "metadata": {},
201 |    "outputs": [],
202 |    "source": [
203 |     "data_dirs = glob.glob('sc-reference/cheng_2022/results/*')\n",
204 |     "new_names = pd.read_csv('sc-reference/cheng_2022/sample_shorthands.txt',sep='\\t')\n",
205 |     "\n",
206 |     "adata_list = []\n",
207 |     "for dir in data_dirs:\n",
208 |     "    # Get the shorhand\n",
209 |     "    sample = dir.split('/')[-1]\n",
210 |     "    shorthand = new_names[new_names['old']==sample]['new'].item()\n",
211 |     "    patient = shorthand.split('_')[0]\n",
212 |     "    if 'CRPC' in patient:\n",
213 |     "        phenot_abbr = 'CRPC'\n",
214 |     "    else:\n",
215 |     "        phenot_abbr = 'PCa'\n",
216 |     "\n",
217 |     "    adata = sc.read_10x_mtx(dir+'/outs/filtered_feature_bc_matrix')\n",
218 |     "\n",
219 |     "    adata.obs['sample'] = shorthand\n",
220 |     "    adata.obs['patient'] = 'cheng_'+patient\n",
221 |     "    adata.obs['celltype_orig'] = 'unknown'\n",
222 |     "    adata.obs['phenotype'] = phenot_abbr\n",
223 |     "    adata.obs['dataset'] = 'cheng_2022'\n",
224 |     "\n",
225 |     "    adata.obs_names = shorthand + '_' + adata.obs_names\n",
226 |     "    adata_list.append(adata)\n",
227 |     "\n",
228 |     "adata_concat = ad.concat(adata_list, join='outer', fill_value=0)\n",
229 |     "adata_concat.obs\n",
230 |     "adata_concat.write('sc-reference/cheng_2022/adata_obj.h5ad')"
231 |    ]
232 |   },
233 |   {
234 |    "attachments": {},
235 |    "cell_type": "markdown",
236 |    "metadata": {},
237 |    "source": [
238 |     "### Formatting Wong et al. 2022 data"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": null,
244 |    "metadata": {},
245 |    "outputs": [],
246 |    "source": [
247 |     "dat = sc.read_h5ad('sc-reference/wong_2022/wong_2022_data.h5ad')\n",
248 |     "annot = dat.obs.copy()\n",
249 |     "cell_annot = pd.read_csv('sc-reference/wong_2022/GSE185344_PH_scRNA.rename_cluster.csv')\n",
250 |     "cell_annot.index = cell_annot['Unnamed: 0']\n",
251 |     "merged_obs = pd.merge(annot,cell_annot,left_index=True,right_index=True,how='left')\n",
252 |     "\n",
253 |     "if (merged_obs.index == dat.obs.index).all():\n",
254 |     "        dat.obs = merged_obs\n",
255 |     "        del dat.raw\n",
256 |     "\n",
257 |     "# Format the phenotypes\n",
258 |     "phenot = [s.split('_')[2] for s in dat.obs['orig.ident']]\n",
259 |     "phenot = [w.replace('Benign', 'normal') for w in phenot]\n",
260 |     "phenot = [w.replace('Tumor', 'PCa') for w in phenot]\n",
261 |     "\n",
262 |     "# Format the patient \n",
263 |     "patient = ['_'.join(s.split('_')[:2]) for s in dat.obs['orig.ident']]\n",
264 |     "\n",
265 |     "new_obs = pd.DataFrame()\n",
266 |     "new_obs.index = dat.obs.index.copy()\n",
267 |     "new_obs['sample'] = 'wong2022_'+dat.obs['orig.ident'].copy()\n",
268 |     "new_obs['patient'] = ['wong2022_'+ p for p in patient]\n",
269 |     "new_obs['celltype_orig'] = dat.obs['cellactivity_clusters'].copy()\n",
270 |     "new_obs['phenotype'] = phenot\n",
271 |     "new_obs['dataset'] = 'wong_2022'\n",
272 |     "\n",
273 |     "dat.obs = new_obs\n",
274 |     "\n",
275 |     "# Lose the unnecessary column 'features'\n",
276 |     "dat.var = dat.var.drop(columns='features')\n",
277 |     "\n",
278 |     "dat.write('sc-reference/wong_2022/adata_obj.h5ad')\n"
279 |    ]
280 |   },
281 |   {
282 |    "attachments": {},
283 |    "cell_type": "markdown",
284 |    "metadata": {},
285 |    "source": [
286 |     "### Formatting Chen et al. 2022 data"
287 |    ]
288 |   },
289 |   {
290 |    "cell_type": "code",
291 |    "execution_count": null,
292 |    "metadata": {},
293 |    "outputs": [],
294 |    "source": [
295 |     "data_dirs = glob.glob('sc-reference/chen_2022/results/*')\n",
296 |     "\n",
297 |     "adata_list = []\n",
298 |     "for dir in data_dirs:\n",
299 |     "    # Get the shorhand\n",
300 |     "    print(dir)\n",
301 |     "    sample = dir.split('/')[-1]\n",
302 |     "    if 'PCa' in sample:\n",
303 |     "        phenot_abbr = 'PCa'\n",
304 |     "    else:\n",
305 |     "        phenot_abbr = 'normal'\n",
306 |     "\n",
307 |     "    adata = sc.read_10x_mtx(dir+'/outs/filtered_feature_bc_matrix')\n",
308 |     "\n",
309 |     "    adata.obs['sample'] = 'chen2022_'+sample\n",
310 |     "    adata.obs['patient'] = 'chen2022_'+sample\n",
311 |     "    adata.obs['celltype_orig'] = 'unknown'\n",
312 |     "    adata.obs['phenotype'] = phenot_abbr\n",
313 |     "    adata.obs['dataset'] = 'chen_2022'\n",
314 |     "\n",
315 |     "    adata.obs_names = sample + '_' + adata.obs_names\n",
316 |     "    adata_list.append(adata)\n",
317 |     "\n",
318 |     "adata_concat = ad.concat(adata_list, join='outer', fill_value=0)\n",
319 |     "adata_concat.obs\n",
320 |     "adata_concat.write('sc-reference/chen_2022/adata_obj.h5ad')\n"
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "markdown",
325 |    "metadata": {},
326 |    "source": [
327 |     "### Formatting Hirz et al. 2023 data "
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": null,
333 |    "metadata": {},
334 |    "outputs": [],
335 |    "source": [
336 |     "\n",
337 |     "# Download the files into a list and concatenate together\n",
338 |     "# There are no counts for GSM5494349_SCG-PCA2-T-LG.count.csv\n",
339 |     "# PCA24 samples have been prepared another way, excluded\n",
340 |     "\n",
341 |     "sc_files = sorted(glob.glob('sc-reference/hirz_2023/*SCG*')) # adjacent normal tissue\n",
342 |     "print(sc_files)\n",
343 |     "print()\n",
344 |     "hirz_annot = pd.read_csv('sc-reference/hirz_2023/GSE181294_scRNAseq.ano.csv',index_col=0)\n",
345 |     "hirz_annot = hirz_annot.drop(columns=['sample'])\n",
346 |     "hirz_annot = hirz_annot.rename(columns={'cells':'celltype_orig'})\n",
347 |     "\n",
348 |     "adata_list = []\n",
349 |     "for f in sc_files:\n",
350 |     "    name_split = f.split('/')[-1].split('_')[-1].split('-')\n",
351 |     "    sample = ('_').join(name_split[1:3])\n",
352 |     "    patient = name_split[1]\n",
353 |     "    if name_split[2] == 'N':\n",
354 |     "        phenot_abbr = 'normal'\n",
355 |     "    else:\n",
356 |     "        phenot_abbr = 'PCa'\n",
357 |     "    \n",
358 |     "    adata = sc.read_csv(f,dtype=np.int16)\n",
359 |     "    adata = adata.T\n",
360 |     "\n",
361 |     "    ####### Add metadata columns\n",
362 |     "    meta = adata.obs.copy()\n",
363 |     "    meta['sample'] = 'hirz_'+sample\n",
364 |     "    meta['patient'] = 'hirz_'+patient\n",
365 |     "    meta = meta.merge(hirz_annot,how='left',left_index=True,right_index=True,)\n",
366 |     "    meta['phenotype'] = phenot_abbr\n",
367 |     "    meta['dataset'] = 'hirz_2023'\n",
368 |     "\n",
369 |     "    if (meta.index == adata.obs_names).all():\n",
370 |     "        adata.obs = meta.copy()\n",
371 |     "    print(sample)\n",
372 |     "    adata_list.append(adata)\n",
373 |     "\n",
374 |     "adata_concat = ad.concat(adata_list, join='outer', fill_value=0)\n",
375 |     "\n",
376 |     "adata_concat.obs\n",
377 |     "adata_concat.write('sc-reference/hirz_2023/adata_obj.h5ad')"
378 |    ]
379 |   }
380 |  ],
381 |  "metadata": {
382 |   "kernelspec": {
383 |    "display_name": "Python 3",
384 |    "language": "python",
385 |    "name": "python3"
386 |   },
387 |   "language_info": {
388 |    "codemirror_mode": {
389 |     "name": "ipython",
390 |     "version": 3
391 |    },
392 |    "file_extension": ".py",
393 |    "mimetype": "text/x-python",
394 |    "name": "python",
395 |    "nbconvert_exporter": "python",
396 |    "pygments_lexer": "ipython3",
397 |    "version": "3.7.12"
398 |   },
399 |   "orig_nbformat": 4
400 |  },
401 |  "nbformat": 4,
402 |  "nbformat_minor": 2
403 | }
404 | 


--------------------------------------------------------------------------------
/spatial_analysis_environment.yml:
--------------------------------------------------------------------------------
  1 | # packages in environment at /home/ak431480/.conda/envs/squidpy:
  2 | #
  3 | # Name                    Version                   Build  Channel
  4 | _libgcc_mutex             0.1                        main  
  5 | _openmp_mutex             5.1                       1_gnu  
  6 | _r-mutex                  1.0.0               anacondar_1  
  7 | aiohttp                   3.8.3                    pypi_0    pypi
  8 | aiosignal                 1.3.1                    pypi_0    pypi
  9 | alabaster                 0.7.12                   pypi_0    pypi
 10 | anndata                   0.8.0                    pypi_0    pypi
 11 | anndata2ri                1.1                      pypi_0    pypi
 12 | annoy                     1.17.1                   pypi_0    pypi
 13 | anyio                     3.6.2                    pypi_0    pypi
 14 | appdirs                   1.4.4                    pypi_0    pypi
 15 | argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
 16 | argon2-cffi-bindings      21.2.0           py38h0a891b7_2    conda-forge
 17 | asciitree                 0.3.3                    pypi_0    pypi
 18 | astor                     0.8.1                    pypi_0    pypi
 19 | asttokens                 2.2.0                    pypi_0    pypi
 20 | async-timeout             4.0.2                    pypi_0    pypi
 21 | attrs                     22.1.0                   pypi_0    pypi
 22 | autograd                  1.6.2                    pypi_0    pypi
 23 | autograd-gamma            0.5.0                    pypi_0    pypi
 24 | babel                     2.11.0                   pypi_0    pypi
 25 | backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
 26 | backports                 1.0                pyhd8ed1ab_3    conda-forge
 27 | backports-zoneinfo        0.2.1                    pypi_0    pypi
 28 | backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
 29 | beautifulsoup4            4.11.1             pyha770c72_0    conda-forge
 30 | binutils_impl_linux-64    2.38                 h2a08ee3_1  
 31 | binutils_linux-64         2.38.0               hc2dff05_0  
 32 | blas                      1.1                    openblas    conda-forge
 33 | bleach                    5.0.1              pyhd8ed1ab_0    conda-forge
 34 | blosc2                    2.0.0                    pypi_0    pypi
 35 | build                     0.9.0                    pypi_0    pypi
 36 | bwidget                   1.9.14               ha770c72_1    conda-forge
 37 | bzip2                     1.0.8                h7f98852_4    conda-forge
 38 | c-ares                    1.18.1               h7f98852_0    conda-forge
 39 | ca-certificates           2022.10.11           h06a4308_0  
 40 | cachey                    0.2.1                    pypi_0    pypi
 41 | cairo                     1.16.0            h18b612c_1001    conda-forge
 42 | certifi                   2022.9.24        py38h06a4308_0  
 43 | cffi                      1.15.1                   pypi_0    pypi
 44 | charset-normalizer        2.1.1                    pypi_0    pypi
 45 | click                     8.1.3                    pypi_0    pypi
 46 | cloudpickle               2.2.0                    pypi_0    pypi
 47 | cmake                     3.25.0                   pypi_0    pypi
 48 | commonmark                0.9.1                    pypi_0    pypi
 49 | contourpy                 1.0.6                    pypi_0    pypi
 50 | curl                      7.86.0               h5eee18b_0  
 51 | cycler                    0.11.0                   pypi_0    pypi
 52 | cython                    0.29.33                  pypi_0    pypi
 53 | dask                      2022.11.1                pypi_0    pypi
 54 | dask-image                2022.9.0                 pypi_0    pypi
 55 | debugpy                   1.6.4                    pypi_0    pypi
 56 | decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
 57 | defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
 58 | deprecated                1.2.13                   pypi_0    pypi
 59 | docrep                    0.3.2                    pypi_0    pypi
 60 | docstring-parser          0.15                     pypi_0    pypi
 61 | docutils                  0.19                     pypi_0    pypi
 62 | dunamai                   1.15.0                   pypi_0    pypi
 63 | entrypoints               0.4                pyhd8ed1ab_0    conda-forge
 64 | et-xmlfile                1.1.0                    pypi_0    pypi
 65 | executing                 1.2.0              pyhd8ed1ab_0    conda-forge
 66 | fasteners                 0.18                     pypi_0    pypi
 67 | fbpca                     1.0                      pypi_0    pypi
 68 | fcsparser                 0.2.4                    pypi_0    pypi
 69 | flit-core                 3.8.0              pyhd8ed1ab_0    conda-forge
 70 | fontconfig                2.14.1               hef1e5e3_0  
 71 | fonttools                 4.38.0                   pypi_0    pypi
 72 | formulaic                 0.6.6                    pypi_0    pypi
 73 | freetype                  2.10.4               h0708190_1    conda-forge
 74 | freetype-py               2.3.0                    pypi_0    pypi
 75 | fribidi                   1.0.10               h36c2ea0_0    conda-forge
 76 | frozenlist                1.3.3                    pypi_0    pypi
 77 | fsspec                    2022.11.0                pypi_0    pypi
 78 | future                    0.18.3                   pypi_0    pypi
 79 | gcc_impl_linux-64         11.2.0               h1234567_1  
 80 | gcc_linux-64              11.2.0               h5c386dc_0  
 81 | geosketch                 1.2                      pypi_0    pypi
 82 | get-version               3.5.4                    pypi_0    pypi
 83 | gfortran_impl_linux-64    11.2.0               h1234567_1  
 84 | gfortran_linux-64         11.2.0               hc2dff05_0  
 85 | glib                      2.69.1               he621ea3_2  
 86 | gprofiler-official        1.0.0                    pypi_0    pypi
 87 | graphite2                 1.3.14               h295c915_1  
 88 | graphlib-backport         1.0.3                    pypi_0    pypi
 89 | gseapy                    1.1.0                    pypi_0    pypi
 90 | gsva                      1.0.6                    pypi_0    pypi
 91 | gxx_impl_linux-64         11.2.0               h1234567_1  
 92 | gxx_linux-64              11.2.0               hc2dff05_0  
 93 | h5py                      3.7.0                    pypi_0    pypi
 94 | harfbuzz                  4.3.0                hd55b92a_0  
 95 | heapdict                  1.0.1                    pypi_0    pypi
 96 | hsluv                     5.0.3                    pypi_0    pypi
 97 | icu                       58.2              hf484d3e_1000    conda-forge
 98 | idna                      3.4                      pypi_0    pypi
 99 | igraph                    0.10.2                   pypi_0    pypi
100 | imageio                   2.22.4                   pypi_0    pypi
101 | imagesize                 1.4.1                    pypi_0    pypi
102 | importlib-metadata        5.1.0                    pypi_0    pypi
103 | importlib-resources       5.10.0                   pypi_0    pypi
104 | importlib_resources       5.10.2             pyhd8ed1ab_0    conda-forge
105 | inflect                   6.0.2                    pypi_0    pypi
106 | interface-meta            1.3.0                    pypi_0    pypi
107 | intervaltree              2.1.0                    pypi_0    pypi
108 | ipykernel                 6.17.1                   pypi_0    pypi
109 | ipython                   8.7.0                    pypi_0    pypi
110 | ipython-genutils          0.2.0                    pypi_0    pypi
111 | ipython_genutils          0.2.0                      py_1    conda-forge
112 | ipywidgets                8.0.2                    pypi_0    pypi
113 | jedi                      0.18.2             pyhd8ed1ab_0    conda-forge
114 | jinja2                    3.1.2              pyhd8ed1ab_1    conda-forge
115 | joblib                    1.2.0                    pypi_0    pypi
116 | jpeg                      9e                   h166bdaf_1    conda-forge
117 | jsonschema                4.17.3             pyhd8ed1ab_0    conda-forge
118 | jupyter                   1.0.0                    pypi_0    pypi
119 | jupyter-client            7.4.7                    pypi_0    pypi
120 | jupyter-console           6.4.4                    pypi_0    pypi
121 | jupyter-core              5.1.0                    pypi_0    pypi
122 | jupyter-server            1.23.3                   pypi_0    pypi
123 | jupyter_client            7.0.6              pyhd8ed1ab_0    conda-forge
124 | jupyter_core              5.1.3            py38h578d9bd_0    conda-forge
125 | jupyterlab-widgets        3.0.3                    pypi_0    pypi
126 | jupyterlab_pygments       0.2.2              pyhd8ed1ab_0    conda-forge
127 | kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
128 | keyutils                  1.6.1                h166bdaf_0    conda-forge
129 | kiwisolver                1.4.4                    pypi_0    pypi
130 | krb5                      1.19.3               h3790be6_0    conda-forge
131 | ld_impl_linux-64          2.38                 h1181459_1  
132 | leidenalg                 0.9.0                    pypi_0    pypi
133 | lerc                      3.0                  h295c915_0  
134 | libcurl                   7.86.0               h91b91d3_0  
135 | libdeflate                1.8                  h7f8727e_5  
136 | libedit                   3.1.20191231         he28a2e2_2    conda-forge
137 | libev                     4.33                 h516909a_1    conda-forge
138 | libffi                    3.4.2                h6a678d5_6  
139 | libgcc-devel_linux-64     11.2.0               h1234567_1  
140 | libgcc-ng                 11.2.0               h1234567_1  
141 | libgfortran-ng            11.2.0               h00389a5_1  
142 | libgfortran5              11.2.0               h1234567_1  
143 | libgomp                   11.2.0               h1234567_1  
144 | libnghttp2                1.46.0               hce63b2e_0  
145 | libopenblas               0.3.20          pthreads_h78a6416_0    conda-forge
146 | libpng                    1.6.37               hbc83047_0  
147 | libsodium                 1.0.18               h36c2ea0_1    conda-forge
148 | libssh2                   1.10.0               ha56f1ee_2    conda-forge
149 | libstdcxx-devel_linux-64  11.2.0               h1234567_1  
150 | libstdcxx-ng              11.2.0               h1234567_1  
151 | libtiff                   4.4.0                hecacb30_2  
152 | libuuid                   2.32.1            h7f98852_1000    conda-forge
153 | libwebp-base              1.2.4                h5eee18b_0  
154 | libxcb                    1.13              h7f98852_1004    conda-forge
155 | libxml2                   2.9.14               h74e7548_0  
156 | lifelines                 0.27.8                   pypi_0    pypi
157 | llvmlite                  0.38.1                   pypi_0    pypi
158 | locket                    1.0.0                    pypi_0    pypi
159 | louvain                   0.8.0                    pypi_0    pypi
160 | lz4-c                     1.9.3                h9c3ff4c_1    conda-forge
161 | magicgui                  0.6.1                    pypi_0    pypi
162 | make                      4.3                  hd18ef5c_1    conda-forge
163 | markupsafe                2.1.1            py38h0a891b7_1    conda-forge
164 | matplotlib                3.6.2                    pypi_0    pypi
165 | matplotlib-inline         0.1.6              pyhd8ed1ab_0    conda-forge
166 | matplotlib-scalebar       0.8.1                    pypi_0    pypi
167 | matplotlib-venn           0.11.9                   pypi_0    pypi
168 | mistune                   2.0.4              pyhd8ed1ab_0    conda-forge
169 | msgpack                   1.0.4                    pypi_0    pypi
170 | multidict                 6.0.2                    pypi_0    pypi
171 | napari                    0.4.15                   pypi_0    pypi
172 | napari-console            0.0.6                    pypi_0    pypi
173 | napari-plugin-engine      0.2.0                    pypi_0    pypi
174 | napari-svg                0.1.6                    pypi_0    pypi
175 | natsort                   8.2.0                    pypi_0    pypi
176 | nbclassic                 0.4.8                    pypi_0    pypi
177 | nbclient                  0.7.2              pyhd8ed1ab_0    conda-forge
178 | nbconvert                 7.2.5                    pypi_0    pypi
179 | nbconvert-core            7.2.8              pyhd8ed1ab_0    conda-forge
180 | nbconvert-pandoc          7.2.8              pyhd8ed1ab_0    conda-forge
181 | nbformat                  5.7.0                    pypi_0    pypi
182 | ncls                      0.0.66                   pypi_0    pypi
183 | ncurses                   6.3                  h5eee18b_3  
184 | nest-asyncio              1.5.6              pyhd8ed1ab_0    conda-forge
185 | networkx                  2.8.8                    pypi_0    pypi
186 | notebook                  6.5.2                    pypi_0    pypi
187 | notebook-shim             0.2.2                    pypi_0    pypi
188 | npe2                      0.6.1                    pypi_0    pypi
189 | numba                     0.55.2                   pypi_0    pypi
190 | numcodecs                 0.10.2                   pypi_0    pypi
191 | numexpr                   2.8.4                    pypi_0    pypi
192 | numpy                     1.22.4                   pypi_0    pypi
193 | numpydoc                  1.5.0                    pypi_0    pypi
194 | omnipath                  1.0.5                    pypi_0    pypi
195 | openblas                  0.3.20          pthreads_h320a7e8_0    conda-forge
196 | openpyxl                  3.1.2                    pypi_0    pypi
197 | openssl                   1.1.1s               h7f8727e_0  
198 | packaging                 21.3                     pypi_0    pypi
199 | palantir                  1.0.1                    pypi_0    pypi
200 | pandas                    1.5.2                    pypi_0    pypi
201 | pandoc                    2.12                 h06a4308_1  
202 | pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge
203 | pango                     1.50.7               h05da053_0  
204 | parso                     0.8.3              pyhd8ed1ab_0    conda-forge
205 | partd                     1.3.0                    pypi_0    pypi
206 | patsy                     0.5.3                    pypi_0    pypi
207 | pcre                      8.45                 h9c3ff4c_0    conda-forge
208 | pcre2                     10.37                h032f7d1_0    conda-forge
209 | pep517                    0.13.0                   pypi_0    pypi
210 | pexpect                   4.8.0              pyh1a96a4e_2    conda-forge
211 | phenograph                1.5.7                    pypi_0    pypi
212 | pickleshare               0.7.5                   py_1003    conda-forge
213 | pillow                    9.3.0                    pypi_0    pypi
214 | pims                      0.6.1                    pypi_0    pypi
215 | pint                      0.20.1                   pypi_0    pypi
216 | pip                       22.2.2           py38h06a4308_0  
217 | pixman                    0.38.0            h516909a_1003    conda-forge
218 | pkgutil-resolve-name      1.3.10             pyhd8ed1ab_0    conda-forge
219 | platformdirs              2.5.4                    pypi_0    pypi
220 | prometheus_client         0.15.0             pyhd8ed1ab_0    conda-forge
221 | prompt-toolkit            3.0.33                   pypi_0    pypi
222 | psutil                    5.9.4                    pypi_0    pypi
223 | psygnal                   0.6.1                    pypi_0    pypi
224 | pthread-stubs             0.4               h36c2ea0_1001    conda-forge
225 | ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge
226 | pure_eval                 0.2.2              pyhd8ed1ab_0    conda-forge
227 | py-cpuinfo                9.0.0                    pypi_0    pypi
228 | pycparser                 2.21               pyhd8ed1ab_0    conda-forge
229 | pydantic                  1.10.2                   pypi_0    pypi
230 | pydot                     1.4.2                    pypi_0    pypi
231 | pygments                  2.13.0                   pypi_0    pypi
232 | pynndescent               0.5.8                    pypi_0    pypi
233 | pyopengl                  3.1.6                    pypi_0    pypi
234 | pyparsing                 3.0.9                    pypi_0    pypi
235 | pyqt5                     5.15.7                   pypi_0    pypi
236 | pyqt5-qt5                 5.15.2                   pypi_0    pypi
237 | pyqt5-sip                 12.11.0                  pypi_0    pypi
238 | pyranges                  0.0.120                  pypi_0    pypi
239 | pyrle                     0.0.35                   pypi_0    pypi
240 | pyrsistent                0.19.2                   pypi_0    pypi
241 | python                    3.8.15               h7a1cb2a_2  
242 | python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
243 | python-fastjsonschema     2.16.2             pyhd8ed1ab_0    conda-forge
244 | python_abi                3.8                      2_cp38    conda-forge
245 | pytomlpp                  1.0.11                   pypi_0    pypi
246 | pytz                      2022.6                   pypi_0    pypi
247 | pytz-deprecation-shim     0.1.0.post0              pypi_0    pypi
248 | pywavelets                1.4.1                    pypi_0    pypi
249 | pyyaml                    6.0                      pypi_0    pypi
250 | pyzmq                     24.0.1                   pypi_0    pypi
251 | qtconsole                 5.4.0                    pypi_0    pypi
252 | qtpy                      2.3.0                    pypi_0    pypi
253 | r-askpass                 1.1               r42h76d94ec_0  
254 | r-assertthat              0.2.1             r42hc72bb7e_3    conda-forge
255 | r-backports               1.4.1             r42h76d94ec_0  
256 | r-base                    4.2.0                h1ae530e_0  
257 | r-base64enc               0.1_3             r42h76d94ec_4  
258 | r-bh                      1.78.0_0          r42hc72bb7e_1    conda-forge
259 | r-bit                     4.0.4             r42h76d94ec_0  
260 | r-bit64                   4.0.5             r42h76d94ec_0  
261 | r-blob                    1.2.3             r42hc72bb7e_1    conda-forge
262 | r-boot                    1.3_28.1          r42hc72bb7e_0    conda-forge
263 | r-bslib                   0.3.1             r42h6115d3f_0  
264 | r-cachem                  1.0.6             r42h76d94ec_0  
265 | r-callr                   3.7.3             r42hc72bb7e_0    conda-forge
266 | r-cellranger              1.1.0           r42hc72bb7e_1005    conda-forge
267 | r-class                   7.3_20            r42h76d94ec_0  
268 | r-cli                     3.3.0             r42h884c59f_0  
269 | r-clipr                   0.8.0             r42hc72bb7e_1    conda-forge
270 | r-cluster                 2.1.3             r42h640688f_0  
271 | r-codetools               0.2_18            r42hc72bb7e_1    conda-forge
272 | r-colorspace              2.0_3             r42h76d94ec_0  
273 | r-commonmark              1.8.0             r42h76d94ec_0  
274 | r-cpp11                   0.4.3             r42hc72bb7e_0    conda-forge
275 | r-crayon                  1.5.2             r42hc72bb7e_1    conda-forge
276 | r-crul                    1.3               r42h785f33e_1    conda-forge
277 | r-curl                    4.3.2             r42h76d94ec_0  
278 | r-data.table              1.14.2            r42h76d94ec_0  
279 | r-dbi                     1.1.3             r42hc72bb7e_1    conda-forge
280 | r-dbplyr                  2.2.1             r42hc72bb7e_1    conda-forge
281 | r-digest                  0.6.29            r42h884c59f_0  
282 | r-dplyr                   1.0.9             r42h884c59f_0  
283 | r-dqrng                   0.3.0             r42h884c59f_0  
284 | r-dtplyr                  1.2.2             r42hc72bb7e_2    conda-forge
285 | r-e1071                   1.7_9             r42h884c59f_0  
286 | r-ellipsis                0.3.2             r42h76d94ec_0  
287 | r-evaluate                0.19              r42hc72bb7e_0    conda-forge
288 | r-fansi                   1.0.3             r42h76d94ec_0  
289 | r-farver                  2.1.0             r42h884c59f_0  
290 | r-fastmap                 1.1.0             r42h884c59f_0  
291 | r-fontawesome             0.4.0             r42hc72bb7e_0    conda-forge
292 | r-forcats                 0.5.2             r42hc72bb7e_1    conda-forge
293 | r-foreach                 1.5.2             r42hc72bb7e_1    conda-forge
294 | r-foreign                 0.8_82            r42h76d94ec_0  
295 | r-formatr                 1.13              r42hc72bb7e_0    conda-forge
296 | r-fs                      1.5.2             r42h884c59f_0  
297 | r-future                  1.30.0            r42hc72bb7e_0    conda-forge
298 | r-future.apply            1.10.0            r42hc72bb7e_0    conda-forge
299 | r-gargle                  1.2.1             r42hc72bb7e_1    conda-forge
300 | r-generics                0.1.3             r42hc72bb7e_1    conda-forge
301 | r-gistr                   0.9.0             r42hc72bb7e_1    conda-forge
302 | r-globals                 0.16.2            r42hc72bb7e_0    conda-forge
303 | r-glue                    1.6.2             r42h76d94ec_0  
304 | r-googledrive             2.0.0             r42hc72bb7e_1    conda-forge
305 | r-googlesheets4           1.0.1             r42h785f33e_1    conda-forge
306 | r-gower                   1.0.0             r42h76d94ec_0  
307 | r-gtable                  0.3.1             r42hc72bb7e_1    conda-forge
308 | r-hardhat                 1.2.0             r42hc72bb7e_1    conda-forge
309 | r-haven                   2.5.0             r42h884c59f_0  
310 | r-hexbin                  1.28.2            r42h640688f_0  
311 | r-highr                   0.10              r42hc72bb7e_0    conda-forge
312 | r-hms                     1.1.2             r42hc72bb7e_1    conda-forge
313 | r-htmltools               0.5.2             r42h76d94ec_0  
314 | r-htmlwidgets             1.5.4             r42h6115d3f_0  
315 | r-httpcode                0.3.0             r42ha770c72_2    conda-forge
316 | r-httpuv                  1.6.5             r42h884c59f_0  
317 | r-httr                    1.4.4             r42hc72bb7e_1    conda-forge
318 | r-ids                     1.0.1             r42hc72bb7e_2    conda-forge
319 | r-irdisplay               1.1               r42hd8ed1ab_1    conda-forge
320 | r-irkernel                1.3.1             r42h785f33e_0    conda-forge
321 | r-isoband                 0.2.5             r42h884c59f_0  
322 | r-iterators               1.0.14            r42hc72bb7e_1    conda-forge
323 | r-jquerylib               0.1.4             r42hc72bb7e_1    conda-forge
324 | r-jsonlite                1.8.0             r42h76d94ec_0  
325 | r-kernsmooth              2.23_20           r42h640688f_0  
326 | r-knitr                   1.41              r42hc72bb7e_0    conda-forge
327 | r-labeling                0.4.2             r42hc72bb7e_2    conda-forge
328 | r-later                   1.3.0             r42h884c59f_0  
329 | r-lattice                 0.20_45           r42h76d94ec_0  
330 | r-lazyeval                0.2.2             r42h76d94ec_0  
331 | r-lifecycle               1.0.1             r42h142f84f_0  
332 | r-listenv                 0.9.0             r42hc72bb7e_0    conda-forge
333 | r-lobstr                  1.1.1             r42h884c59f_0  
334 | r-lubridate               1.8.0             r42h884c59f_0  
335 | r-magrittr                2.0.3             r42h76d94ec_0  
336 | r-maps                    3.4.0             r42h76d94ec_0  
337 | r-mass                    7.3_57            r42h76d94ec_0  
338 | r-mime                    0.12              r42h76d94ec_0  
339 | r-modelmetrics            1.2.2.2           r42h884c59f_0  
340 | r-munsell                 0.5.0           r42hc72bb7e_1005    conda-forge
341 | r-nlme                    3.1_157           r42h640688f_0  
342 | r-nnet                    7.3_17            r42h76d94ec_0  
343 | r-numderiv                2016.8_1.1        r42hc72bb7e_4    conda-forge
344 | r-openssl                 2.0.2             r42h76d94ec_0  
345 | r-parallelly              1.34.0            r42hc72bb7e_0    conda-forge
346 | r-pbdzmq                  0.3_7             r42h884c59f_0  
347 | r-pillar                  1.8.1             r42hc72bb7e_1    conda-forge
348 | r-pkgconfig               2.0.3             r42hc72bb7e_2    conda-forge
349 | r-plyr                    1.8.7             r42h884c59f_0  
350 | r-prettyunits             1.1.1             r42hc72bb7e_2    conda-forge
351 | r-proc                    1.18.0            r42h884c59f_0  
352 | r-processx                3.5.3             r42h76d94ec_0  
353 | r-progress                1.2.2             r42hc72bb7e_3    conda-forge
354 | r-progressr               0.13.0            r42hc72bb7e_0    conda-forge
355 | r-promises                1.2.0.1           r42h884c59f_0  
356 | r-proxy                   0.4_26            r42h76d94ec_0  
357 | r-pryr                    0.1.5             r42h884c59f_0  
358 | r-ps                      1.7.0             r42h76d94ec_0  
359 | r-purrr                   0.3.4             r42h76d94ec_0  
360 | r-quantmod                0.4.20            r42hc72bb7e_1    conda-forge
361 | r-r6                      2.5.1             r42hc72bb7e_1    conda-forge
362 | r-randomforest            4.7_1.1           r42h640688f_0  
363 | r-rappdirs                0.3.3             r42h76d94ec_0  
364 | r-rbokeh                  0.5.2             r42hc72bb7e_2    conda-forge
365 | r-rcolorbrewer            1.1_3             r42h785f33e_1    conda-forge
366 | r-rcpp                    1.0.8.3           r42h884c59f_0  
367 | r-readr                   2.1.2             r42h884c59f_0  
368 | r-readxl                  1.4.0             r42h884c59f_0  
369 | r-rematch                 1.0.1           r42hc72bb7e_1005    conda-forge
370 | r-rematch2                2.1.2             r42hc72bb7e_2    conda-forge
371 | r-repr                    1.1.5             r42h785f33e_0    conda-forge
372 | r-reprex                  2.0.2             r42hc72bb7e_1    conda-forge
373 | r-reshape2                1.4.4             r42h884c59f_0  
374 | r-rlang                   1.0.2             r42h884c59f_0  
375 | r-rmarkdown               2.19              r42hc72bb7e_0    conda-forge
376 | r-rpart                   4.1.16            r42h76d94ec_0  
377 | r-rstudioapi              0.14              r42hc72bb7e_1    conda-forge
378 | r-rvest                   1.0.3             r42hc72bb7e_1    conda-forge
379 | r-sass                    0.4.1             r42h884c59f_0  
380 | r-scales                  1.2.1             r42hc72bb7e_1    conda-forge
381 | r-selectr                 0.4_2             r42hc72bb7e_2    conda-forge
382 | r-shape                   1.4.6             r42ha770c72_1    conda-forge
383 | r-shiny                   1.7.4             r42h785f33e_0    conda-forge
384 | r-sitmo                   2.0.2             r42h884c59f_0  
385 | r-sourcetools             0.1.7             r42h884c59f_0  
386 | r-spatial                 7.3_15            r42h76d94ec_0  
387 | r-squarem                 2021.1            r42hc72bb7e_1    conda-forge
388 | r-stringi                 1.7.6             r42h884c59f_0  
389 | r-stringr                 1.4.0             r42h6115d3f_0  
390 | r-sys                     3.4               r42h76d94ec_0  
391 | r-tibble                  3.1.7             r42h76d94ec_0  
392 | r-tidyr                   1.2.0             r42h884c59f_0  
393 | r-tidyselect              1.1.2             r42h142f84f_0  
394 | r-timedate                4022.108          r42hc72bb7e_0    conda-forge
395 | r-tinytex                 0.43              r42hc72bb7e_0    conda-forge
396 | r-triebeard               0.3.0             r42h884c59f_0  
397 | r-ttr                     0.24.3            r42h76d94ec_0  
398 | r-tzdb                    0.3.0             r42h884c59f_0  
399 | r-urltools                1.7.3             r42h884c59f_0  
400 | r-utf8                    1.2.2             r42h76d94ec_0  
401 | r-uuid                    1.1_0             r42h76d94ec_0  
402 | r-vctrs                   0.4.1             r42h884c59f_0  
403 | r-viridislite             0.4.1             r42hc72bb7e_1    conda-forge
404 | r-vroom                   1.5.7             r42h884c59f_0  
405 | r-withr                   2.5.0             r42hc72bb7e_1    conda-forge
406 | r-xfun                    0.31              r42h76d94ec_0  
407 | r-xml2                    1.3.3             r42h884c59f_0  
408 | r-xtable                  1.8_4             r42hc72bb7e_4    conda-forge
409 | r-xts                     0.12.1            r42h76d94ec_0  
410 | r-yaml                    2.3.5             r42h76d94ec_0  
411 | r-zoo                     1.8_10            r42h76d94ec_0  
412 | readline                  8.2                  h5eee18b_0  
413 | requests                  2.28.1                   pypi_0    pypi
414 | rich                      12.6.0                   pypi_0    pypi
415 | rpy2                      3.5.8                    pypi_0    pypi
416 | scanorama                 1.7                      pypi_0    pypi
417 | scanpy                    1.9.1                    pypi_0    pypi
418 | scib                      1.1.1                    pypi_0    pypi
419 | scikit-image              0.19.3                   pypi_0    pypi
420 | scikit-learn              1.1.3                    pypi_0    pypi
421 | scikit-posthocs           0.8.0                    pypi_0    pypi
422 | scipy                     1.9.3                    pypi_0    pypi
423 | scrublet                  0.2.3                    pypi_0    pypi
424 | seaborn                   0.12.1                   pypi_0    pypi
425 | seacells                  0.3.2                    pypi_0    pypi
426 | send2trash                1.8.0              pyhd8ed1ab_0    conda-forge
427 | session-info              1.0.0                    pypi_0    pypi
428 | setuptools                65.5.0           py38h06a4308_0  
429 | six                       1.16.0             pyh6c4a22f_0    conda-forge
430 | slicerator                1.1.0                    pypi_0    pypi
431 | sniffio                   1.3.0                    pypi_0    pypi
432 | snowballstemmer           2.2.0                    pypi_0    pypi
433 | sorted-nearest            0.0.38                   pypi_0    pypi
434 | sortedcontainers          2.4.0                    pypi_0    pypi
435 | soupsieve                 2.3.2.post1        pyhd8ed1ab_0    conda-forge
436 | sphinx                    5.3.0                    pypi_0    pypi
437 | sphinxcontrib-applehelp   1.0.2                    pypi_0    pypi
438 | sphinxcontrib-devhelp     1.0.2                    pypi_0    pypi
439 | sphinxcontrib-htmlhelp    2.0.0                    pypi_0    pypi
440 | sphinxcontrib-jsmath      1.0.1                    pypi_0    pypi
441 | sphinxcontrib-qthelp      1.0.3                    pypi_0    pypi
442 | sphinxcontrib-serializinghtml 1.1.5                    pypi_0    pypi
443 | sqlite                    3.40.0               h5082296_0  
444 | squidpy                   1.2.3                    pypi_0    pypi
445 | stack_data                0.6.2              pyhd8ed1ab_0    conda-forge
446 | statsmodels               0.13.5                   pypi_0    pypi
447 | stdlib-list               0.8.0                    pypi_0    pypi
448 | superqt                   0.4.0                    pypi_0    pypi
449 | sysroot_linux-64          2.12                he073ed8_15    conda-forge
450 | tables                    3.8.0                    pypi_0    pypi
451 | tabulate                  0.9.0                    pypi_0    pypi
452 | terminado                 0.17.0                   pypi_0    pypi
453 | texttable                 1.6.7                    pypi_0    pypi
454 | threadpoolctl             3.1.0                    pypi_0    pypi
455 | tifffile                  2022.10.10               pypi_0    pypi
456 | tinycss2                  1.2.1              pyhd8ed1ab_0    conda-forge
457 | tk                        8.6.12               h1ccaba5_0  
458 | tktable                   2.10                 hb7b940f_3    conda-forge
459 | tomli                     2.0.1                    pypi_0    pypi
460 | toolz                     0.12.0                   pypi_0    pypi
461 | tornado                   6.2                      pypi_0    pypi
462 | tqdm                      4.64.1                   pypi_0    pypi
463 | traitlets                 5.6.0                    pypi_0    pypi
464 | typer                     0.7.0                    pypi_0    pypi
465 | typing-extensions         4.4.0                hd8ed1ab_0    conda-forge
466 | typing_extensions         4.4.0              pyha770c72_0    conda-forge
467 | tzdata                    2022.7                   pypi_0    pypi
468 | tzlocal                   4.2                      pypi_0    pypi
469 | umap-learn                0.5.3                    pypi_0    pypi
470 | urllib3                   1.26.13                  pypi_0    pypi
471 | validators                0.20.0                   pypi_0    pypi
472 | venn                      0.1.3                    pypi_0    pypi
473 | vispy                     0.12.1                   pypi_0    pypi
474 | wcwidth                   0.2.5                    pypi_0    pypi
475 | webencodings              0.5.1                    pypi_0    pypi
476 | websocket-client          1.4.2                    pypi_0    pypi
477 | wheel                     0.37.1             pyhd3eb1b0_0  
478 | widgetsnbextension        4.0.3                    pypi_0    pypi
479 | wrapt                     1.14.1                   pypi_0    pypi
480 | xarray                    2022.11.0                pypi_0    pypi
481 | xgboost                   2.0.0                    pypi_0    pypi
482 | xlsxwriter                3.1.7                    pypi_0    pypi
483 | xorg-kbproto              1.0.7             h7f98852_1002    conda-forge
484 | xorg-libice               1.0.10               h7f98852_0    conda-forge
485 | xorg-libsm                1.2.3             hd9c2040_1000    conda-forge
486 | xorg-libx11               1.7.2                h7f98852_0    conda-forge
487 | xorg-libxau               1.0.9                h7f98852_0    conda-forge
488 | xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
489 | xorg-libxext              1.3.4                h7f98852_1    conda-forge
490 | xorg-libxrender           0.9.10            h7f98852_1003    conda-forge
491 | xorg-renderproto          0.11.1            h7f98852_1002    conda-forge
492 | xorg-xextproto            7.3.0             h7f98852_1002    conda-forge
493 | xorg-xproto               7.0.31            h7f98852_1007    conda-forge
494 | xz                        5.2.6                h5eee18b_0  
495 | yarl                      1.8.1                    pypi_0    pypi
496 | zarr                      2.13.3                   pypi_0    pypi
497 | zeromq                    4.3.4                h9c3ff4c_1    conda-forge
498 | zipp                      3.11.0             pyhd8ed1ab_0    conda-forge
499 | zlib                      1.2.13               h5eee18b_0  
500 | zstd                      1.5.2                ha4553b6_0  
501 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU GENERAL PUBLIC LICENSE
  2 |                        Version 3, 29 June 2007
  3 | 
  4 |  Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
  5 |  Everyone is permitted to copy and distribute verbatim copies
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  8 |                             Preamble
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 10 |   The GNU General Public License is a free, copyleft license for
 11 | software and other kinds of works.
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154 |   2. Basic Permissions.
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195 |   4. Conveying Verbatim Copies.
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197 |   You may convey verbatim copies of the Program's source code as you
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202 | keep intact all notices of the absence of any warranty; and give all
203 | recipients a copy of this License along with the Program.
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205 |   You may charge any price or no price for each copy that you convey,
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208 |   5. Conveying Modified Source Versions.
209 | 
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214 |     a) The work must carry prominent notices stating that you modified
215 |     it, and giving a relevant date.
216 | 
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220 |     "keep intact all notices".
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222 |     c) You must license the entire work, as a whole, under this
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226 |     regardless of how they are packaged.  This License gives no
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228 |     invalidate such permission if you have separately received it.
229 | 
230 |     d) If the work has interactive user interfaces, each must display
231 |     Appropriate Legal Notices; however, if the Program has interactive
232 |     interfaces that do not display Appropriate Legal Notices, your
233 |     work need not make them do so.
234 | 
235 |   A compilation of a covered work with other separate and independent
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237 | and which are not combined with it such as to form a larger program,
238 | in or on a volume of a storage or distribution medium, is called an
239 | "aggregate" if the compilation and its resulting copyright are not
240 | used to limit the access or legal rights of the compilation's users
241 | beyond what the individual works permit.  Inclusion of a covered work
242 | in an aggregate does not cause this License to apply to the other
243 | parts of the aggregate.
244 | 
245 |   6. Conveying Non-Source Forms.
246 | 
247 |   You may convey a covered work in object code form under the terms
248 | of sections 4 and 5, provided that you also convey the
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250 | in one of these ways:
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252 |     a) Convey the object code in, or embodied in, a physical product
253 |     (including a physical distribution medium), accompanied by the
254 |     Corresponding Source fixed on a durable physical medium
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256 | 
257 |     b) Convey the object code in, or embodied in, a physical product
258 |     (including a physical distribution medium), accompanied by a
259 |     written offer, valid for at least three years and valid for as
260 |     long as you offer spare parts or customer support for that product
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262 |     copy of the Corresponding Source for all the software in the
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264 |     medium customarily used for software interchange, for a price no
265 |     more than your reasonable cost of physically performing this
266 |     conveying of source, or (2) access to copy the
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268 | 
269 |     c) Convey individual copies of the object code with a copy of the
270 |     written offer to provide the Corresponding Source.  This
271 |     alternative is allowed only occasionally and noncommercially, and
272 |     only if you received the object code with such an offer, in accord
273 |     with subsection 6b.
274 | 
275 |     d) Convey the object code by offering access from a designated
276 |     place (gratis or for a charge), and offer equivalent access to the
277 |     Corresponding Source in the same way through the same place at no
278 |     further charge.  You need not require recipients to copy the
279 |     Corresponding Source along with the object code.  If the place to
280 |     copy the object code is a network server, the Corresponding Source
281 |     may be on a different server (operated by you or a third party)
282 |     that supports equivalent copying facilities, provided you maintain
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284 |     Corresponding Source.  Regardless of what server hosts the
285 |     Corresponding Source, you remain obligated to ensure that it is
286 |     available for as long as needed to satisfy these requirements.
287 | 
288 |     e) Convey the object code using peer-to-peer transmission, provided
289 |     you inform other peers where the object code and Corresponding
290 |     Source of the work are being offered to the general public at no
291 |     charge under subsection 6d.
292 | 
293 |   A separable portion of the object code, whose source code is excluded
294 | from the Corresponding Source as a System Library, need not be
295 | included in conveying the object code work.
296 | 
297 |   A "User Product" is either (1) a "consumer product", which means any
298 | tangible personal property which is normally used for personal, family,
299 | or household purposes, or (2) anything designed or sold for incorporation
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303 | typical or common use of that class of product, regardless of the status
304 | of the particular user or of the way in which the particular user
305 | actually uses, or expects or is expected to use, the product.  A product
306 | is a consumer product regardless of whether the product has substantial
307 | commercial, industrial or non-consumer uses, unless such uses represent
308 | the only significant mode of use of the product.
309 | 
310 |   "Installation Information" for a User Product means any methods,
311 | procedures, authorization keys, or other information required to install
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313 | a modified version of its Corresponding Source.  The information must
314 | suffice to ensure that the continued functioning of the modified object
315 | code is in no case prevented or interfered with solely because
316 | modification has been made.
317 | 
318 |   If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
320 | part of a transaction in which the right of possession and use of the
321 | User Product is transferred to the recipient in perpetuity or for a
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325 | if neither you nor any third party retains the ability to install
326 | modified object code on the User Product (for example, the work has
327 | been installed in ROM).
328 | 
329 |   The requirement to provide Installation Information does not include a
330 | requirement to continue to provide support service, warranty, or updates
331 | for a work that has been modified or installed by the recipient, or for
332 | the User Product in which it has been modified or installed.  Access to a
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335 | protocols for communication across the network.
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337 |   Corresponding Source conveyed, and Installation Information provided,
338 | in accord with this section must be in a format that is publicly
339 | documented (and with an implementation available to the public in
340 | source code form), and must require no special password or key for
341 | unpacking, reading or copying.
342 | 
343 |   7. Additional Terms.
344 | 
345 |   "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law.  If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 | 
354 |   When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it.  (Additional permissions may be written to require their own
357 | removal in certain cases when you modify the work.)  You may place
358 | additional permissions on material, added by you to a covered work,
359 | for which you have or can give appropriate copyright permission.
360 | 
361 |   Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
364 | 
365 |     a) Disclaiming warranty or limiting liability differently from the
366 |     terms of sections 15 and 16 of this License; or
367 | 
368 |     b) Requiring preservation of specified reasonable legal notices or
369 |     author attributions in that material or in the Appropriate Legal
370 |     Notices displayed by works containing it; or
371 | 
372 |     c) Prohibiting misrepresentation of the origin of that material, or
373 |     requiring that modified versions of such material be marked in
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376 |     d) Limiting the use for publicity purposes of names of licensors or
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378 | 
379 |     e) Declining to grant rights under trademark law for use of some
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381 | 
382 |     f) Requiring indemnification of licensors and authors of that
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384 |     it) with contractual assumptions of liability to the recipient, for
385 |     any liability that these contractual assumptions directly impose on
386 |     those licensors and authors.
387 | 
388 |   All other non-permissive additional terms are considered "further
389 | restrictions" within the meaning of section 10.  If the Program as you
390 | received it, or any part of it, contains a notice stating that it is
391 | governed by this License along with a term that is a further
392 | restriction, you may remove that term.  If a license document contains
393 | a further restriction but permits relicensing or conveying under this
394 | License, you may add to a covered work material governed by the terms
395 | of that license document, provided that the further restriction does
396 | not survive such relicensing or conveying.
397 | 
398 |   If you add terms to a covered work in accord with this section, you
399 | must place, in the relevant source files, a statement of the
400 | additional terms that apply to those files, or a notice indicating
401 | where to find the applicable terms.
402 | 
403 |   Additional terms, permissive or non-permissive, may be stated in the
404 | form of a separately written license, or stated as exceptions;
405 | the above requirements apply either way.
406 | 
407 |   8. Termination.
408 | 
409 |   You may not propagate or modify a covered work except as expressly
410 | provided under this License.  Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 | 
415 |   However, if you cease all violation of this License, then your
416 | license from a particular copyright holder is reinstated (a)
417 | provisionally, unless and until the copyright holder explicitly and
418 | finally terminates your license, and (b) permanently, if the copyright
419 | holder fails to notify you of the violation by some reasonable means
420 | prior to 60 days after the cessation.
421 | 
422 |   Moreover, your license from a particular copyright holder is
423 | reinstated permanently if the copyright holder notifies you of the
424 | violation by some reasonable means, this is the first time you have
425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 | 
429 |   Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
431 | this License.  If your rights have been terminated and not permanently
432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 | 
435 |   9. Acceptance Not Required for Having Copies.
436 | 
437 |   You are not required to accept this License in order to receive or
438 | run a copy of the Program.  Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance.  However,
441 | nothing other than this License grants you permission to propagate or
442 | modify any covered work.  These actions infringe copyright if you do
443 | not accept this License.  Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 | 
446 |   10. Automatic Licensing of Downstream Recipients.
447 | 
448 |   Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License.  You are not responsible
451 | for enforcing compliance by third parties with this License.
452 | 
453 |   An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations.  If propagation of a covered
456 | work results from an entity transaction, each party to that
457 | transaction who receives a copy of the work also receives whatever
458 | licenses to the work the party's predecessor in interest had or could
459 | give under the previous paragraph, plus a right to possession of the
460 | Corresponding Source of the work from the predecessor in interest, if
461 | the predecessor has it or can get it with reasonable efforts.
462 | 
463 |   You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License.  For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 | 
471 |   11. Patents.
472 | 
473 |   A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based.  The
475 | work thus licensed is called the contributor's "contributor version".
476 | 
477 |   A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version.  For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 | 
487 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 | 
492 |   In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement).  To "grant" such a patent license to a
496 | party means to make such an agreement or commitment not to enforce a
497 | patent against the party.
498 | 
499 |   If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
501 | to copy, free of charge and under the terms of this License, through a
502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients.  "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 | 
513 |   If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 | 
521 |   A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License.  You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 | 
536 |   Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 | 
540 |   12. No Surrender of Others' Freedom.
541 | 
542 |   If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License.  If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all.  For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 | 
552 |   13. Use with the GNU Affero General Public License.
553 | 
554 |   Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work.  The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 | 
563 |   14. Revised Versions of this License.
564 | 
565 |   The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time.  Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 | 
570 |   Each version is given a distinguishing version number.  If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation.  If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 | 
579 |   If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 | 
584 |   Later license versions may give you additional or different
585 | permissions.  However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 | 
589 |   15. Disclaimer of Warranty.
590 | 
591 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 | 
600 |   16. Limitation of Liability.
601 | 
602 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 | 
612 |   17. Interpretation of Sections 15 and 16.
613 | 
614 |   If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 | 
621 |                      END OF TERMS AND CONDITIONS
622 | 
623 |             How to Apply These Terms to Your New Programs
624 | 
625 |   If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 | 
629 |   To do so, attach the following notices to the program.  It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 | 
634 |     <one line to give the program's name and a brief idea of what it does.>
635 |     Copyright (C) <year>  <name of author>
636 | 
637 |     This program is free software: you can redistribute it and/or modify
638 |     it under the terms of the GNU General Public License as published by
639 |     the Free Software Foundation, either version 3 of the License, or
640 |     (at your option) any later version.
641 | 
642 |     This program is distributed in the hope that it will be useful,
643 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
644 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
645 |     GNU General Public License for more details.
646 | 
647 |     You should have received a copy of the GNU General Public License
648 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
649 | 
650 | Also add information on how to contact you by electronic and paper mail.
651 | 
652 |   If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 | 
655 |     <program>  Copyright (C) <year>  <name of author>
656 |     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 |     This is free software, and you are welcome to redistribute it
658 |     under certain conditions; type `show c' for details.
659 | 
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License.  Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 | 
664 |   You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | <https://www.gnu.org/licenses/>.
668 | 
669 |   The GNU General Public License does not permit incorporating your program
670 | into proprietary programs.  If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library.  If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License.  But first, please read
674 | <https://www.gnu.org/licenses/why-not-lgpl.html>.
675 | 


--------------------------------------------------------------------------------
/single_cell_analysis_environment.yml:
--------------------------------------------------------------------------------
  1 | # packages in environment at /home/ak431480/.conda/envs/scib-pipeline-R4.0:
  2 | #
  3 | # Name                    Version                   Build  Channel
  4 | _libgcc_mutex             0.1                 conda_forge    conda-forge
  5 | _openmp_mutex             4.5                  2_kmp_llvm    conda-forge
  6 | _r-mutex                  1.0.1               anacondar_1    conda-forge
  7 | absl-py                   1.4.0              pyhd8ed1ab_0    conda-forge
  8 | adjusttext                0.7.3                    pypi_0    pypi
  9 | aioeasywebdav             2.4.0           py37h89c1867_1001    conda-forge
 10 | aiohttp                   3.8.3            py37h540881e_0    conda-forge
 11 | aiosignal                 1.3.1              pyhd8ed1ab_0    conda-forge
 12 | amply                     0.1.5              pyhd8ed1ab_0    conda-forge
 13 | anndata                   0.8.0              pyhd8ed1ab_1    conda-forge
 14 | anndata2ri                1.1                pyhdfd78af_0    bioconda
 15 | anyio                     3.6.2              pyhd8ed1ab_0    conda-forge
 16 | appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge
 17 | argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
 18 | argon2-cffi-bindings      21.2.0           py37h540881e_2    conda-forge
 19 | async-timeout             4.0.2              pyhd8ed1ab_0    conda-forge
 20 | asynctest                 0.13.0                     py_0    conda-forge
 21 | atk-1.0                   2.38.0               hd4edc92_1    conda-forge
 22 | attmap                    0.13.2             pyhd8ed1ab_0    conda-forge
 23 | attrs                     22.2.0             pyh71513ae_0    conda-forge
 24 | backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
 25 | backports                 1.0                pyhd8ed1ab_3    conda-forge
 26 | backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
 27 | backports.zoneinfo        0.2.1            py37h540881e_5    conda-forge
 28 | bbknn                     1.3.9            py37h8f50634_1    bioconda
 29 | bcrypt                    3.2.2            py37h540881e_0    conda-forge
 30 | beautifulsoup4            4.11.1             pyha770c72_0    conda-forge
 31 | binutils_impl_linux-64    2.39                 he00db2b_1    conda-forge
 32 | bioconductor-beachmat     2.6.4             r40h399db7b_1    bioconda
 33 | bioconductor-biobase      2.50.0            r40hd029910_1    bioconda
 34 | bioconductor-biocgenerics 0.36.0            r40hdfd78af_1    bioconda
 35 | bioconductor-biocneighbors 1.8.2             r40h399db7b_0    bioconda
 36 | bioconductor-biocparallel 1.24.1            r40h399db7b_0    bioconda
 37 | bioconductor-biocsingular 1.6.0             r40h399db7b_1    bioconda
 38 | bioconductor-bluster      1.0.0             r40h399db7b_2    bioconda
 39 | bioconductor-delayedarray 0.16.3            r40hd029910_0    bioconda
 40 | bioconductor-delayedmatrixstats 1.12.3            r40hdfd78af_0    bioconda
 41 | bioconductor-edger        3.32.1            r40h399db7b_0    bioconda
 42 | bioconductor-genomeinfodb 1.26.4            r40hdfd78af_0    bioconda
 43 | bioconductor-genomeinfodbdata 1.2.4             r40hdfd78af_2    bioconda
 44 | bioconductor-genomicranges 1.42.0            r40hd029910_1    bioconda
 45 | bioconductor-hdf5array    1.18.1            r40hd029910_0    bioconda
 46 | bioconductor-iranges      2.24.1            r40hd029910_0    bioconda
 47 | bioconductor-limma        3.46.0            r40hd029910_1    bioconda
 48 | bioconductor-matrixgenerics 1.2.1             r40hdfd78af_0    bioconda
 49 | bioconductor-rhdf5        2.34.0            r40h399db7b_1    bioconda
 50 | bioconductor-rhdf5filters 1.2.0             r40h399db7b_1    bioconda
 51 | bioconductor-rhdf5lib     1.12.1            r40hd029910_0    bioconda
 52 | bioconductor-s4vectors    0.28.1            r40hd029910_0    bioconda
 53 | bioconductor-scater       1.18.6            r40hdfd78af_0    bioconda
 54 | bioconductor-scran        1.18.5            r40h399db7b_0    bioconda
 55 | bioconductor-scuttle      1.0.4             r40h399db7b_0    bioconda
 56 | bioconductor-singlecellexperiment 1.12.0            r40hdfd78af_1    bioconda
 57 | bioconductor-sparsematrixstats 1.2.1             r40h399db7b_0    bioconda
 58 | bioconductor-summarizedexperiment 1.20.0            r40hdfd78af_1    bioconda
 59 | bioconductor-xvector      0.30.0            r40hd029910_1    bioconda
 60 | bioconductor-zlibbioc     1.36.0            r40hd029910_1    bioconda
 61 | bleach                    5.0.1              pyhd8ed1ab_0    conda-forge
 62 | blinker                   1.5                pyhd8ed1ab_0    conda-forge
 63 | boto3                     1.26.52            pyhd8ed1ab_0    conda-forge
 64 | botocore                  1.29.52            pyhd8ed1ab_0    conda-forge
 65 | brotli                    1.0.9                h166bdaf_8    conda-forge
 66 | brotli-bin                1.0.9                h166bdaf_8    conda-forge
 67 | brotlipy                  0.7.0           py37h540881e_1004    conda-forge
 68 | bwidget                   1.9.14               ha770c72_1    conda-forge
 69 | bzip2                     1.0.8                h7f98852_4    conda-forge
 70 | c-ares                    1.18.1               h7f98852_0    conda-forge
 71 | ca-certificates           2022.12.7            ha878542_0    conda-forge
 72 | cached-property           1.5.2                hd8ed1ab_1    conda-forge
 73 | cached_property           1.5.2              pyha770c72_1    conda-forge
 74 | cachetools                4.2.4                    pypi_0    pypi
 75 | cairo                     1.16.0            ha61ee94_1014    conda-forge
 76 | certifi                   2022.12.7          pyhd8ed1ab_0    conda-forge
 77 | cffi                      1.15.1           py37h43b0acd_1    conda-forge
 78 | charset-normalizer        2.1.1              pyhd8ed1ab_0    conda-forge
 79 | chex                      0.1.5              pyhd8ed1ab_0    conda-forge
 80 | click                     8.1.3            py37h89c1867_0    conda-forge
 81 | coin-or-cbc               2.10.8               h3786ebc_0    conda-forge
 82 | coin-or-cgl               0.60.6               h6f57e76_2    conda-forge
 83 | coin-or-clp               1.17.7               hc56784d_2    conda-forge
 84 | coin-or-osi               0.108.7              h2720bb7_2    conda-forge
 85 | coin-or-utils             2.11.6               h202d8b1_2    conda-forge
 86 | coincbc                   2.10.8            0_metapackage    conda-forge
 87 | colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
 88 | commonmark                0.9.1                      py_0    conda-forge
 89 | configargparse            1.5.3              pyhd8ed1ab_0    conda-forge
 90 | connection_pool           0.0.3              pyhd3deb0d_0    conda-forge
 91 | cryptography              38.0.2           py37h5994e8b_1    conda-forge
 92 | curl                      7.86.0               h2283fc2_1    conda-forge
 93 | cycler                    0.11.0             pyhd8ed1ab_0    conda-forge
 94 | cython                    0.29.33                  pypi_0    pypi
 95 | dataclasses               0.8                pyhc8e2a94_3    conda-forge
 96 | datrie                    0.8.2            py37h540881e_4    conda-forge
 97 | debugpy                   1.6.3            py37hd23a5d3_0    conda-forge
 98 | decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
 99 | defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
100 | deprecated                1.2.13                   pypi_0    pypi
101 | dm-tree                   0.1.7            py37hfb7772e_0    conda-forge
102 | docrep                    0.3.2              pyh44b312d_0    conda-forge
103 | docutils                  0.19             py37h89c1867_0    conda-forge
104 | dpath                     2.0.6            py37h89c1867_1    conda-forge
105 | dropbox                   11.36.0            pyhd8ed1ab_0    conda-forge
106 | dunamai                   1.15.0             pyhd8ed1ab_0    conda-forge
107 | entrypoints               0.4                pyhd8ed1ab_0    conda-forge
108 | et_xmlfile                1.0.1                   py_1001    conda-forge
109 | exceptiongroup            1.1.0              pyhd8ed1ab_0    conda-forge
110 | expat                     2.5.0                h27087fc_0    conda-forge
111 | fbpca                     1.0                        py_0    conda-forge
112 | filechunkio               1.8                        py_2    conda-forge
113 | filelock                  3.9.0              pyhd8ed1ab_0    conda-forge
114 | flax                      0.5.0              pyhd8ed1ab_0    conda-forge
115 | flit-core                 3.8.0              pyhd8ed1ab_0    conda-forge
116 | font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    conda-forge
117 | font-ttf-inconsolata      3.000                h77eed37_0    conda-forge
118 | font-ttf-source-code-pro  2.038                h77eed37_0    conda-forge
119 | font-ttf-ubuntu           0.83                 hab24e00_0    conda-forge
120 | fontconfig                2.14.1               hc2a2eb6_0    conda-forge
121 | fonts-conda-ecosystem     1                             0    conda-forge
122 | fonts-conda-forge         1                             0    conda-forge
123 | fonttools                 4.38.0           py37h540881e_0    conda-forge
124 | freetype                  2.12.1               hca18f0e_1    conda-forge
125 | fribidi                   1.0.10               h36c2ea0_0    conda-forge
126 | frozenlist                1.3.1            py37h540881e_0    conda-forge
127 | fsspec                    2022.11.0          pyhd8ed1ab_0    conda-forge
128 | ftputil                   5.0.4              pyhd8ed1ab_0    conda-forge
129 | future                    0.18.2           py37h89c1867_5    conda-forge
130 | future-fstrings           1.2.0                    pypi_0    pypi
131 | gcc                       12.2.0              h26027b1_11    conda-forge
132 | gcc_impl_linux-64         12.2.0              hcc96c02_19    conda-forge
133 | gdk-pixbuf                2.42.8               hff1cb4f_1    conda-forge
134 | geos                      3.11.0               h27087fc_0    conda-forge
135 | geosketch                 1.2                        py_0    bioconda
136 | get_version               3.5.4              pyhd8ed1ab_0    conda-forge
137 | gettext                   0.21.1               h27087fc_0    conda-forge
138 | gfortran_impl_linux-64    12.2.0              h55be85b_19    conda-forge
139 | giflib                    5.2.1                h36c2ea0_2    conda-forge
140 | git                       2.39.1          pl5321h693f4a3_0    conda-forge
141 | gitdb                     4.0.10             pyhd8ed1ab_0    conda-forge
142 | gitpython                 3.1.30             pyhd8ed1ab_0    conda-forge
143 | glpk                      5.0                  h445213a_0    conda-forge
144 | gmp                       6.2.1                h58526e2_0    conda-forge
145 | google-api-core           2.11.0             pyhd8ed1ab_0    conda-forge
146 | google-api-python-client  2.73.0             pyhd8ed1ab_0    conda-forge
147 | google-auth               1.35.0                   pypi_0    pypi
148 | google-auth-httplib2      0.1.0              pyhd8ed1ab_1    conda-forge
149 | google-auth-oauthlib      0.4.1                      py_2    conda-forge
150 | google-cloud-core         2.3.2              pyhd8ed1ab_0    conda-forge
151 | google-cloud-storage      2.7.0              pyh1a96a4e_0    conda-forge
152 | google-crc32c             1.1.2            py37h5d4fa31_3    conda-forge
153 | google-resumable-media    2.4.0              pyhd8ed1ab_0    conda-forge
154 | googleapis-common-protos  1.57.1             pyhd8ed1ab_0    conda-forge
155 | graphite2                 1.3.13            h58526e2_1001    conda-forge
156 | graphviz                  7.0.5                h2e5815a_0    conda-forge
157 | grpc-cpp                  1.47.1               h30feacc_7    conda-forge
158 | grpcio                    1.46.3           py37h0327239_0    conda-forge
159 | gsl                       2.7                  he838d99_0    conda-forge
160 | gtk2                      2.24.33              h90689f9_2    conda-forge
161 | gts                       0.7.6                h64030ff_2    conda-forge
162 | gxx                       12.2.0              h26027b1_11    conda-forge
163 | gxx_impl_linux-64         12.2.0              hcc96c02_19    conda-forge
164 | h5py                      3.7.0           nompi_py37hf1ce037_101    conda-forge
165 | harfbuzz                  6.0.0                h8e241bc_0    conda-forge
166 | hdf5                      1.12.2          nompi_h4df4325_100    conda-forge
167 | httplib2                  0.21.0             pyhd8ed1ab_0    conda-forge
168 | humanfriendly             10.0             py37h89c1867_2    conda-forge
169 | icu                       70.1                 h27087fc_0    conda-forge
170 | idna                      3.4                pyhd8ed1ab_0    conda-forge
171 | igraph                    0.10.3                   pypi_0    pypi
172 | imageio                   2.25.1                   pypi_0    pypi
173 | importlib-metadata        1.7.0                    pypi_0    pypi
174 | importlib_metadata        4.11.4               hd8ed1ab_0    conda-forge
175 | importlib_resources       5.10.2             pyhd8ed1ab_0    conda-forge
176 | iniconfig                 2.0.0              pyhd8ed1ab_0    conda-forge
177 | intervaltree              2.1.0                      py_1    conda-forge
178 | ipykernel                 6.16.2             pyh210e3f2_0    conda-forge
179 | ipython                   7.33.0           py37h89c1867_0    conda-forge
180 | ipython_genutils          0.2.0                      py_1    conda-forge
181 | ipywidgets                8.0.4              pyhd8ed1ab_0    conda-forge
182 | jax                       0.3.25             pyhd8ed1ab_0    conda-forge
183 | jaxlib                    0.3.22          cpu_py37hb467de3_0    conda-forge
184 | jedi                      0.18.2             pyhd8ed1ab_0    conda-forge
185 | jinja2                    3.1.2              pyhd8ed1ab_1    conda-forge
186 | jmespath                  1.0.1              pyhd8ed1ab_0    conda-forge
187 | joblib                    1.2.0              pyhd8ed1ab_0    conda-forge
188 | jpeg                      9e                   h166bdaf_2    conda-forge
189 | jsonschema                4.17.3             pyhd8ed1ab_0    conda-forge
190 | jupyter_client            7.4.9              pyhd8ed1ab_0    conda-forge
191 | jupyter_core              4.11.1           py37h89c1867_0    conda-forge
192 | jupyter_server            1.23.4             pyhd8ed1ab_0    conda-forge
193 | jupyterlab_pygments       0.2.2              pyhd8ed1ab_0    conda-forge
194 | jupyterlab_widgets        3.0.5              pyhd8ed1ab_0    conda-forge
195 | kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
196 | keyutils                  1.6.1                h166bdaf_0    conda-forge
197 | kiwisolver                1.4.4            py37h7cecad7_0    conda-forge
198 | krb5                      1.19.3               h08a2579_0    conda-forge
199 | lcms2                     2.14                 h6ed2654_0    conda-forge
200 | ld_impl_linux-64          2.39                 hcc3a1bd_1    conda-forge
201 | leidenalg                 0.9.1                    pypi_0    pypi
202 | lerc                      4.0.0                h27087fc_0    conda-forge
203 | libabseil                 20220623.0      cxx17_h05df665_6    conda-forge
204 | libblas                   3.9.0           16_linux64_openblas    conda-forge
205 | libbrotlicommon           1.0.9                h166bdaf_8    conda-forge
206 | libbrotlidec              1.0.9                h166bdaf_8    conda-forge
207 | libbrotlienc              1.0.9                h166bdaf_8    conda-forge
208 | libcblas                  3.9.0           16_linux64_openblas    conda-forge
209 | libcrc32c                 1.1.2                h9c3ff4c_0    conda-forge
210 | libcurl                   7.86.0               h2283fc2_1    conda-forge
211 | libdeflate                1.14                 h166bdaf_0    conda-forge
212 | libedit                   3.1.20191231         he28a2e2_2    conda-forge
213 | libev                     4.33                 h516909a_1    conda-forge
214 | libffi                    3.4.2                h7f98852_5    conda-forge
215 | libgcc-devel_linux-64     12.2.0              h3b97bd3_19    conda-forge
216 | libgcc-ng                 12.2.0              h65d4601_19    conda-forge
217 | libgd                     2.3.3                h18fbbfe_3    conda-forge
218 | libgfortran-ng            12.2.0              h69a702a_19    conda-forge
219 | libgfortran5              12.2.0              h337968e_19    conda-forge
220 | libgit2                   1.5.0                hdb3ecda_1    conda-forge
221 | libglib                   2.74.1               h606061b_1    conda-forge
222 | libgomp                   12.2.0              h65d4601_19    conda-forge
223 | libhwloc                  2.8.0                h32351e8_1    conda-forge
224 | libiconv                  1.17                 h166bdaf_0    conda-forge
225 | liblapack                 3.9.0           16_linux64_openblas    conda-forge
226 | liblapacke                3.9.0           16_linux64_openblas    conda-forge
227 | libllvm11                 11.1.0               he0ac6c6_5    conda-forge
228 | libnghttp2                1.51.0               hff17c54_0    conda-forge
229 | libnsl                    2.0.0                h7f98852_0    conda-forge
230 | libopenblas               0.3.21          pthreads_h78a6416_3    conda-forge
231 | libpng                    1.6.39               h753d276_0    conda-forge
232 | libprotobuf               3.21.8               h6239696_0    conda-forge
233 | librsvg                   2.54.4               h7abd40a_0    conda-forge
234 | libsanitizer              12.2.0              h46fd767_19    conda-forge
235 | libsodium                 1.0.18               h36c2ea0_1    conda-forge
236 | libsqlite                 3.40.0               h753d276_0    conda-forge
237 | libssh2                   1.10.0               hf14f497_3    conda-forge
238 | libstdcxx-devel_linux-64  12.2.0              h3b97bd3_19    conda-forge
239 | libstdcxx-ng              12.2.0              h46fd767_19    conda-forge
240 | libtiff                   4.4.0                h82bc61c_5    conda-forge
241 | libtool                   2.4.7                h27087fc_0    conda-forge
242 | libuuid                   2.32.1            h7f98852_1000    conda-forge
243 | libwebp                   1.2.4                h522a892_0    conda-forge
244 | libwebp-base              1.2.4                h166bdaf_0    conda-forge
245 | libxcb                    1.13              h7f98852_1004    conda-forge
246 | libxml2                   2.10.3               h7463322_0    conda-forge
247 | libzlib                   1.2.13               h166bdaf_4    conda-forge
248 | llvm-openmp               15.0.7               h0cdce71_0    conda-forge
249 | llvmlite                  0.39.1           py37h0761922_0    conda-forge
250 | logmuse                   0.2.6              pyh8c360ce_0    conda-forge
251 | make                      4.3                  hd18ef5c_1    conda-forge
252 | markdown                  3.3.4                    pypi_0    pypi
253 | markupsafe                2.1.1            py37h540881e_1    conda-forge
254 | matplotlib-base           3.5.3            py37hf395dca_2    conda-forge
255 | matplotlib-inline         0.1.6              pyhd8ed1ab_0    conda-forge
256 | matplotlib-venn           0.11.9                   pypi_0    pypi
257 | mistune                   2.0.4              pyhd8ed1ab_0    conda-forge
258 | mkl                       2022.2.1         h84fe81f_16997    conda-forge
259 | msgpack-python            1.0.4            py37h7cecad7_0    conda-forge
260 | multidict                 6.0.2            py37h540881e_1    conda-forge
261 | multipledispatch          0.6.0                      py_0    conda-forge
262 | munkres                   1.1.4              pyh9f0ad1d_0    conda-forge
263 | natsort                   8.2.0              pyhd8ed1ab_0    conda-forge
264 | nbclassic                 0.4.8              pyhd8ed1ab_0    conda-forge
265 | nbclient                  0.7.0              pyhd8ed1ab_0    conda-forge
266 | nbconvert                 7.2.8              pyhd8ed1ab_0    conda-forge
267 | nbconvert-core            7.2.8              pyhd8ed1ab_0    conda-forge
268 | nbconvert-pandoc          7.2.8              pyhd8ed1ab_0    conda-forge
269 | nbformat                  5.7.3              pyhd8ed1ab_0    conda-forge
270 | ncurses                   6.3                  h27087fc_1    conda-forge
271 | nest-asyncio              1.5.6              pyhd8ed1ab_0    conda-forge
272 | networkx                  2.7                pyhd8ed1ab_0    conda-forge
273 | nimfa                     1.4.0                    pypi_0    pypi
274 | ninja                     1.11.0               h924138e_0    conda-forge
275 | notebook                  6.5.2              pyha770c72_1    conda-forge
276 | notebook-shim             0.2.2              pyhd8ed1ab_0    conda-forge
277 | numba                     0.56.3           py37hf081915_0    conda-forge
278 | numpy                     1.21.6           py37h976b520_0    conda-forge
279 | numpyro                   0.10.1             pyhd8ed1ab_0    conda-forge
280 | nvidia-cublas-cu11        11.10.3.66               pypi_0    pypi
281 | nvidia-cuda-nvrtc-cu11    11.7.99                  pypi_0    pypi
282 | nvidia-cuda-runtime-cu11  11.7.99                  pypi_0    pypi
283 | nvidia-cudnn-cu11         8.5.0.96                 pypi_0    pypi
284 | oauth2client              4.1.3                      py_0    conda-forge
285 | oauthlib                  3.2.2              pyhd8ed1ab_0    conda-forge
286 | openblas                  0.3.21          pthreads_h320a7e8_3    conda-forge
287 | openjpeg                  2.5.0                h7d73246_1    conda-forge
288 | openpyxl                  3.0.10           py37h540881e_1    conda-forge
289 | openssl                   3.0.7                h0b41bf4_1    conda-forge
290 | opt_einsum                3.3.0              pyhd8ed1ab_1    conda-forge
291 | optax                     0.1.4              pyhd8ed1ab_0    conda-forge
292 | packaging                 23.0               pyhd8ed1ab_0    conda-forge
293 | pandas                    1.3.5            py37he8f5f7f_0    conda-forge
294 | pandoc                    2.19.2               h32600fe_1    conda-forge
295 | pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge
296 | pango                     1.50.12              hd33c08f_1    conda-forge
297 | paramiko                  2.12.0             pyhd8ed1ab_0    conda-forge
298 | parso                     0.8.3              pyhd8ed1ab_0    conda-forge
299 | patsy                     0.5.3                    pypi_0    pypi
300 | pcre2                     10.40                hc3806b6_0    conda-forge
301 | peppy                     0.35.3             pyhd8ed1ab_0    conda-forge
302 | perl                      5.32.1          2_h7f98852_perl5    conda-forge
303 | pexpect                   4.8.0              pyh1a96a4e_2    conda-forge
304 | pickleshare               0.7.5                   py_1003    conda-forge
305 | pillow                    9.2.0            py37h850a105_2    conda-forge
306 | pip                       22.3.1             pyhd8ed1ab_0    conda-forge
307 | pixman                    0.40.0               h36c2ea0_0    conda-forge
308 | pkgutil-resolve-name      1.3.10             pyhd8ed1ab_0    conda-forge
309 | plac                      1.3.5              pyhd8ed1ab_0    conda-forge
310 | pluggy                    1.0.0            py37h89c1867_3    conda-forge
311 | ply                       3.11                       py_1    conda-forge
312 | prettytable               3.6.0              pyhd8ed1ab_0    conda-forge
313 | prometheus_client         0.15.0             pyhd8ed1ab_0    conda-forge
314 | prompt-toolkit            3.0.36             pyha770c72_0    conda-forge
315 | protobuf                  4.21.8           py37hd23a5d3_0    conda-forge
316 | psutil                    5.9.3            py37h540881e_0    conda-forge
317 | pthread-stubs             0.4               h36c2ea0_1001    conda-forge
318 | ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge
319 | pulp                      2.6.0            py37h89c1867_1    conda-forge
320 | pyasn1                    0.4.8                      py_0    conda-forge
321 | pyasn1-modules            0.2.7                      py_0    conda-forge
322 | pycparser                 2.21               pyhd8ed1ab_0    conda-forge
323 | pydeprecate               0.3.1              pyhd8ed1ab_0    conda-forge
324 | pydot                     1.4.2                    pypi_0    pypi
325 | pygments                  2.14.0             pyhd8ed1ab_0    conda-forge
326 | pyjwt                     2.6.0              pyhd8ed1ab_0    conda-forge
327 | pynacl                    1.5.0            py37h540881e_1    conda-forge
328 | pynndescent               0.5.7                    pypi_0    pypi
329 | pyopenssl                 23.0.0             pyhd8ed1ab_0    conda-forge
330 | pyparsing                 3.0.9              pyhd8ed1ab_0    conda-forge
331 | pyro-api                  0.1.2              pyhd8ed1ab_0    conda-forge
332 | pyro-ppl                  1.8.4              pyhd8ed1ab_0    conda-forge
333 | pyrsistent                0.18.1           py37h540881e_1    conda-forge
334 | pysftp                    0.2.9                      py_1    conda-forge
335 | pysocks                   1.7.1            py37h89c1867_5    conda-forge
336 | pytest                    7.2.0            py37h89c1867_0    conda-forge
337 | python                    3.7.12          hf930737_100_cpython    conda-forge
338 | python-annoy              1.17.0           py37hd23a5d3_4    conda-forge
339 | python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
340 | python-fastjsonschema     2.16.2             pyhd8ed1ab_0    conda-forge
341 | python-irodsclient        1.1.6              pyhd8ed1ab_0    conda-forge
342 | python-tzdata             2022.7             pyhd8ed1ab_0    conda-forge
343 | python_abi                3.7                     3_cp37m    conda-forge
344 | pytorch-lightning         1.5.8              pyhd8ed1ab_0    conda-forge
345 | pytz                      2022.7.1           pyhd8ed1ab_0    conda-forge
346 | pytz-deprecation-shim     0.1.0.post0      py37h89c1867_2    conda-forge
347 | pyu2f                     0.1.5              pyhd8ed1ab_0    conda-forge
348 | pywavelets                1.3.0                    pypi_0    pypi
349 | pyyaml                    6.0              py37h540881e_4    conda-forge
350 | pyzmq                     24.0.1           py37h0c0c2a8_0    conda-forge
351 | r-abind                   1.4_5           r40hc72bb7e_1003    conda-forge
352 | r-askpass                 1.1               r40hcfec24a_2    conda-forge
353 | r-assertthat              0.2.1             r40hc72bb7e_2    conda-forge
354 | r-backports               1.4.1             r40hcfec24a_0    conda-forge
355 | r-base                    4.0.5                hb87df5d_8    conda-forge
356 | r-base64enc               0.1_3           r40hcfec24a_1004    conda-forge
357 | r-beeswarm                0.4.0             r40hcfec24a_1    conda-forge
358 | r-bh                      1.78.0_0          r40hc72bb7e_0    conda-forge
359 | r-bit                     4.0.4             r40hcfec24a_0    conda-forge
360 | r-bit64                   4.0.5             r40hcfec24a_0    conda-forge
361 | r-bitops                  1.0_7             r40h06615bd_0    conda-forge
362 | r-blob                    1.2.3             r40hc72bb7e_0    conda-forge
363 | r-boot                    1.3_28            r40hc72bb7e_0    conda-forge
364 | r-brew                    1.0_7             r40hc72bb7e_0    conda-forge
365 | r-brio                    1.1.3             r40hcfec24a_0    conda-forge
366 | r-broom                   1.0.1             r40hc72bb7e_0    conda-forge
367 | r-bslib                   0.4.0             r40hc72bb7e_0    conda-forge
368 | r-cachem                  1.0.6             r40hcfec24a_0    conda-forge
369 | r-callr                   3.7.2             r40hc72bb7e_0    conda-forge
370 | r-caret                   6.0_93            r40h06615bd_0    conda-forge
371 | r-catools                 1.18.2            r40h7525677_0    conda-forge
372 | r-cellranger              1.1.0           r40hc72bb7e_1004    conda-forge
373 | r-class                   7.3_20            r40hcfec24a_0    conda-forge
374 | r-cli                     3.4.1             r40h7525677_0    conda-forge
375 | r-clipr                   0.8.0             r40hc72bb7e_0    conda-forge
376 | r-cluster                 2.1.3             r40h8da6f51_0    conda-forge
377 | r-codetools               0.2_18            r40hc72bb7e_0    conda-forge
378 | r-colorspace              2.0_3             r40h06615bd_0    conda-forge
379 | r-commonmark              1.8.0             r40h06615bd_0    conda-forge
380 | r-cowplot                 1.1.1             r40hc72bb7e_0    conda-forge
381 | r-cpp11                   0.4.2             r40hc72bb7e_0    conda-forge
382 | r-crayon                  1.5.1             r40hc72bb7e_0    conda-forge
383 | r-credentials             1.3.2             r40hc72bb7e_0    conda-forge
384 | r-crosstalk               1.2.0             r40hc72bb7e_0    conda-forge
385 | r-crul                    1.3               r40h785f33e_0    conda-forge
386 | r-curl                    4.3.2             r40hcfec24a_0    conda-forge
387 | r-data.table              1.14.2            r40hcfec24a_0    conda-forge
388 | r-dbi                     1.1.3             r40hc72bb7e_0    conda-forge
389 | r-dbplyr                  2.2.1             r40hc72bb7e_0    conda-forge
390 | r-deldir                  1.0_6             r40h8da6f51_0    conda-forge
391 | r-desc                    1.4.2             r40hc72bb7e_0    conda-forge
392 | r-devtools                2.4.4             r40hc72bb7e_0    conda-forge
393 | r-diffobj                 0.3.5             r40hcfec24a_0    conda-forge
394 | r-digest                  0.6.29            r40h03ef668_0    conda-forge
395 | r-downlit                 0.4.2             r40hc72bb7e_0    conda-forge
396 | r-dplyr                   1.0.10            r40h7525677_0    conda-forge
397 | r-dqrng                   0.3.0             r40h7525677_0    conda-forge
398 | r-dtplyr                  1.2.2             r40hc72bb7e_0    conda-forge
399 | r-e1071                   1.7_11            r40h7525677_0    conda-forge
400 | r-ellipsis                0.3.2             r40hcfec24a_0    conda-forge
401 | r-essentials              4.0             r40hd8ed1ab_2002    conda-forge
402 | r-evaluate                0.16              r40hc72bb7e_0    conda-forge
403 | r-fansi                   1.0.3             r40h06615bd_0    conda-forge
404 | r-farver                  2.1.1             r40h7525677_0    conda-forge
405 | r-fastmap                 1.1.0             r40h03ef668_0    conda-forge
406 | r-fitdistrplus            1.1_8             r40hc72bb7e_0    conda-forge
407 | r-fnn                     1.1.3.1           r40h7525677_0    conda-forge
408 | r-fontawesome             0.3.0             r40hc72bb7e_0    conda-forge
409 | r-forcats                 0.5.2             r40hc72bb7e_0    conda-forge
410 | r-foreach                 1.5.2             r40hc72bb7e_0    conda-forge
411 | r-foreign                 0.8_82            r40hcfec24a_0    conda-forge
412 | r-formatr                 1.12              r40hc72bb7e_0    conda-forge
413 | r-fs                      1.5.2             r40h7525677_1    conda-forge
414 | r-futile.logger           1.4.3           r40hc72bb7e_1003    conda-forge
415 | r-futile.options          1.0.1           r40hc72bb7e_1002    conda-forge
416 | r-future                  1.28.0            r40hc72bb7e_0    conda-forge
417 | r-future.apply            1.9.1             r40hc72bb7e_0    conda-forge
418 | r-gargle                  1.2.1             r40hc72bb7e_0    conda-forge
419 | r-generics                0.1.3             r40hc72bb7e_0    conda-forge
420 | r-gert                    1.5.0             r40h163148b_2    conda-forge
421 | r-ggbeeswarm              0.6.0           r40ha770c72_1003    conda-forge
422 | r-ggplot2                 3.3.6             r40hc72bb7e_0    conda-forge
423 | r-ggrepel                 0.9.1             r40h03ef668_0    conda-forge
424 | r-ggridges                0.5.4             r40hc72bb7e_0    conda-forge
425 | r-gh                      1.3.1             r40hc72bb7e_0    conda-forge
426 | r-gistr                   0.9.0             r40hc72bb7e_0    conda-forge
427 | r-gitcreds                0.1.2             r40hc72bb7e_0    conda-forge
428 | r-glmnet                  4.1_2             r40h8da6f51_0    conda-forge
429 | r-globals                 0.16.1            r40hc72bb7e_0    conda-forge
430 | r-glue                    1.6.2             r40h06615bd_0    conda-forge
431 | r-goftest                 1.2_3             r40h06615bd_0    conda-forge
432 | r-googledrive             2.0.0             r40hc72bb7e_0    conda-forge
433 | r-googlesheets4           1.0.1             r40h785f33e_0    conda-forge
434 | r-gower                   1.0.0             r40hcfec24a_0    conda-forge
435 | r-gplots                  3.1.3             r40hc72bb7e_0    conda-forge
436 | r-gridextra               2.3             r40hc72bb7e_1003    conda-forge
437 | r-gtable                  0.3.1             r40hc72bb7e_0    conda-forge
438 | r-gtools                  3.9.3             r40h06615bd_0    conda-forge
439 | r-hardhat                 1.2.0             r40hc72bb7e_0    conda-forge
440 | r-haven                   2.5.0             r40h7525677_0    conda-forge
441 | r-here                    1.0.1             r40hc72bb7e_0    conda-forge
442 | r-hexbin                  1.28.2            r40h8da6f51_0    conda-forge
443 | r-highr                   0.9               r40hc72bb7e_0    conda-forge
444 | r-hms                     1.1.2             r40hc72bb7e_0    conda-forge
445 | r-htmltools               0.5.3             r40h7525677_0    conda-forge
446 | r-htmlwidgets             1.5.4             r40hc72bb7e_0    conda-forge
447 | r-httpcode                0.3.0             r40ha770c72_1    conda-forge
448 | r-httpuv                  1.6.6             r40h7525677_0    conda-forge
449 | r-httr                    1.4.4             r40hc72bb7e_0    conda-forge
450 | r-ica                     1.0_3             r40hc72bb7e_0    conda-forge
451 | r-ids                     1.0.1             r40hc72bb7e_1    conda-forge
452 | r-igraph                  1.3.4             r40hb34fc8a_0    conda-forge
453 | r-ini                     0.3.1           r40hc72bb7e_1003    conda-forge
454 | r-ipred                   0.9_13            r40h06615bd_0    conda-forge
455 | r-irdisplay               1.1               r40hd8ed1ab_0    conda-forge
456 | r-irkernel                1.3               r40hc72bb7e_0    conda-forge
457 | r-irlba                   2.3.5             r40h5f7b363_0    conda-forge
458 | r-isoband                 0.2.5             r40h03ef668_0    conda-forge
459 | r-iterators               1.0.14            r40hc72bb7e_0    conda-forge
460 | r-jquerylib               0.1.4             r40hc72bb7e_0    conda-forge
461 | r-jsonlite                1.8.0             r40h06615bd_0    conda-forge
462 | r-kernsmooth              2.23_20           r40h742201e_0    conda-forge
463 | r-knitr                   1.40              r40hc72bb7e_0    conda-forge
464 | r-labeling                0.4.2             r40hc72bb7e_1    conda-forge
465 | r-lambda.r                1.2.4             r40hc72bb7e_1    conda-forge
466 | r-later                   1.2.0             r40h03ef668_0    conda-forge
467 | r-lattice                 0.20_45           r40hcfec24a_0    conda-forge
468 | r-lava                    1.6.10            r40hc72bb7e_0    conda-forge
469 | r-lazyeval                0.2.2             r40hcfec24a_2    conda-forge
470 | r-leiden                  0.4.3             r40hc72bb7e_0    conda-forge
471 | r-lifecycle               1.0.2             r40hc72bb7e_0    conda-forge
472 | r-listenv                 0.8.0             r40hc72bb7e_1    conda-forge
473 | r-lmtest                  0.9_40            r40h8da6f51_0    conda-forge
474 | r-lobstr                  1.1.2             r40h7525677_0    conda-forge
475 | r-locfit                  1.5_9.4           r40hcfec24a_1    conda-forge
476 | r-lsei                    1.3_0             r40hc3ea6d6_1    conda-forge
477 | r-lubridate               1.8.0             r40h03ef668_0    conda-forge
478 | r-magrittr                2.0.3             r40h06615bd_0    conda-forge
479 | r-maps                    3.4.0             r40hcfec24a_0    conda-forge
480 | r-mass                    7.3_58.1          r40h06615bd_0    conda-forge
481 | r-matrix                  1.4_1             r40h0154571_0    conda-forge
482 | r-matrixstats             0.62.0            r40h06615bd_0    conda-forge
483 | r-memoise                 2.0.1             r40hc72bb7e_0    conda-forge
484 | r-mgcv                    1.8_40            r40h0154571_0    conda-forge
485 | r-mime                    0.12              r40hcfec24a_0    conda-forge
486 | r-miniui                  0.1.1.1         r40hc72bb7e_1002    conda-forge
487 | r-modelmetrics            1.2.2.2           r40h03ef668_1    conda-forge
488 | r-modelr                  0.1.9             r40hc72bb7e_0    conda-forge
489 | r-munsell                 0.5.0           r40hc72bb7e_1004    conda-forge
490 | r-nlme                    3.1_159           r40h8da6f51_0    conda-forge
491 | r-nnet                    7.3_17            r40hcfec24a_0    conda-forge
492 | r-npsurv                  0.5_0             r40hc72bb7e_0    conda-forge
493 | r-numderiv                2016.8_1.1        r40hc72bb7e_3    conda-forge
494 | r-openssl                 2.0.3             r40h1f3e0c5_0    conda-forge
495 | r-parallelly              1.32.1            r40hc72bb7e_0    conda-forge
496 | r-patchwork               1.1.2             r40hc72bb7e_0    conda-forge
497 | r-pbapply                 1.5_0             r40hc72bb7e_0    conda-forge
498 | r-pbdzmq                  0.3_7             r40h42bf92c_0    conda-forge
499 | r-pillar                  1.8.1             r40hc72bb7e_0    conda-forge
500 | r-pkgbuild                1.3.1             r40hc72bb7e_0    conda-forge
501 | r-pkgconfig               2.0.3             r40hc72bb7e_1    conda-forge
502 | r-pkgdown                 2.0.6             r40hc72bb7e_0    conda-forge
503 | r-pkgload                 1.3.0             r40hc72bb7e_0    conda-forge
504 | r-plotly                  4.10.0            r40hc72bb7e_0    conda-forge
505 | r-plyr                    1.8.7             r40h7525677_0    conda-forge
506 | r-png                     0.1_7           r40hcfec24a_1004    conda-forge
507 | r-polyclip                1.10_0            r40h7525677_2    conda-forge
508 | r-praise                  1.0.0           r40hc72bb7e_1005    conda-forge
509 | r-prettyunits             1.1.1             r40hc72bb7e_1    conda-forge
510 | r-proc                    1.18.0            r40h03ef668_0    conda-forge
511 | r-processx                3.7.0             r40h06615bd_0    conda-forge
512 | r-prodlim                 2019.11.13        r40h03ef668_1    conda-forge
513 | r-profvis                 0.3.7             r40hcfec24a_0    conda-forge
514 | r-progress                1.2.2             r40hc72bb7e_2    conda-forge
515 | r-progressr               0.11.0            r40hc72bb7e_0    conda-forge
516 | r-promises                1.2.0.1           r40h03ef668_0    conda-forge
517 | r-proxy                   0.4_27            r40h06615bd_0    conda-forge
518 | r-pryr                    0.1.5             r40h7525677_0    conda-forge
519 | r-ps                      1.7.1             r40h06615bd_0    conda-forge
520 | r-purrr                   0.3.4             r40hcfec24a_1    conda-forge
521 | r-quantmod                0.4.20            r40hc72bb7e_0    conda-forge
522 | r-r6                      2.5.1             r40hc72bb7e_0    conda-forge
523 | r-ragg                    0.3.1             r40h71971c5_0    conda-forge
524 | r-randomforest            4.6_14          r40h8da6f51_1004    conda-forge
525 | r-rann                    2.6.1             r40h7525677_2    conda-forge
526 | r-rappdirs                0.3.3             r40hcfec24a_0    conda-forge
527 | r-rbokeh                  0.5.2             r40hc72bb7e_1    conda-forge
528 | r-rcmdcheck               1.4.0             r40h785f33e_0    conda-forge
529 | r-rcolorbrewer            1.1_3             r40h785f33e_0    conda-forge
530 | r-rcpp                    1.0.9             r40h7525677_1    conda-forge
531 | r-rcppannoy               0.0.19            r40h7525677_0    conda-forge
532 | r-rcpparmadillo           0.11.2.3.1        r40h9f5de39_0    conda-forge
533 | r-rcppeigen               0.3.3.9.2         r40h43535f1_0    conda-forge
534 | r-rcpphnsw                0.4.1             r40h7525677_0    conda-forge
535 | r-rcppparallel            5.1.5             r40h7525677_0    conda-forge
536 | r-rcppprogress            0.4.2             r40hc72bb7e_1    conda-forge
537 | r-rcpptoml                0.1.7             r40h03ef668_1    conda-forge
538 | r-rcurl                   1.98_1.8          r40h06615bd_0    conda-forge
539 | r-readr                   2.1.2             r40h03ef668_0    conda-forge
540 | r-readxl                  1.4.1             r40hf23e330_0    conda-forge
541 | r-recipes                 1.0.1             r40hc72bb7e_0    conda-forge
542 | r-recommended             4.0             r40hd8ed1ab_1004    conda-forge
543 | r-rematch                 1.0.1           r40hc72bb7e_1004    conda-forge
544 | r-rematch2                2.1.2             r40hc72bb7e_1    conda-forge
545 | r-remotes                 2.4.2             r40hc72bb7e_0    conda-forge
546 | r-repr                    1.1.4             r40h785f33e_0    conda-forge
547 | r-reprex                  2.0.2             r40hc72bb7e_0    conda-forge
548 | r-reshape2                1.4.4             r40h03ef668_1    conda-forge
549 | r-reticulate              1.27              r40h38f115c_0    conda-forge
550 | r-rgeos                   0.5_9             r40hf730bdb_3    conda-forge
551 | r-rlang                   1.0.6             r40h7525677_0    conda-forge
552 | r-rmarkdown               2.16              r40hc72bb7e_0    conda-forge
553 | r-rocr                    1.0_11            r40hc72bb7e_1    conda-forge
554 | r-roxygen2                7.2.1             r40h7525677_0    conda-forge
555 | r-rpart                   4.1.16            r40hcfec24a_0    conda-forge
556 | r-rprojroot               2.0.3             r40hc72bb7e_0    conda-forge
557 | r-rspectra                0.16_1            r40h9f5de39_0    conda-forge
558 | r-rstudioapi              0.14              r40hc72bb7e_0    conda-forge
559 | r-rsvd                    1.0.5             r40hc72bb7e_0    conda-forge
560 | r-rtsne                   0.16              r40h37cf8d7_0    conda-forge
561 | r-rversions               2.1.2             r40hc72bb7e_0    conda-forge
562 | r-rvest                   1.0.3             r40hc72bb7e_0    conda-forge
563 | r-sass                    0.4.2             r40h7525677_0    conda-forge
564 | r-scales                  1.2.1             r40hc72bb7e_0    conda-forge
565 | r-scattermore             0.8               r40h06615bd_0    conda-forge
566 | r-sctransform             0.3.4             r40hef8c1a7_0    conda-forge
567 | r-selectr                 0.4_2             r40hc72bb7e_1    conda-forge
568 | r-sessioninfo             1.2.2             r40hc72bb7e_0    conda-forge
569 | r-seurat                  3.2.3             r40h03ef668_0    conda-forge
570 | r-seuratobject            4.1.1             r40h7525677_0    conda-forge
571 | r-shape                   1.4.6             r40ha770c72_0    conda-forge
572 | r-shiny                   1.7.2             r40h785f33e_0    conda-forge
573 | r-sitmo                   2.0.2             r40h7525677_0    conda-forge
574 | r-snow                    0.4_4             r40hc72bb7e_0    conda-forge
575 | r-sourcetools             0.1.7           r40h03ef668_1002    conda-forge
576 | r-sp                      1.5_0             r40h06615bd_0    conda-forge
577 | r-spatial                 7.3_15            r40hcfec24a_0    conda-forge
578 | r-spatstat                1.64_1            r40h0357c0b_0    conda-forge
579 | r-spatstat.data           2.2_0             r40hc72bb7e_0    conda-forge
580 | r-spatstat.utils          2.3_1             r40h06615bd_0    conda-forge
581 | r-squarem                 2021.1            r40hc72bb7e_0    conda-forge
582 | r-statmod                 1.4.37            r40hc3ea6d6_0    conda-forge
583 | r-stringi                 1.7.8             r40h30a9eb7_0    conda-forge
584 | r-stringr                 1.4.1             r40hc72bb7e_0    conda-forge
585 | r-survival                3.4_0             r40h06615bd_0    conda-forge
586 | r-sys                     3.4               r40hcfec24a_0    conda-forge
587 | r-systemfonts             1.0.4             r40hef9c87a_0    conda-forge
588 | r-tensor                  1.5             r40hc72bb7e_1003    conda-forge
589 | r-testthat                3.1.4             r40h7525677_0    conda-forge
590 | r-tibble                  3.1.8             r40h06615bd_0    conda-forge
591 | r-tidyr                   1.2.1             r40h7525677_0    conda-forge
592 | r-tidyselect              1.1.2             r40hc72bb7e_0    conda-forge
593 | r-tidyverse               1.3.2             r40hc72bb7e_0    conda-forge
594 | r-timedate                4021.104          r40hc72bb7e_0    conda-forge
595 | r-tinytex                 0.42              r40hc72bb7e_0    conda-forge
596 | r-triebeard               0.3.0           r40h7525677_1004    conda-forge
597 | r-ttr                     0.24.3            r40h06615bd_0    conda-forge
598 | r-tzdb                    0.3.0             r40h7525677_0    conda-forge
599 | r-urlchecker              1.0.1             r40hc72bb7e_0    conda-forge
600 | r-urltools                1.7.3             r40h7525677_2    conda-forge
601 | r-usethis                 2.1.6             r40hc72bb7e_0    conda-forge
602 | r-utf8                    1.2.2             r40hcfec24a_0    conda-forge
603 | r-uuid                    1.1_0             r40h06615bd_0    conda-forge
604 | r-uwot                    0.1.14            r40h7525677_0    conda-forge
605 | r-vctrs                   0.4.1             r40h7525677_0    conda-forge
606 | r-vipor                   0.4.5           r40hc72bb7e_1003    conda-forge
607 | r-viridis                 0.6.2             r40hc72bb7e_0    conda-forge
608 | r-viridislite             0.4.1             r40hc72bb7e_0    conda-forge
609 | r-vroom                   1.5.7             r40h03ef668_0    conda-forge
610 | r-waldo                   0.4.0             r40hc72bb7e_0    conda-forge
611 | r-whisker                 0.4               r40hc72bb7e_1    conda-forge
612 | r-withr                   2.5.0             r40hc72bb7e_0    conda-forge
613 | r-xfun                    0.33              r40h7525677_0    conda-forge
614 | r-xml2                    1.3.3             r40h7525677_1    conda-forge
615 | r-xopen                   1.0.0           r40hc72bb7e_1003    conda-forge
616 | r-xtable                  1.8_4             r40hc72bb7e_3    conda-forge
617 | r-xts                     0.12.1            r40h06615bd_0    conda-forge
618 | r-yaml                    2.3.5             r40h06615bd_0    conda-forge
619 | r-zip                     2.2.1             r40h06615bd_0    conda-forge
620 | r-zoo                     1.8_11            r40h06615bd_0    conda-forge
621 | re2                       2022.06.01           h27087fc_1    conda-forge
622 | readline                  8.1.2                h0f457ee_0    conda-forge
623 | requests                  2.28.2             pyhd8ed1ab_0    conda-forge
624 | requests-oauthlib         1.3.1              pyhd8ed1ab_0    conda-forge
625 | reretry                   0.11.8             pyhd8ed1ab_0    conda-forge
626 | rich                      13.1.0             pyhd8ed1ab_0    conda-forge
627 | rpy2                      3.5.1           py37r40hda87dfa_0    conda-forge
628 | rsa                       4.9                pyhd8ed1ab_0    conda-forge
629 | s3transfer                0.6.0              pyhd8ed1ab_0    conda-forge
630 | scanorama                 1.7                        py_0    bioconda
631 | scanpy                    1.9.1                    pypi_0    pypi
632 | scgen                     2.1.0                    pypi_0    pypi
633 | scib                      1.1.1                    pypi_0    pypi
634 | scikit-image              0.19.3                   pypi_0    pypi
635 | scikit-learn              1.0.2            py37hf9e9bfc_0    conda-forge
636 | scikit-misc               0.1.4                    pypi_0    pypi
637 | scipy                     1.7.3            py37hf2a6cf1_0    conda-forge
638 | scrublet                  0.2.3                    pypi_0    pypi
639 | scvi-tools                0.16.1             pyhd8ed1ab_0    conda-forge
640 | seaborn                   0.12.2                   pypi_0    pypi
641 | sed                       4.8                  he412f7d_0    conda-forge
642 | send2trash                1.8.0              pyhd8ed1ab_0    conda-forge
643 | session-info              1.0.0                    pypi_0    pypi
644 | setuptools                59.8.0           py37h89c1867_1    conda-forge
645 | simplegeneric             0.8.1                      py_1    conda-forge
646 | six                       1.16.0             pyh6c4a22f_0    conda-forge
647 | slacker                   0.14.0                     py_0    conda-forge
648 | sleef                     3.5.1                h9b69904_2    conda-forge
649 | smart_open                6.3.0              pyhd8ed1ab_1    conda-forge
650 | smmap                     3.0.5              pyh44b312d_0    conda-forge
651 | snakemake                 7.20.0               hdfd78af_0    bioconda
652 | snakemake-minimal         7.20.0             pyhdfd78af_0    bioconda
653 | sniffio                   1.3.0              pyhd8ed1ab_0    conda-forge
654 | sortedcontainers          2.4.0              pyhd8ed1ab_0    conda-forge
655 | soupsieve                 2.3.2.post1        pyhd8ed1ab_0    conda-forge
656 | sqlite                    3.40.0               h4ff8645_0    conda-forge
657 | statsmodels               0.13.5                   pypi_0    pypi
658 | stdlib-list               0.8.0                    pypi_0    pypi
659 | stone                     3.3.1              pyhd8ed1ab_0    conda-forge
660 | stopit                    1.1.2                      py_0    conda-forge
661 | sysroot_linux-64          2.12                he073ed8_15    conda-forge
662 | tabulate                  0.9.0              pyhd8ed1ab_1    conda-forge
663 | tbb                       2021.7.0             h924138e_1    conda-forge
664 | tensorboard               2.3.0                      py_0    conda-forge
665 | tensorboard-plugin-wit    1.8.1              pyhd8ed1ab_0    conda-forge
666 | terminado                 0.17.1             pyh41d4057_0    conda-forge
667 | texttable                 1.6.7                    pypi_0    pypi
668 | threadpoolctl             3.1.0              pyh8a188c0_0    conda-forge
669 | throttler                 1.2.1              pyhd8ed1ab_0    conda-forge
670 | tifffile                  2021.11.2                pypi_0    pypi
671 | tinycss2                  1.2.1              pyhd8ed1ab_0    conda-forge
672 | tk                        8.6.12               h27826a3_0    conda-forge
673 | tktable                   2.10                 hb7b940f_3    conda-forge
674 | tomli                     2.0.1              pyhd8ed1ab_0    conda-forge
675 | toolz                     0.12.0             pyhd8ed1ab_0    conda-forge
676 | toposort                  1.7                pyhd8ed1ab_0    conda-forge
677 | torch                     1.13.1                   pypi_0    pypi
678 | torchmetrics              0.11.0             pyhd8ed1ab_0    conda-forge
679 | torchvision               0.14.1                   pypi_0    pypi
680 | tornado                   6.2              py37h540881e_0    conda-forge
681 | tqdm                      4.64.1             pyhd8ed1ab_0    conda-forge
682 | traitlets                 5.8.1              pyhd8ed1ab_0    conda-forge
683 | trvaep                    0.1.0                    pypi_0    pypi
684 | typing-extensions         4.4.0                hd8ed1ab_0    conda-forge
685 | typing_extensions         4.4.0              pyha770c72_0    conda-forge
686 | tzdata                    2022g                h191b570_0    conda-forge
687 | tzlocal                   4.2              py37h89c1867_1    conda-forge
688 | ubiquerg                  0.6.2              pyhd8ed1ab_0    conda-forge
689 | umap-learn                0.5.3            py37h89c1867_0    conda-forge
690 | unicodedata2              14.0.0           py37h540881e_1    conda-forge
691 | upsetplot                 0.8.0                    pypi_0    pypi
692 | uritemplate               4.1.1              pyhd8ed1ab_0    conda-forge
693 | urllib3                   1.26.14            pyhd8ed1ab_0    conda-forge
694 | veracitools               0.1.3                      py_0    conda-forge
695 | wcwidth                   0.2.6              pyhd8ed1ab_0    conda-forge
696 | webencodings              0.5.1                      py_1    conda-forge
697 | websocket-client          1.4.2              pyhd8ed1ab_0    conda-forge
698 | werkzeug                  2.2.2              pyhd8ed1ab_0    conda-forge
699 | wheel                     0.38.4             pyhd8ed1ab_0    conda-forge
700 | widgetsnbextension        4.0.5              pyhd8ed1ab_0    conda-forge
701 | wrapt                     1.14.1           py37h540881e_0    conda-forge
702 | xlsxwriter                3.1.9                    pypi_0    pypi
703 | xorg-kbproto              1.0.7             h7f98852_1002    conda-forge
704 | xorg-libice               1.0.10               h7f98852_0    conda-forge
705 | xorg-libsm                1.2.3             hd9c2040_1000    conda-forge
706 | xorg-libx11               1.7.2                h7f98852_0    conda-forge
707 | xorg-libxau               1.0.9                h7f98852_0    conda-forge
708 | xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
709 | xorg-libxext              1.3.4                h7f98852_1    conda-forge
710 | xorg-libxrender           0.9.10            h7f98852_1003    conda-forge
711 | xorg-libxt                1.2.1                h7f98852_2    conda-forge
712 | xorg-renderproto          0.11.1            h7f98852_1002    conda-forge
713 | xorg-xextproto            7.3.0             h7f98852_1002    conda-forge
714 | xorg-xproto               7.0.31            h7f98852_1007    conda-forge
715 | xz                        5.2.6                h166bdaf_0    conda-forge
716 | yaml                      0.2.5                h7f98852_2    conda-forge
717 | yarl                      1.7.2            py37h540881e_2    conda-forge
718 | yte                       1.5.1            py37h89c1867_0    conda-forge
719 | zeromq                    4.3.4                h9c3ff4c_1    conda-forge
720 | zipp                      3.11.0             pyhd8ed1ab_0    conda-forge
721 | zlib                      1.2.13               h166bdaf_4    conda-forge
722 | zstd                      1.5.2                h3eb15da_5    conda-forge
723 | 


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