├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── cellanova ├── .DS_Store ├── __init__.py ├── model.py └── utils.py ├── data ├── Simu1_SeuratObj.rds └── t1d_example.h5ad ├── dist ├── cellanova-0.1.0-py3-none-any.whl └── cellanova-0.1.0.tar.gz ├── environment.yml ├── figures └── model.jpg ├── pyproject.toml └── tutorials ├── cellanova_custom_integration.html ├── cellanova_custom_integration.ipynb ├── cellanova_integration.html ├── cellanova_integration.ipynb ├── eval_batch_removal.html ├── eval_batch_removal.ipynb ├── eval_distortion.html ├── eval_distortion.ipynb ├── eval_signal_preservation.html └── eval_signal_preservation.ipynb /.gitattributes: -------------------------------------------------------------------------------- 1 | data/*.h5ad filter=lfs diff=lfs merge=lfs -text 2 | data/Simu1_SeuratObj.rds filter=lfs diff=lfs merge=lfs -text 3 | data/t1d_example.h5ad filter=lfs diff=lfs merge=lfs -text 4 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | cellanova/__pycache__/ 4 | *.py[cod] 5 | *$py.class 6 | 7 | # C extensions 8 | *.so 9 | 10 | # Distribution / packaging 11 | .Python 12 | build/ 13 | develop-eggs/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CellANOVA: Cell State Space Analysis of Variance 2 | 3 | - [Overview](#overview) 4 | - [Installation](#installation) 5 | - [Tutorials](#tutorials) 6 | - [License](#license) 7 | 8 | 9 | 10 | ## Overview 11 | The integration of cells across samples to remove unwanted batch variation plays a critical role in single cell analyses. When the samples are expected to be biologically distinct, it is often unclear how aggressively the cells should be aligned across samples to achieve uniformity. CellANOVA is a Python package for batch integration with signal recovery in single cell data. It builds on existing single cell data integration methods, and uses a pool of control samples to quantify the batch effect and separate meaningful biological variation from unwanted batch variation. When used with an existing integration method, CellAnova allows the recovery of biological signals that are lost during integration. 12 | 13 | There are two ways to use CellANOVA. You can start from scratch, or you can start with an existing integration. If you start from scratch, CellANOVA will compute an initial integration with [Harmony](https://portals.broadinstitute.org/harmony/). However, we have also achieved good results when starting with integration computed using [Seurat](https://satijalab.org/seurat/articles/integration_rpca.html). The method is agnostic to the initial integration algorithm, and if you prefer to start by performing your own integration, you can choose any algorithm hat gives a reasonable initial alignment of your data. 14 | 15 | You will also need to select a pool of control samples. These ``control'' samples will be used to estimate a latent linear space that captures cell- and gene-specific unwanted variations. The basis vectors of this linear space will then be used to quantify the batch effect in each cell across all samples, and this will allow you to recover any cell- and gene-specific biological signal that is separable from batch effect that may have been erased during the initial integration. By using only samples in the control pool in the estimation of the batch variation space, CellANOVA preserves any biological differences in the non-control samples that lie outside this space. Importantly, CellANOVA produces a batch corrected gene expression matrix which can be used for gene- and pathway-level downstream analyses. 16 | The selection of control pool samples depends on your experimental design. Please see our manuscript for examples on how controls could be selected in case-control, longitudinal, or other single cell study designs. 17 | 18 | CellANOVA has been tested on data sets consisting of hundreds of thousands of cells. Here are the runtimes recorded on a XX machine: 19 | 100,000 cells: 20 | 1,000,000 cells: 21 | 22 | For more model details, validation results and real dataset analysis, please check out our [manuscript on bioRXiv](https://www.biorxiv.org/content/10.1101/2023.05.05.539614v3). If you use our method, please use the following citation: 23 | 24 | ``` 25 | @article{zhang2023signal, 26 | title={Signal recovery in single cell batch integration}, 27 | author={Zhang, Zhaojun and Mathew, Divij and Lim, Tristan and Huang, Sijia and Wherry, E John and Minn, Andy J and Ma, Zongming and Zhang, Nancy R}, 28 | journal={bioRxiv}, 29 | pages={2023--05}, 30 | year={2023}, 31 | publisher={Cold Spring Harbor Laboratory} 32 | } 33 | ``` 34 | 35 | 36 | ## Installation 37 | 38 | Our Python package has been tested on python=3.7, 3.8, 3.9. It depends on numpy>=1.20.3, scipy>=1.7.1, pandas>=1.3.2, scikit-learn>=1.0.2, anndata>=0.7.6, scanpy>=1.8.1, harmonypy>=0.0.6. 39 | 40 | If you use pip, then the package can be installed by 41 | 42 | ```bash 43 | pip install cellanova 44 | ``` 45 | 46 | If you use conda environment, you can use the following command for an easy setup. It will build a seperate cellanova environment, and have all dependencies installed. 47 | 48 | ```bash 49 | conda env create -f environment.yml 50 | ``` 51 | 52 | 53 | 54 | ## Tutorials 55 | ### Quick Start 56 | 57 | The following is a quick example showing CellANOVA integration pipeline. 58 | 59 | ```python 60 | ## load required package 61 | import anndata as ad 62 | import scanpy as sc 63 | import cellanova as cnova 64 | 65 | ## load and preprocess data 66 | adata = sc.read_h5ad('raw_data.h5ad') 67 | adata_prep = cnova.model.preprocess_data(adata, integrate_key='dataidx') 68 | 69 | ## construct control pool 70 | control_dict = { 71 | 'pool1': list(set(adata_prep[adata_prep.obs['condition']=='control',].obs['dataidx'])), 72 | } 73 | 74 | ## model fitting 75 | adata_prep= cnova.model.calc_ME(adata_prep, integrate_key='dataidx') 76 | adata_prep = cnova.model.calc_BE(adata_prep, integrate_key, control_dict) 77 | adata_prep = cnova.model.calc_TE(adata_prep, integrate_key) 78 | 79 | ## create an independent anndata object for cellanova-integrated data 80 | integrated = ad.AnnData(adata_prep.layers['denoised'], dtype=np.float32) 81 | integrated.obs = adata_prep.obs.copy() 82 | integrated.var_names = adata_prep.var_names 83 | ``` 84 | 85 | 86 | ### Example Workflows 87 | 88 | For more detailed illustrations, please refer to the following jupyter notebooks: 89 | * [CellANOVA integration workfolow](https://github.com/Janezjz/cellanova/blob/main/tutorials/cellanova_integration.ipynb) 90 | * [CellANOVA with custom integration](https://github.com/Janezjz/cellanova/blob/main/tutorials/cellanova_custom_integration.ipynb) 91 | * [Evaluation of batch removal performance](https://github.com/Janezjz/cellanova/blob/main/tutorials/eval_batch_removal.ipynb) 92 | * [Evaluation of distortion](https://github.com/Janezjz/cellanova/blob/main/tutorials/eval_distortion.ipynb) 93 | * [Evaluation of signal preservation](https://github.com/Janezjz/cellanova/blob/main/tutorials/eval_signal_preservation.ipynb) 94 | 95 | 96 | 97 | ## License 98 | 99 | This project is licensed under the terms of GNU GENERAL PUBLIC LICENSE Version 3. 100 | -------------------------------------------------------------------------------- /cellanova/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Janezjz/cellanova/01ff1eb8ba21debcdae41b138883f06aa7cfac75/cellanova/.DS_Store -------------------------------------------------------------------------------- /cellanova/__init__.py: -------------------------------------------------------------------------------- 1 | from . import model, utils 2 | 3 | __version__ = "0.1.0" -------------------------------------------------------------------------------- /cellanova/model.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import functools as ft 5 | import seaborn as sea 6 | import anndata as ad 7 | import scanpy as sc 8 | import scanpy.external as sce 9 | from sklearn import linear_model 10 | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay 11 | from scipy.io import mmread 12 | from scipy.sparse import csc_matrix, diags 13 | from scipy.sparse.linalg import svds 14 | import gc 15 | import sys 16 | import harmonypy as hm 17 | 18 | 19 | 20 | 21 | def calc_ME(adata, integrate_key, k_harmony=70, dim_ME = 70, return_harmony = False): 22 | ''' Compute cell states and main effects 23 | 24 | Parameters 25 | ---------- 26 | adata : anndata object 27 | Preprocessed data. Should be normalized log-transformed and scaled. 28 | integrate_key : str 29 | Key for the integration unit for harmony. Should be a column name of adata.obs. 30 | dim_ME : int, optional 31 | Dimension of main effects. 32 | n_pcs : int, optional 33 | Number of principle components used in harmony integration. 34 | return_harmony : boolean, optional 35 | Whether or not return harmony integrated data. 36 | 37 | Returns 38 | ---------- 39 | adata: anndata object 40 | Three new attributes added. 41 | adata.obsm['Cmat'] : np.array, the estimated cell embedding. 42 | adata.varm['Mmat'] : np.array, the estimated main effect matrix. 43 | adata.layers['main_effect'] : np.array, the estimated main effects. 44 | adata_harmony: anndata object 45 | Returned only when return_harmony = True. Harmony integrated data. 46 | ''' 47 | 48 | # harmony integration 49 | rna_temp = ad.AnnData(adata[:, adata.var.highly_variable].X, dtype = np.float32) 50 | rna_temp.var_names = adata[:, adata.var.highly_variable].var_names 51 | rna_temp.obs = adata.obs.copy() 52 | sc.tl.pca(rna_temp, n_comps = k_harmony) 53 | sce.pp.harmony_integrate(rna_temp, integrate_key, max_iter_harmony = 30) 54 | adata_harmony = ad.AnnData(rna_temp.obsm['X_pca_harmony'] @ rna_temp.varm['PCs'].T, dtype = np.float32) 55 | adata_harmony.var_names = rna_temp.var_names 56 | adata_harmony.obs = rna_temp.obs.copy() 57 | 58 | # estimate C, M 59 | Cmat, _, _ = svds(adata_harmony.X, k=dim_ME) 60 | dataidx = np.unique(adata.obs[integrate_key]) 61 | bloc_coef = () 62 | counter = 0 63 | for x in dataidx: 64 | bloc_temp = linear_model.LinearRegression(fit_intercept = False) 65 | bloc_temp.fit(Cmat[adata.obs[integrate_key] == x,:], adata.layers['scale'][adata.obs[integrate_key] == x,:]) 66 | bloc_coef = bloc_coef + (bloc_temp.coef_.T,) 67 | counter = counter + 1 68 | Mmat = ft.reduce(np.add, bloc_coef) / len(dataidx) 69 | 70 | adata.obsm['Cmat'] = Cmat 71 | adata.varm['Mmat'] = Mmat.T 72 | adata.layers['main_effect'] = Cmat @ Mmat 73 | 74 | if return_harmony: 75 | return adata, adata_harmony 76 | else: 77 | return adata 78 | 79 | 80 | def fit_M(adata, integrate_key): 81 | 82 | # input: 83 | # adata is after preprocessing step. 84 | # adata including all control and treatment samples 85 | # adata should have cellstates stored in adata.obsm['Cmat'] 86 | # integrate_key indicates sample ID, the smallest unit 87 | 88 | Cmat = adata.obsm['Cmat'] 89 | dataidx = np.unique(adata.obs[integrate_key]) 90 | bloc_coef = () 91 | counter = 0 92 | for x in dataidx: 93 | bloc_temp = linear_model.LinearRegression(fit_intercept = False) 94 | bloc_temp.fit(Cmat[adata.obs[integrate_key] == x,:], adata.layers['scale'][adata.obs[integrate_key] == x,:]) 95 | bloc_coef = bloc_coef + (bloc_temp.coef_.T,) 96 | counter = counter + 1 97 | Mmat = ft.reduce(np.add, bloc_coef) / len(dataidx) 98 | 99 | return Mmat 100 | 101 | 102 | 103 | 104 | def calc_BE(adata, integrate_key, control_dict, var_cutoff = 0.9, k_max = 1500, verbose = False, k_select = None): 105 | ''' Compute basis of batch effect. Perform batch correction. 106 | 107 | Parameters 108 | ---------- 109 | adata : anndata object 110 | Preprocessed data. Should be normalized log-transformed and scaled. 111 | integrate_key : str 112 | Key for the integration unit in the control_dict. Should be a column name of adata.obs. 113 | control_dict : dict 114 | A dictionary, in which each key is a control group, and the corresponding values are the integration units 115 | in this group. 116 | var_cutoff : float, optional 117 | Fraction of explained variance to determine the optimal value of k in truncated SVD. 118 | k_max: int, optional 119 | Max of singular values and vectors to compute. 120 | verbose: boolean, optional 121 | Whether to plot sigular values or not. 122 | k_select: int, optional 123 | Pre-determined number of singular values and vectors to compute. 124 | 125 | Returns 126 | ---------- 127 | adata: anndata object 128 | Three new attributes added. 129 | adata.varm['V_BE_basis'] : np.array, batch effect basis matrix. 130 | adata.uns['S_BE_basis'] : np.array, singular values of batch basis. 131 | adata.layers['corrected'] : np.array, the batch corrected object. 132 | ''' 133 | 134 | # regression 135 | control_groups = list(control_dict.keys()) 136 | LL = adata.obsm['Cmat'] 137 | res = () 138 | for g in control_groups: 139 | control_batch = control_dict[g] 140 | bloc_coef = () 141 | counter = 0 142 | 143 | for x in control_batch: 144 | bloc_temp = linear_model.LinearRegression(fit_intercept = False) 145 | bloc_temp.fit(LL[adata.obs[integrate_key] == x,:], adata.layers['scale'][adata.obs[integrate_key] == x,:]) 146 | bloc_coef = bloc_coef + (bloc_temp.coef_.T,) 147 | counter = counter + 1 148 | 149 | M = ft.reduce(np.add, bloc_coef) / len(control_batch) 150 | res_temp = np.concatenate(bloc_coef, axis = 0) - np.tile(M, (len(control_batch),1)) 151 | res = res + (res_temp,) 152 | 153 | res_combined = np.concatenate(res, axis = 0) 154 | 155 | # perform svd 156 | k_max = np.min([k_max, res_combined.shape[0]-1, res_combined.shape[1]-1]) 157 | _, DD1, _ = svds(res_combined, k = k_max) 158 | if verbose: 159 | plt.plot(sorted(np.sqrt(DD1[DD1 > 0]),reverse = True),'bo-') 160 | if k_select is not None: 161 | k = k_select 162 | else: 163 | DD1_rev = np.array(sorted(np.sqrt(DD1[DD1 > 0]),reverse = True)) 164 | variance = np.cumsum(DD1_rev ** 2) / np.sum(DD1_rev ** 2) 165 | k = np.argmax(variance >= var_cutoff) 166 | _, DD1, VV1T = svds(res_combined, k = k) 167 | 168 | adata.varm['V_BE_basis'] = VV1T.T 169 | adata.uns['S_BE_basis'] = DD1 170 | be = (adata.layers['scale'] - adata.obsm['Cmat'] @ adata.varm['Mmat'].T) @ VV1T.T @ VV1T 171 | adata.layers['corrected'] = adata.layers['scale'] - be 172 | 173 | return adata 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | def calc_TE(adata, integrate_key, #control_dict, 183 | var_cutoff = 0.7, k_max = 1500, verbose = False, k_select = None): 184 | ''' Compute basis of treatment effect. Perform final integration. 185 | 186 | Parameters 187 | ---------- 188 | adata : anndata object 189 | Preprocessed data. Should be normalized log-transformed and scaled. 190 | integrate_key : str 191 | Key for the integration unit in the control_dict. Should be a column name of adata.obs. 192 | control_dict : dict 193 | A dictionary, in which each key is a control group, and the corresponding values are the integration units 194 | in this group. 195 | var_cutoff : float, optional 196 | Fraction of explained variance to determine the optimal value of k in truncated SVD. 197 | k_max: int, optional 198 | Max of singular values and vectors to compute. 199 | verbose: boolean, optional 200 | Whether to plot sigular values or not. 201 | k_select: int, optional 202 | Pre-determined number of singular values and vectors to compute. 203 | 204 | Returns 205 | ---------- 206 | adata: anndata object 207 | Four new attributes added. 208 | adata.varm['W_TE_basis'] : np.array, treatment effect basis matrix. 209 | adata.uns['S_TE_basis'] : np.array, singular values of treatment basis. 210 | adata.layers['trt_effect'] : np.array, the estimated treatment effects. 211 | adata.layers['denoised'] : np.array, the integrated data. 212 | ''' 213 | 214 | # regression 215 | trt_batch = list(set(adata.obs[integrate_key])) 216 | trt_bloc_coef = () 217 | counter = 0 218 | for x in trt_batch: 219 | bloc_temp = linear_model.LinearRegression(fit_intercept = False) 220 | bloc_temp.fit(adata.obsm['Cmat'][adata.obs[integrate_key] == x,:], 221 | adata.layers['corrected'][adata.obs[integrate_key] == x,:]) 222 | trt_bloc_coef = trt_bloc_coef + (bloc_temp.coef_.T,) 223 | counter = counter + 1 224 | 225 | res_combined = np.concatenate(trt_bloc_coef, axis = 0) - np.tile(adata.varm['Mmat'].T, (len(trt_bloc_coef),1)) 226 | 227 | # perform svd 228 | k_max = np.min([k_max, res_combined.shape[0]-1, res_combined.shape[1]-1]) 229 | _, DD2, _ = svds(res_combined, k = k_max) 230 | if verbose: 231 | plt.plot(sorted(np.sqrt(DD2[DD2 > 0]),reverse = True),'bo-') 232 | if k_select is not None: 233 | k = k_select 234 | else: 235 | DD2_rev = np.array(sorted(np.sqrt(DD2[DD2 > 0]),reverse = True)) 236 | variance = np.cumsum(DD2_rev ** 2) / np.sum(DD2_rev ** 2) 237 | k = np.argmax(variance >= var_cutoff) 238 | _, DD2, WW2T = svds(res_combined, k = k) 239 | 240 | adata.varm['W_TE_basis'] = WW2T.T 241 | adata.uns['S_TE_basis'] = DD2 242 | te = (adata.layers['corrected'] - adata.obsm['Cmat'] @ adata.varm['Mmat'].T) @ WW2T.T @ WW2T 243 | adata.layers['trt_effect'] = te 244 | adata.layers['denoised'] = adata.layers['trt_effect'] + adata.layers['main_effect'] 245 | 246 | return adata 247 | 248 | 249 | 250 | def calc_BT_coef(adata, integrate_key): 251 | 252 | Cmat = adata.obsm['Cmat'] 253 | dataidx = np.unique(adata.obs[integrate_key]) 254 | bloc_coef = () 255 | counter = 0 256 | for x in dataidx: 257 | bloc_temp = linear_model.LinearRegression(fit_intercept = False) 258 | bloc_temp.fit(Cmat[adata.obs[integrate_key] == x,:], adata.layers['scale'][adata.obs[integrate_key] == x,:]) 259 | bloc_coef = bloc_coef + (bloc_temp.coef_.T,) 260 | counter = counter + 1 261 | Mmat = ft.reduce(np.add, bloc_coef) / len(dataidx) 262 | 263 | 264 | 265 | def preprocess_data(adata, integrate_key, n_hvgs=3000, hvg_only = True, copy_raw = False, copy_lognorm = True): 266 | ''' Perform CellANOVA preprocessing: library size normalization + log1p + hvg selection + standardization 267 | 268 | Parameters 269 | ---------- 270 | adata : anndata object 271 | Raw data, containing gene expression counts. 272 | integrate_key : str 273 | Key indicating smallest batch unit. Should be a column name of adata.obs. 274 | n_hvgs : int, optional 275 | Number of highly variable genes. 276 | hvg_only : boolean, optional 277 | Whether or not return only highly variable genes or all genes. 278 | copy_raw : boolean, optional 279 | Whether or not save a copy of raw counts. 280 | 281 | Returns 282 | ---------- 283 | adata: anndata object 284 | Data after preprocessing, which can be passed to cell state estimation. 285 | ''' 286 | 287 | ## Preprocess: reorder, normalize per cell (sum to 1e5), log, select 3000 hvgs batch-wise, scale 288 | batch = np.unique(adata.obs[integrate_key]) 289 | list_batch = [] 290 | for x in batch: 291 | adata_iter = adata[adata.obs[integrate_key] == x] 292 | if copy_raw: 293 | adata_iter.layers['counts'] = adata_iter.X 294 | sc.pp.normalize_total(adata_iter, target_sum=1e5) 295 | list_batch.append(adata_iter) 296 | 297 | adata_prep = ad.concat(list_batch) 298 | adata_prep = sc.pp.log1p(adata_prep, copy = True) 299 | if copy_lognorm: 300 | adata_prep.layers['lognorm'] = adata_prep.X 301 | sc.pp.highly_variable_genes(adata_prep, n_top_genes=n_hvgs, batch_key = integrate_key) 302 | if hvg_only: 303 | adata_prep = adata_prep[:, adata_prep.var.highly_variable].copy() 304 | 305 | sc.pp.scale(adata_prep) 306 | adata_prep.layers['scale'] = adata_prep.X 307 | 308 | return adata_prep 309 | 310 | 311 | 312 | -------------------------------------------------------------------------------- /cellanova/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import functools as ft 5 | import seaborn as sea 6 | import anndata as ad 7 | import scanpy as sc 8 | import scanpy.external as sce 9 | from sklearn import linear_model 10 | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay 11 | from scipy.io import mmread 12 | from scipy.sparse import csc_matrix, diags 13 | from scipy.sparse.linalg import svds 14 | import gc 15 | import sys 16 | import harmonypy as hm 17 | from sklearn.neighbors import NearestNeighbors 18 | 19 | 20 | 21 | def fit_knn(mat_train, mat_holdout, n_neighbors, algorithm = 'kd_tree'): 22 | 23 | # fit knn using mat_train 24 | # return nn indices and distances in train set for holdout set 25 | knn = NearestNeighbors(n_neighbors = n_neighbors, algorithm = algorithm).fit(mat_train) 26 | distances, indices = knn.kneighbors(mat_holdout) 27 | indices = indices[:,1:] 28 | distances = distances[:,1:] 29 | 30 | return indices, distances 31 | 32 | 33 | 34 | def calc_knn_prop(knn_indices, labels_train, label_categories): 35 | 36 | # knn_indices: shape = (n_holdout_samples, (knn-1)), np.array 37 | # labels_train: shape = (n_train_samples, ), pd.object 38 | # label_categories: shape = (n_label_categories, ), np.array 39 | n = knn_indices.shape[0] 40 | n_category = label_categories.shape[0] 41 | nn_prop = np.zeros(shape = (n, n_category)) 42 | 43 | for i in range(n): 44 | knn_labels = labels_train[knn_indices[i,]] 45 | for k in range(n_category): 46 | nn_prop[i, k] = sum(knn_labels == label_categories[k]) 47 | 48 | nn_prop = nn_prop / knn_indices.shape[1] 49 | return nn_prop 50 | 51 | 52 | def calc_oobNN(adata_orig, batch_key, condition_key, n_neighbors=15): 53 | ''' Compute out-of-batch k-nearest-neighbor composition 54 | 55 | Parameters 56 | ---------- 57 | adata_orig : anndata object 58 | Expression data stored in adata_orig.X, based on which to compute out of batch nearest neighbors. 59 | batch_key : str 60 | Variable name indicating batch. Should be a column name of adata.obs. 61 | condition_key : str 62 | Variable name indicating condition. Should be a column name of adata.obs. 63 | We compute out-of-batch proportion of each condition level within each cell's neighborhood. 64 | n_neighbors : int, optional 65 | Number of k-nearest neighbors. 66 | 67 | Returns 68 | ---------- 69 | res: anndata object 70 | One new attribute added. 71 | res.obsm['knn_prop'] : pd.DataFrame, out-of-batch k-nearest-neighbor composition, cell-by-condition 72 | ''' 73 | 74 | np.random.seed(123) 75 | 76 | list_holdout = [] 77 | for holdout_idx in np.unique(adata_orig.obs[batch_key]): 78 | 79 | adata_train = adata_orig[~adata_orig.obs[batch_key].isin([holdout_idx])] 80 | adata_holdout = adata_orig[adata_orig.obs[batch_key].isin([holdout_idx])] 81 | num_cells = adata_train.obs[condition_key].value_counts().min() 82 | 83 | a_list = [] 84 | for x in np.unique(adata_train.obs[condition_key]): 85 | a1 = adata_train[adata_train.obs[condition_key].isin([x])] 86 | random_indices = np.random.choice(a1.shape[0], size=num_cells, replace = False) 87 | a1 = a1[random_indices,:] 88 | a_list.append(a1) 89 | 90 | adata_train = ad.concat(a_list) 91 | adata = ad.concat([adata_train, adata_holdout]) 92 | 93 | mat = sc.tl.pca(adata.X, n_comps = 20) 94 | mat_train = mat[~adata.obs[batch_key].isin([holdout_idx]),] 95 | mat_holdout = mat[adata.obs[batch_key].isin([holdout_idx]),] 96 | 97 | # fit knn 98 | indices, distances = fit_knn(mat_train=mat_train, mat_holdout=mat_holdout, n_neighbors=n_neighbors, algorithm = 'kd_tree') 99 | 100 | # compute proprotion 101 | labels_train = adata_train.obs[condition_key].astype('object') 102 | label_categories = np.unique(labels_train) 103 | result = calc_knn_prop(indices, labels_train, label_categories) 104 | knn_df = pd.DataFrame(data=result, 105 | index = adata_holdout.obs_names, 106 | columns = label_categories) 107 | adata_holdout.obsm['knn_prop'] = knn_df 108 | list_holdout.append(adata_holdout) 109 | 110 | res = ad.concat(list_holdout) 111 | return res 112 | 113 | 114 | -------------------------------------------------------------------------------- /data/Simu1_SeuratObj.rds: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c98e58d80670b9e33c75fabfa6c280772b951b78106bf4cf1cf00a9fb95fd7e7 3 | size 282543263 4 | -------------------------------------------------------------------------------- /data/t1d_example.h5ad: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:a8d1235ee0d273d93a54f98df185e1fc496904ddd72d60f1548b31e4500cd3b9 3 | size 2095693344 4 | -------------------------------------------------------------------------------- /dist/cellanova-0.1.0-py3-none-any.whl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Janezjz/cellanova/01ff1eb8ba21debcdae41b138883f06aa7cfac75/dist/cellanova-0.1.0-py3-none-any.whl -------------------------------------------------------------------------------- /dist/cellanova-0.1.0.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Janezjz/cellanova/01ff1eb8ba21debcdae41b138883f06aa7cfac75/dist/cellanova-0.1.0.tar.gz -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: cnova-env 2 | channels: 3 | - 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tinycss2=1.2.1=pyhd8ed1ab_0 228 | - tk=8.6.12=h5dbffcc_0 229 | - toml=0.10.2=pyhd8ed1ab_0 230 | - tomli=2.0.1=pyhd8ed1ab_0 231 | - tornado=6.2=py38hef030d1_1 232 | - tqdm=4.65.0=pyhd8ed1ab_1 233 | - traitlets=5.9.0=pyhd8ed1ab_0 234 | - typing-extensions=4.5.0=hd8ed1ab_0 235 | - typing_extensions=4.5.0=pyha770c72_0 236 | - umap-learn=0.5.3=py38h50d1736_0 237 | - unicodedata2=15.0.0=py38hef030d1_0 238 | - urllib3=1.26.15=pyhd8ed1ab_0 239 | - wcwidth=0.2.6=pyhd8ed1ab_0 240 | - webencodings=0.5.1=py_1 241 | - websocket-client=1.5.1=pyhd8ed1ab_0 242 | - wheel=0.40.0=pyhd8ed1ab_0 243 | - widgetsnbextension=4.0.7=pyhd8ed1ab_0 244 | - xorg-libxau=1.0.9=h35c211d_0 245 | - xorg-libxdmcp=1.1.3=h35c211d_0 246 | - xz=5.2.6=h775f41a_0 247 | - yaml=0.2.5=h0d85af4_2 248 | - zeromq=4.3.4=he49afe7_1 249 | - zipp=3.15.0=pyhd8ed1ab_0 250 | - zstd=1.5.2=hbc0c0cd_6 251 | - pip: 252 | - aiofiles==22.1.0 253 | - aiosqlite==0.18.0 254 | - arrow==1.2.3 255 | - babel==2.12.1 256 | - build==0.10.0 257 | - cellanova==0.1.0 258 | - fqdn==1.5.1 259 | - harmonypy==0.0.9 260 | - isoduration==20.11.0 261 | - json5==0.9.11 262 | - jsonpointer==2.3 263 | - jupyter-server-fileid==0.9.0 264 | - jupyter-server-ydoc==0.8.0 265 | - jupyter-ydoc==0.2.4 266 | - jupyterlab==3.6.3 267 | - jupyterlab-server==2.22.1 268 | - pyproject-hooks==1.0.0 269 | - setuptools==67.7.1 270 | - uri-template==1.2.0 271 | - webcolors==1.13 272 | - y-py==0.5.9 273 | - ypy-websocket==0.8.2 274 | prefix: /Users/zzj/opt/anaconda3/envs/cnova-env 275 | -------------------------------------------------------------------------------- /figures/model.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Janezjz/cellanova/01ff1eb8ba21debcdae41b138883f06aa7cfac75/figures/model.jpg -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=42", "wheel"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "cellanova" 7 | version = "0.1.0" 8 | description = "Cell state space analysis of variance for signal recovery with batch correction " 9 | authors = [ 10 | { name="Jane Zhang", email="zjzhang1235@gmail.com" }, 11 | { name="Zongming Ma"}, 12 | { name="Nancy Zhang"} 13 | ] 14 | readme = "README.md" 15 | requires-python = ">=3.7" 16 | classifiers = [ 17 | "Programming Language :: Python :: 3", 18 | "Operating System :: OS Independent", 19 | ] 20 | dependencies = [ 21 | "numpy>=1.20.3", 22 | "matplotlib", 23 | "seaborn", 24 | "scanpy>=1.8.1", 25 | "statsmodels>=0.12.2", 26 | "anndata>=0.7.6", 27 | "pandas>=1.1.0", 28 | "scipy>=1.7.1", 29 | "scikit-learn>=1.0.2", 30 | "harmonypy>=0.0.6" 31 | ] 32 | 33 | [project.urls] 34 | "Homepage" = "https://github.com/Janezjz/cellanova" 35 | 36 | --------------------------------------------------------------------------------