├── .gitignore ├── LICENSE ├── README.md ├── blood ├── dahlin18 │ └── dahlin18.ipynb ├── nestorowa16 │ └── nestorowa16.ipynb ├── paul15 │ ├── paul15-subsampled.ipynb │ └── paul15.ipynb └── simulated │ └── simulated.ipynb ├── comparisons ├── nestorowa16_monocle2 │ └── nestorowa16_monocle2.ipynb ├── paul15_monocle2 │ ├── monocle2_alternative.ipynb │ └── monocle2_original.ipynb └── simulated_data │ ├── _exports.ipynb │ ├── dpt.ipynb │ ├── eclair │ ├── README.md │ └── logfile_run_X_krumsiek11_scaled.txt │ ├── monocle2.ipynb │ └── stemID │ ├── README.md │ └── logfile_X_krumsiek11_blobs_shifted.txt ├── connectivity_measure └── connectivity_measure.ipynb ├── deep_learning └── deep_learning.ipynb ├── embedding_quality └── embedding_quality.ipynb ├── planaria ├── planaria.ipynb ├── planaria_paga_velocyto.ipynb └── velocyto_analysis │ └── planaria_velocyto_epidermis.ipynb ├── robustness ├── paul15_robustness.ipynb └── simulated_data_robustness.ipynb └── zebrafish └── zebrafish.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | *.pdf 2 | .DS_Store 3 | .gitignore 4 | LICENSE 5 | blood/.DS_Store 6 | blood/dahlin18/.DS_Store 7 | blood/dahlin18/.ipynb_checkpoints/ 8 | blood/dahlin18/backup/ 9 | blood/dahlin18/data/ 10 | blood/dahlin18/figures/ 11 | blood/dahlin18/write/ 12 | blood/nestorowa16/.DS_Store 13 | blood/nestorowa16/.ipynb_checkpoints/ 14 | blood/nestorowa16/cache/ 15 | blood/nestorowa16/data/ 16 | blood/nestorowa16/figures/ 17 | blood/nestorowa16/gephi_coords_paga.gdf 18 | blood/nestorowa16/write/ 19 | blood/paul15/.DS_Store 20 | blood/paul15/.ipynb_checkpoints/ 21 | blood/paul15/data/ 22 | blood/paul15/figures/ 23 | blood/paul15/write/ 24 | blood/simulated/.DS_Store 25 | blood/simulated/.RData 26 | blood/simulated/.Rhistory 27 | blood/simulated/.ipynb_checkpoints/ 28 | blood/simulated/.scanpy/ 29 | blood/simulated/README.md 30 | blood/simulated/comparisons/ 31 | blood/simulated/comparisons_exports.ipynb 32 | blood/simulated/figures/ 33 | blood/simulated/simulated_robustness.ipynb 34 | blood/simulated/write/ 35 | connectivity_measure/.DS_Store 36 | connectivity_measure/.ipynb_checkpoints/ 37 | connectivity_measure/figures/ 38 | embedding_quality/.DS_Store 39 | embedding_quality/.ipynb_checkpoints/ 40 | embedding_quality/figures/ 41 | embedding_quality/write/ 42 | planaria/.DS_Store 43 | planaria/.ipynb_checkpoints/ 44 | planaria/data/ 45 | planaria/figures/ 46 | planaria/velocyto_analysis/ 47 | planaria/write/ 48 | zebrafish/.DS_Store 49 | zebrafish/.ipynb_checkpoints/ 50 | zebrafish/cache/ 51 | zebrafish/data/ 52 | zebrafish/figures/ 53 | zebrafish/write/ 54 | comparisons/paul15_monocle2/data/ 55 | comparisons/paul15_monocle2/figures/ 56 | comparisons/simulated_data/README.md 57 | comparisons/simulated_data/data/ 58 | comparisons/simulated_data/eclair/ECLAIR_instance/ 59 | comparisons/simulated_data/eclair/X_krumsiek11.txt 60 | comparisons/simulated_data/eclair/X_krumsiek11_blobs.txt 61 | comparisons/simulated_data/eclair/X_krumsiek11_scaled.txt 62 | comparisons/simulated_data/eclair/eclair_plots.ipynb 63 | comparisons/simulated_data/eclair/figures/ 64 | comparisons/simulated_data/figures/ 65 | comparisons/simulated_data/stemID/RaceID3_StemID2_class.R 66 | comparisons/simulated_data/stemID/RaceID3_StemID2_sample.R 67 | comparisons/simulated_data/stemID/Rplots_X_krumsiek11_blobs_shifted_1.png 68 | comparisons/simulated_data/stemID/Rplots_X_krumsiek11_blobs_shifted_2.png 69 | comparisons/simulated_data/stemID/X_krumsiek11_blobs_shifted.csv 70 | comparisons/simulated_data/stemID/X_krumsiek11_shifted.csv 71 | comparisons/simulated_data/write/ 72 | deep_learning/data/ 73 | deep_learning/figures/ 74 | deep_learning/write/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2019, F. Alexander Wolf 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | * Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PAGA - partition-based graph abstraction 2 | 3 | *Mapping out the coarse-grained connectivity structures of complex manifolds [(Genome Biology, 2019)](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x).* 4 | 5 | ![PAGA for hematopoiesis.](http://www.falexwolf.de/img/paga_paul15.png "PAGA for hematopoiesis.") 6 | 7 | PAGA is available within [Scanpy](https://scanpy.readthedocs.io/en/latest/examples.html#trajectory-inference) through: [`tl.paga`](https://scanpy.readthedocs.io/en/latest/api/scanpy.tl.paga.html) | [`pl.paga`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga.html) | [`pl.paga_path`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga_path.html) | [`pl.paga_compare`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga_compare.html). 8 | 9 | Below you find links to all central example notebooks, which also allow reproducing all main figures of the paper. If you start working with PAGA, go through [*blood/paul15*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/paul15/paul15.ipynb). 10 | 11 | notebook | system | details | reference | figure 12 | ---------------| ---------------| ---------| ----------| ------ 13 | [*blood/simulated*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/simulated/simulated.ipynb) | hematopoiesis | simulated | [Krumsiek *et al.*, Plos One (2011)](https://doi.org/10.1371/journal.pone.0022649) | 2a 14 | [*blood/paul15*](https://scanpy-tutorials.readthedocs.io/en/latest/paga-paul15.html) | murine hematopoiesis | 2,730 cells, MARS-seq | [Paul *et al.*, Cell (2015)](https://doi.org/10.1016/j.cell.2015.11.013) | 2b 15 | [*blood/nestorowa16*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/nestorowa16/nestorowa16.ipynb) | murine hematopoiesis | 1,654 cells, Smart-seq2 | [Nestorowa *et al.*, Blood (2016)](https://doi.org/10.1182/blood-2016-05-716480) | 2c 16 | [*blood/dahlin18*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/dahlin18/dahlin18.ipynb) | murine hematopoiesis | 44,802 cells, 10x Genomics | [Dahlin *et al.*, Blood (2018)](https://doi.org/10.1182/blood-2017-12-821413) | 2d 17 | [*planaria*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/planaria/planaria.ipynb) | planaria | 21,612 cells | [Plass *et al.*, Science (2018)](https://doi.org/10.1126/science.aaq1723) | 3 18 | [*zebrafish*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/zebrafish/zebrafish.ipynb) | zebrafish embryo | 53,181 cells | [Wagner *et al.*, Science (2018)](https://doi.org/10.1126/science.aar4362) | 4 19 | [*1M_neurons*](https://github.com/theislab/scanpy_usage/blob/master/170522_visualizing_one_million_cells/logfile_1.3M.txt) | neurons | 1.3 million cells, 10x Genomics | [10x Genomics (2017)](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1M_neurons) | S12 20 | [*deep_learning*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/deep_learning/deep_learning.ipynb) | cycling Jurkat cells | 30,000 single-cell images | [Eulenberg *et al.*, Nat. Commun. (2017)](https://doi.org/10.1038/s41467-017-00623-3) | S14 21 | 22 | All supplemental figures of the paper can be reproduced based on the following table. 23 | 24 | notebook | description | figure 25 | ---------------| ----------| ------ 26 | [*connectivity_measure*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/connectivity_measure/connectivity_measure.ipynb) | connectivity measure | S1, S2, S3 27 | [*robustness*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/robustness) | robustness and multi-resolution capacity | S4, S5 28 | [*comparisons/simulated_data*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/simulated_data) | comparisons for simulated data | S6, S7 29 | [*comparisons/paul15_monocle2*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/paul15_monocle2) | comparison Monocle 2 for Paul *et al.* (2015) | S8 30 | [*comparisons/nestorowa16_monocle2*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/nestorowa16_monocle2) | comparison Monocle 2 for Nestorowa *et al.* (2016) | S9 31 | [*embedding_quality*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/embedding_quality/embedding_quality.ipynb) | quantifying embedding quality | S10 32 | [*simulation*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/simulated/simulated.ipynb) | simulating hematopoiesis | S11 33 | [*1M_neurons*](https://github.com/theislab/scanpy_usage/blob/master/170522_visualizing_one_million_cells/logfile_1.3M.txt) | neurons, 1.3 million cells, 10x Genomics, [10x Genomics (2017)](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1M_neurons) | S12 34 | [*blood/paul15*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/paul15/paul15.ipynb) | annotation of louvain clusters using PAGA | S13 35 | [*deep_learning*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/deep_learning/deep_learning.ipynb) | cycling Jurkat cells, 30,000 single-cell images, [Eulenberg *et al.*, Nat. Commun. (2017)](https://doi.org/10.1038/s41467-017-00623-3) | S14 36 | -------------------------------------------------------------------------------- /blood/paul15/paul15.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "This notebook moved [here](https://scanpy-tutorials.readthedocs.io/en/latest/paga-paul15.html)." 8 | ] 9 | } 10 | ], 11 | "metadata": { 12 | "anaconda-cloud": {}, 13 | "kernelspec": { 14 | "display_name": "Python 3", 15 | "language": "python", 16 | "name": "python3" 17 | }, 18 | "language_info": { 19 | "codemirror_mode": { 20 | "name": "ipython", 21 | "version": 3 22 | }, 23 | "file_extension": ".py", 24 | "mimetype": "text/x-python", 25 | "name": "python", 26 | "nbconvert_exporter": "python", 27 | "pygments_lexer": "ipython3", 28 | "version": "3.6.7" 29 | } 30 | }, 31 | "nbformat": 4, 32 | "nbformat_minor": 2 33 | } 34 | -------------------------------------------------------------------------------- /comparisons/paul15_monocle2/monocle2_alternative.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "*First compiled: September 17, 2017.*" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Monocle 2 for data of [Paul *et al.*, Cell (2015)](https://doi.org/10.1016/j.cell.2015.11.013): alternative preprocessing" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "Here we use an alternative preprocessing as in [*monocle2_original*](monocle2_original.ipynb), which reproduces the notebook that accompanies the Monocle 2 publication of [Qiu *et al.*, Nat. Meth. (2017)](https://doi.org/10.1038/nmeth.4402), available from \n", 22 | "[here](https://github.com/cole-trapnell-lab/monocle2-rge-paper/blob/26ce18c97f22e9488eb85971a23311f5252bfce5/Paul_dataset_analysis_final.ipynb).\n", 23 | "\n", 24 | "Instead of manually removing a cluster of lympoid cells, we keep this cluster. Everything else is exactly as in the original notebook." 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 1, 30 | "metadata": { 31 | "collapsed": false, 32 | "scrolled": true 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "rm(list = ls()) # clear the environment \n", 37 | "options(warn=-1) # turn off warning message globally \n", 38 | "suppressMessages(library(monocle))\n", 39 | "suppressMessages(library(plyr))\n", 40 | "suppressMessages(library(dplyr))" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "# Load the data" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "This RData is from Maren Büttner in the Theis lab https://github.com/theislab/scAnalysisTutorial. It's the same data as the one available within Scanpy via [*sc.examples.paul15()*](https://scanpy.readthedocs.io/en/latest/api/scanpy.api.datasets.paul15.html)." 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 2, 60 | "metadata": { 61 | "collapsed": true 62 | }, 63 | "outputs": [], 64 | "source": [ 65 | "load('./data/Paul_Cell_MARSseq_GSE72857.RData') \n", 66 | "# the following code is used to select feature genes used by Maren \n", 67 | "gene.names <-sapply(strsplit(rownames(data.debatched), \";\"), \"[\", 1)\n", 68 | "is.informative <- gene.names %in% info.genes[order(info.genes)]\n", 69 | "data.info.genes <- data.debatched[is.informative,]\n", 70 | "rownames(data.info.genes) <- gene.names[is.informative]" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "# Create the cell datasets" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "In constrast to the preprocessing as done for Supplemental Figure 16 of [Qiu *et al.*, Nat. Meth. (2017)](https://doi.org/10.1038/nmeth.4402) and as reproduced here in [*monocle2_original*](monocle2_original.ipynb), now, we do not filter out the cluster of lymphoid cells." 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 3, 90 | "metadata": { 91 | "collapsed": false, 92 | "scrolled": true 93 | }, 94 | "outputs": [ 95 | { 96 | "name": "stderr", 97 | "output_type": "stream", 98 | "text": [ 99 | "Removing 23 outliers\n" 100 | ] 101 | }, 102 | { 103 | "data": { 104 | "text/html": [ 105 | "elapsed: 144.463" 106 | ], 107 | "text/latex": [ 108 | "\\textbf{elapsed:} 144.463" 109 | ], 110 | "text/markdown": [ 111 | "**elapsed:** 144.463" 112 | ], 113 | "text/plain": [ 114 | "elapsed \n", 115 | "144.463 " 116 | ] 117 | }, 118 | "metadata": {}, 119 | "output_type": "display_data" 120 | } 121 | ], 122 | "source": [ 123 | "previous_time <- proc.time()[3]\n", 124 | "################################################################################################################################################\n", 125 | "# obtain this mat file from Ido Amit group \n", 126 | "MAP_cells_clusters <- read.csv('./data/MAP.csv', header = F)\n", 127 | "row.names(MAP_cells_clusters) <- MAP_cells_clusters$V1\n", 128 | "\n", 129 | "#filtering cells to include only the ones which were assigned a cluster id: \n", 130 | "valid_subset_GSE72857_exprs <- read.table('./data/GSE72857_umitab.txt', header = T, row.names = 1)\n", 131 | "design_mat <- read.table('./data/GSE72857_experimental_design.txt', header = T, row.names = 1, skip = 19, sep = '\\t')\n", 132 | "design_mat$cluster <- MAP_cells_clusters[row.names(design_mat), 'V2']\n", 133 | "valid_design_mat <- subset(design_mat, !is.na(cluster))\n", 134 | "\n", 135 | "# Get the intersect gene used by Maren Büttner and the genes we have \n", 136 | "common_genes <- rownames(valid_subset_GSE72857_exprs)[rownames(valid_subset_GSE72857_exprs) %in% info.genes]\n", 137 | "fd <- new(\"AnnotatedDataFrame\", data = data.frame(gene_short_name = common_genes, row.names = common_genes))\n", 138 | "pd <- new(\"AnnotatedDataFrame\", data = valid_design_mat)\n", 139 | "\n", 140 | "# create a CDS with data.info.genes \n", 141 | "valid_subset_GSE72857_cds <- newCellDataSet(as(as.matrix(data.info.genes[common_genes, ]), 'sparseMatrix'), \n", 142 | " phenoData = pd, \n", 143 | " featureData = fd,\n", 144 | " lowerDetectionLimit=1,\n", 145 | " expressionFamily=negbinomial.size())\n", 146 | "valid_subset_GSE72857_cds <- estimateSizeFactors(valid_subset_GSE72857_cds)\n", 147 | "valid_subset_GSE72857_cds <- estimateDispersions(valid_subset_GSE72857_cds)\n", 148 | "\n", 149 | "pData(valid_subset_GSE72857_cds)$cell_type <- revalue(as.character(pData(valid_subset_GSE72857_cds)$cluster), \n", 150 | " c(\"1\" = 'erythroid', \"2\" = 'erythroid', \"3\" = 'erythroid', \"4\" = 'erythroid', \"5\" = 'erythroid', \"6\" = 'erythroid', \n", 151 | " \"7\" = 'CMP', \"8\" = 'CMP', \"9\" = 'CMP', \"10\" = 'CMP',\n", 152 | " \"11\" = 'DC', \n", 153 | " \"12\" = 'GMP', \"13\" = 'GMP', \"14\" = 'GMP', \"15\" = 'GMP', \"16\" = 'GMP', \"17\" = 'GMP', \"18\" = 'GMP', \n", 154 | " \"19\" = 'lymphoid'))\n", 155 | "\n", 156 | "# remove all lymphoid cells as they do not belong to myeloid lineage\n", 157 | "# HERE, we commented out the next line; we do NOT remove the lymphoid cells\n", 158 | "# valid_subset_GSE72857_cds <- valid_subset_GSE72857_cds[, pData(valid_subset_GSE72857_cds)$cell_type != 'lymphoid']\n", 159 | "proc.time()[3] - previous_time" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "# Reconstructing the trajectory with Monocle 2 " 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "The variance explained by each component in PCA." 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 4, 179 | "metadata": { 180 | "collapsed": false 181 | }, 182 | "outputs": [ 183 | { 184 | "name": "stderr", 185 | "output_type": "stream", 186 | "text": [ 187 | "Warning message in (function (A, nv = 5, nu = nv, maxit = 100, work = nv + 7, reorth = TRUE, :\n", 188 | "“did not converge--results might be invlaid!; try increasing maxit or fastpath=FALSE”" 189 | ] 190 | }, 191 | { 192 | "data": {}, 193 | "metadata": {}, 194 | "output_type": "display_data" 195 | }, 196 | { 197 | "data": { 198 | "image/png": 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dKSJUseeOCB3r17y2SyUChUW1v7/vvvv/LKK9u3b090lAghlIQYJejhw4cTQoqK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", 199 | "text/plain": [ 200 | "plot without title" 201 | ] 202 | }, 203 | "metadata": {}, 204 | "output_type": "display_data" 205 | } 206 | ], 207 | "source": [ 208 | "options(repr.plot.width=4, repr.plot.height=3)\n", 209 | "plot_pc_variance_explained(valid_subset_GSE72857_cds) + geom_vline(xintercept = 10)" 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "The actual computation." 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": 5, 222 | "metadata": { 223 | "collapsed": false, 224 | "scrolled": false 225 | }, 226 | "outputs": [ 227 | { 228 | "data": { 229 | "text/html": [ 230 | "elapsed: 96.9320000000002" 231 | ], 232 | "text/latex": [ 233 | "\\textbf{elapsed:} 96.9320000000002" 234 | ], 235 | "text/markdown": [ 236 | "**elapsed:** 96.9320000000002" 237 | ], 238 | "text/plain": [ 239 | "elapsed \n", 240 | " 96.932 " 241 | ] 242 | }, 243 | "metadata": {}, 244 | "output_type": "display_data" 245 | } 246 | ], 247 | "source": [ 248 | "previous_time <- proc.time()[3]\n", 249 | "valid_subset_GSE72857_cds2 <- reduceDimension(valid_subset_GSE72857_cds,\n", 250 | " norm_method = 'log', verbose = F, max_components = 10) \n", 251 | "valid_subset_GSE72857_cds2 <- orderCells(valid_subset_GSE72857_cds2, reverse = T)\n", 252 | "proc.time()[3] - previous_time" 253 | ] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "metadata": {}, 258 | "source": [ 259 | "# Similar analysis as for Supplemental Figure 16 of [Qiu *et al.*, Nat. Meth. (2017)](https://doi.org/10.1038/nmeth.4402)" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "## Analogous to Supplemental Figure 16a" 267 | ] 268 | }, 269 | { 270 | "cell_type": "markdown", 271 | "metadata": {}, 272 | "source": [ 273 | "Without removing the lymphoid cells, the figure fundamentally differs from the original [notebook](https://github.com/cole-trapnell-lab/monocle2-rge-paper/blob/26ce18c97f22e9488eb85971a23311f5252bfce5/Paul_dataset_analysis_final.ipynb)." 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 10, 279 | "metadata": { 280 | "collapsed": false 281 | }, 282 | "outputs": [ 283 | { 284 | "data": {}, 285 | "metadata": {}, 286 | "output_type": "display_data" 287 | }, 288 | { 289 | "data": { 290 | "image/png": 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Q1RaN7cDmYGWAoTJk567fV/+fj4PPfcc3bcQBGJkCuAf0GdqMQ71KFwdCJM5oar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291 | "text/plain": [ 292 | "plot without title" 293 | ] 294 | }, 295 | "metadata": {}, 296 | "output_type": "display_data" 297 | } 298 | ], 299 | "source": [ 300 | "detailed_cell_type_color <- c(\"B\" = \"#E088B8\", \"DC\" = \"#46C7EF\", \"Eos\" = \"#EFAD1E\", \"Ery\" = \"#8CB3DF\", \"Mo\" = \"#53C0AD\", \"MEP\" = \"#4EB859\", \"GMP\" = \"#D097C4\", \"MK\" = \"#ACC436\", \"Neu\" = \"#F5918A\", \"lymphoid\" = \"#FF0000\")\n", 301 | "\n", 302 | "pData(valid_subset_GSE72857_cds2)$cell_type2 <- revalue(as.character(pData(valid_subset_GSE72857_cds2)$cluster), \n", 303 | " c(\"1\" = 'Ery', \"2\" = 'Ery', \"3\" = 'Ery', \"4\" = 'Ery', \"5\" = 'Ery', \"6\" = 'Ery', \n", 304 | " \"7\" = 'MEP', \"8\" = 'MK', \"9\" = 'GMP', \"10\" = 'GMP',\n", 305 | " \"11\" = 'DC', \n", 306 | " \"12\" = 'B', \"13\" = 'B', \"14\" = 'Mo', \"15\" = 'Mo', \"16\" = 'Neu', \"17\" = 'Neu', \"18\" = 'Eos', \n", 307 | " \"19\" = 'lymphoid'))\n", 308 | "\n", 309 | "options(repr.plot.width=3, repr.plot.height=4)\n", 310 | "plot_complex_cell_trajectory(valid_subset_GSE72857_cds2, color_by = 'State', show_branch_points = T, \n", 311 | " cell_size = 0.5, cell_link_size = 0.3, root_states = c(15))" 312 | ] 313 | }, 314 | { 315 | "cell_type": "markdown", 316 | "metadata": {}, 317 | "source": [ 318 | "## Analogous to Supplemental Figure 16b" 319 | ] 320 | }, 321 | { 322 | "cell_type": "markdown", 323 | "metadata": {}, 324 | "source": [ 325 | "Without removing the lymphoid cells, the figure fundamentally differs from the original [notebook](https://github.com/cole-trapnell-lab/monocle2-rge-paper/blob/26ce18c97f22e9488eb85971a23311f5252bfce5/Paul_dataset_analysis_final.ipynb)." 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": 11, 331 | "metadata": { 332 | "collapsed": false 333 | }, 334 | "outputs": [ 335 | { 336 | "data": {}, 337 | "metadata": {}, 338 | "output_type": "display_data" 339 | }, 340 | { 341 | "data": { 342 | "image/png": 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4Gz06/6qqtg375IDWo/UGX5rsN3QpEWm3THFEkDRni/p/TtAxhRXcun4vphr9gV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343 | "text/plain": [ 344 | "plot without title" 345 | ] 346 | }, 347 | "metadata": {}, 348 | "output_type": "display_data" 349 | } 350 | ], 351 | "source": [ 352 | "options(repr.plot.width=4, repr.plot.height=3)\n", 353 | "plot_complex_cell_trajectory(valid_subset_GSE72857_cds2, color_by = 'as.factor(cell_type2)', show_branch_points = T, \n", 354 | " cell_size = 0.5, cell_link_size = 0.3, root_states = c(15)) + scale_size(range = c(0.2, 0.2)) +\n", 355 | " theme(axis.text.x = element_text(angle = 30, hjust = 1)) +\n", 356 | " theme (legend.position=\"left\", legend.title=element_blank()) + scale_color_manual(values = detailed_cell_type_color)" 357 | ] 358 | }, 359 | { 360 | "cell_type": "markdown", 361 | "metadata": {}, 362 | "source": [ 363 | "# Session Info" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": 8, 369 | "metadata": { 370 | "collapsed": false 371 | }, 372 | "outputs": [ 373 | { 374 | "data": { 375 | "text/plain": [ 376 | "R version 3.3.2 (2016-10-31)\n", 377 | "Platform: x86_64-apple-darwin13.4.0 (64-bit)\n", 378 | "Running under: macOS Sierra 10.12.5\n", 379 | "\n", 380 | "locale:\n", 381 | "[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8\n", 382 | "\n", 383 | "attached base packages:\n", 384 | " [1] splines stats4 parallel stats graphics grDevices utils \n", 385 | " [8] datasets methods base \n", 386 | "\n", 387 | "other attached packages:\n", 388 | " [1] destiny_2.0.8 dplyr_0.7.1 plyr_1.8.4 \n", 389 | " [4] monocle_2.2.0 L1Graph_0.1.0 lpSolveAPI_5.5.2.0-17\n", 390 | " [7] simplePPT_0.1.0 igraph_1.0.1 DDRTree_0.1.5 \n", 391 | "[10] irlba_2.2.1 VGAM_1.0-3 ggplot2_2.2.1 \n", 392 | "[13] Biobase_2.34.0 BiocGenerics_0.20.0 Matrix_1.2-10 \n", 393 | "\n", 394 | "loaded via a namespace (and not attached):\n", 395 | " [1] nlme_3.1-131 matrixStats_0.52.2 pbkrtest_0.4-7 \n", 396 | " [4] xts_0.9-7 RColorBrewer_1.1-2 repr_0.12.0 \n", 397 | " [7] tools_3.3.2 backports_1.1.0 R6_2.2.2 \n", 398 | "[10] rpart_4.1-11 Hmisc_4.0-3 lazyeval_0.2.0 \n", 399 | "[13] mgcv_1.8-17 colorspace_1.3-2 nnet_7.3-12 \n", 400 | "[16] sp_1.2-5 smoother_1.1 gridExtra_2.2.1 \n", 401 | "[19] quantreg_5.33 htmlTable_1.9 Cairo_1.5-9 \n", 402 | "[22] SparseM_1.77 labeling_0.3 slam_0.1-40 \n", 403 | "[25] scales_0.4.1 checkmate_1.8.2 DEoptimR_1.0-8 \n", 404 | "[28] lmtest_0.9-35 robustbase_0.92-7 proxy_0.4-17 \n", 405 | "[31] pbdZMQ_0.2-6 stringr_1.2.0 digest_0.6.12 \n", 406 | "[34] foreign_0.8-69 minqa_1.2.4 base64enc_0.1-3 \n", 407 | "[37] pkgconfig_2.0.1 htmltools_0.3.6 lme4_1.1-13 \n", 408 | "[40] limma_3.30.13 TTR_0.23-1 htmlwidgets_0.8 \n", 409 | "[43] rlang_0.1.1 FNN_1.1 bindr_0.1 \n", 410 | "[46] zoo_1.8-0 combinat_0.0-8 jsonlite_1.5 \n", 411 | "[49] acepack_1.4.1 car_2.1-4 magrittr_1.5 \n", 412 | "[52] Formula_1.2-1 Rcpp_0.12.11 IRkernel_0.8.6.9000 \n", 413 | "[55] munsell_0.4.3 scatterplot3d_0.3-40 stringi_1.1.5 \n", 414 | "[58] MASS_7.3-47 Rtsne_0.13 grid_3.3.2 \n", 415 | "[61] crayon_1.3.2 lattice_0.20-35 IRdisplay_0.4.4 \n", 416 | "[64] knitr_1.16 uuid_0.1-2 boot_1.3-19 \n", 417 | "[67] reshape2_1.4.2 glue_1.1.1 evaluate_0.10.1 \n", 418 | "[70] latticeExtra_0.6-28 data.table_1.10.4 laeken_0.4.6 \n", 419 | "[73] vcd_1.4-3 nloptr_1.0.4 MatrixModels_0.4-1 \n", 420 | "[76] VIM_4.7.0 gtable_0.2.0 assertthat_0.2.0 \n", 421 | "[79] RcppEigen_0.3.3.3.0 e1071_1.6-8 class_7.3-14 \n", 422 | "[82] survival_2.41-3 qlcMatrix_0.9.5 HSMMSingleCell_0.108.0\n", 423 | "[85] tibble_1.3.3 pheatmap_1.0.8 bindrcpp_0.2 \n", 424 | "[88] cluster_2.0.6 fastICA_1.2-1 densityClust_0.2.1 " 425 | ] 426 | }, 427 | "metadata": {}, 428 | "output_type": "display_data" 429 | } 430 | ], 431 | "source": [ 432 | "sessionInfo()" 433 | ] 434 | } 435 | ], 436 | "metadata": { 437 | "anaconda-cloud": {}, 438 | "kernelspec": { 439 | "display_name": "R", 440 | "language": "R", 441 | "name": "ir" 442 | }, 443 | "language_info": { 444 | "codemirror_mode": "r", 445 | "file_extension": ".r", 446 | "mimetype": "text/x-r-source", 447 | "name": "R", 448 | "pygments_lexer": "r", 449 | "version": "3.3.2" 450 | } 451 | }, 452 | "nbformat": 4, 453 | "nbformat_minor": 1 454 | } 455 | -------------------------------------------------------------------------------- /comparisons/simulated_data/eclair/README.md: -------------------------------------------------------------------------------- 1 | # Comparison with ECLAIR [(Giecold *et al.*, Nucl. Acids Research, 2016)](https://doi.org/10.1093/nar/gkw452) 2 | 3 | ## Contents 4 | 5 | * [*eclair_plots*](eclair_plots.ipynb) visualizes the results of running eclair 6 | * [*logfile_run_X_krumsiek11_scaled.txt*](logfile_run_X_krumsiek11_scaled.txt) shows the logging output and the parameters that allow to reproduce the results discussed [here](../) 7 | * [*X_krumsiek11_scaled.txt*](X_krumsiek11_scaled.txt) contains the scaled data matrix for the simple tree, [*X_krumsiek11.txt*](X_krumsiek11.txt) contains the equivalent unscaled matrix and [*X_krumsiek11_blobs.txt*](X_krumsiek11_blobs.txt) contains the data describing tree and clusters; all in tab-separated format as required by ECLAIR. These files have been generated in [*../../comparisons_exports*](../../comparisons_exports.ipynb), which also provides visualizations of the data. 8 | 9 | ## Notes 10 | 11 | * See the discussion of the results [here](../). 12 | 13 | * We acknowledge help by G. Giecold and S. P. Garcia, who adviced to scale the data matrix and provided default parameters. Parst of the correspondence are archived here: https://github.com/GGiecold/ECLAIR/issues/3 14 | 15 | * The algorithm could not handle the unnormalized data matrices [*X_krumsiek11.txt*](X_krumsiek11.txt) and [*X_krumsiek11_blobs.txt*](X_krumsiek11_blobs.txt). 16 | -------------------------------------------------------------------------------- /comparisons/simulated_data/eclair/logfile_run_X_krumsiek11_scaled.txt: -------------------------------------------------------------------------------- 1 | (py27) Alexs-MBP:eclair alexwolf$ python -m ECLAIR.Build_instance 2 | 3 | ***************************************** 4 | ***************************************** 5 | *** ECLAIR *** 6 | ***************************************** 7 | ***************************************** 8 | 9 | 10 | ECLAIR: provide the path to the file holding the data to be analyzed: 11 | ./X_krumsiek11_scaled.txt 12 | 13 | ECLAIR: how may rows count as header in this file? Enter '0' if the file is not adorned by any header: 14 | 1 15 | 16 | ECLAIR: which column of the data-file holds the names, tags or IDs of its samples? Enter '0' for the 1st column, '1' for the second, etc.: 17 | 0 18 | 19 | ECLAIR: does this data-set include some time information? [Y/n] 20 | n 21 | 22 | ECLAIR: you may choose to exclude some columns as features. If this option does not apply, simply press 'Enter'. Otherwise, provide a list of numbers: 23 | 24 | 25 | ECLAIR: please give an estimate of the number of samples in this data-set: 26 | 600 27 | 28 | ECLAIR: please enter the number of trees that will be bagged into a forest (a value of '50' is recommended): 29 | 50 30 | 31 | ECLAIR: how many points do you want to sample from the dataset? Please provide a fraction of the total number of cells: 32 | 0.8 33 | 34 | ECLAIR: choose the clustering algorithm to be applied to each of 50 subsamples from your data-set. 35 | Available methods: affinity propagation (1), DBSCAN (2), hierarchical clustering (3) & k-means (4) 36 | 37 | 4 38 | 39 | ECLAIR: how many centroids to generate for each run of k-means clustering? 40 | 5 41 | 42 | ECLAIR: the total number of consensus clusters defaults to the highest number of clusters encountered in each of the 50 independent runs of subsamplings and clusterings. Do you want to provide a value instead? [Y/n] 43 | n 44 | 45 | 46 | 47 | ECLAIR INFO 2017-09-15 09:09:31: ready to proceed! 48 | 49 | 50 | 51 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 1. 52 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0896 seconds. 53 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 2. 54 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0386 seconds. 55 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 3. 56 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.046 seconds. 57 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 4. 58 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0503 seconds. 59 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 5. 60 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0482 seconds. 61 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 6. 62 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0516 seconds. 63 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 7. 64 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0496 seconds. 65 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 8. 66 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0489 seconds. 67 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 9. 68 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0468 seconds. 69 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 10. 70 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0479 seconds. 71 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 11. 72 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0498 seconds. 73 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 12. 74 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.048 seconds. 75 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 13. 76 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0505 seconds. 77 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 14. 78 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0546 seconds. 79 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 15. 80 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0499 seconds. 81 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 16. 82 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 83 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 17. 84 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.044 seconds. 85 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 18. 86 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0456 seconds. 87 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 19. 88 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0472 seconds. 89 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 20. 90 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0508 seconds. 91 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 21. 92 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0442 seconds. 93 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 22. 94 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0487 seconds. 95 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 23. 96 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 97 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 24. 98 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 99 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 25. 100 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0454 seconds. 101 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 26. 102 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0485 seconds. 103 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 27. 104 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.048 seconds. 105 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 28. 106 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0515 seconds. 107 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 29. 108 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0501 seconds. 109 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 30. 110 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0459 seconds. 111 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 31. 112 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.046 seconds. 113 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 32. 114 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0423 seconds. 115 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 33. 116 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0441 seconds. 117 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 34. 118 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.047 seconds. 119 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 35. 120 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 121 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 36. 122 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0509 seconds. 123 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 37. 124 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.051 seconds. 125 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 38. 126 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0536 seconds. 127 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 39. 128 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0467 seconds. 129 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 40. 130 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0459 seconds. 131 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 41. 132 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0487 seconds. 133 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 42. 134 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0511 seconds. 135 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 43. 136 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.047 seconds. 137 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 44. 138 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 139 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 45. 140 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0428 seconds. 141 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 46. 142 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0444 seconds. 143 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 47. 144 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0469 seconds. 145 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 48. 146 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0524 seconds. 147 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 49. 148 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 149 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 50. 150 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0502 seconds. 151 | ***** 152 | INFO: Cluster_Ensembles: CSPA: consensus clustering using CSPA. 153 | 154 | # 155 | INFO: Cluster_Ensembles: wgraph: writing wgraph_CSPA. 156 | # 157 | 158 | # 159 | INFO: Cluster_Ensembles: sgraph: calling gpmetis for graph partitioning. 160 | ****************************************************************************** 161 | METIS 5.0 Copyright 1998-13, Regents of the University of Minnesota 162 | (HEAD: , Built on: Sep 15 2017, 09:03:02) 163 | size of idx_t: 32bits, real_t: 32bits, idx_t *: 64bits 164 | 165 | Graph Information ----------------------------------------------------------- 166 | Name: ./wgraph_CSPA, #Vertices: 640, #Edges: 48338, #Parts: 5 167 | 168 | Options --------------------------------------------------------------------- 169 | ptype=kway, objtype=cut, ctype=shem, rtype=greedy, iptype=metisrb 170 | dbglvl=0, ufactor=1.030, no2hop=NO, minconn=NO, contig=NO, nooutput=NO 171 | seed=-1, niter=10, ncuts=1 172 | 173 | Direct k-way Partitioning --------------------------------------------------- 174 | - Edgecut: 16419216, communication volume: 405. 175 | 176 | - Balance: 177 | constraint #0: 1.023 out of 0.008 178 | 179 | - Most overweight partition: 180 | pid: 4, actual: 131, desired: 128, ratio: 1.02. 181 | 182 | - Subdomain connectivity: max: 3, min: 0, avg: 1.60 183 | 184 | - The original graph had 5 connected components and the resulting 185 | partitioning after removing the cut edges has 8 components. 186 | Timing Information ---------------------------------------------------------- 187 | I/O: 0.008 sec 188 | Partitioning: 0.003 sec (METIS time) 189 | Reporting: 0.001 sec 190 | 191 | Memory Information ---------------------------------------------------------- 192 | Max memory used: 0.963 MB 193 | ****************************************************************************** 194 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_CSPA.part.5 195 | # 196 | 197 | INFO: Cluster_Ensembles: cluster_ensembles: CSPA at 0.776945860086. 198 | ***** 199 | 200 | ***** 201 | INFO: Cluster_Ensembles: HGPA: consensus clustering using HGPA. 202 | 203 | # 204 | INFO: Cluster_Ensembles: wgraph: writing wgraph_HGPA. 205 | INFO: Cluster_Ensembles: wgraph: 640 vertices and 250 non-zero hyper-edges. 206 | # 207 | 208 | # 209 | INFO: Cluster_Ensembles: sgraph: calling shmetis for hypergraph partitioning. 210 | ******************************************************************************* 211 | HMETIS 1.5.3 Copyright 1998, Regents of the University of Minnesota 212 | 213 | HyperGraph Information ----------------------------------------------------- 214 | Name: ./wgraph_HGPA, #Vtxs: 640, #Hedges: 250, #Parts: 5, UBfactor: 0.15 215 | Options: HFC, FM, Reconst-False, V-cycles @ End, No Fixed Vertices 216 | 217 | Recursive Partitioning... -------------------------------------------------- 218 | 219 | Bisecting a hgraph of size [vertices=640, hedges=250, balance=0.40] 220 | The mincut for this bisection = 32000, (average = 32000.0) (balance = 0.50) 221 | 222 | Bisecting a hgraph of size [vertices=320, hedges=0, balance=0.50] 223 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 224 | 225 | Bisecting a hgraph of size [vertices=320, hedges=0, balance=0.33] 226 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 227 | 228 | Bisecting a hgraph of size [vertices=160, hedges=0, balance=0.50] 229 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 230 | 231 | -------------------------------------------------------------------------- 232 | Summary for the 5-way partition: 233 | Hyperedge Cut: 32000 (minimize) 234 | Sum of External Degrees: 160000 (minimize) 235 | Scaled Cost: 5.47e-01 (minimize) 236 | Absorption: 240.97 (maximize) 237 | 238 | Partition Sizes & External Degrees: 239 | 160[32000] 160[32000] 160[32000] 80[32000] 80[32000] 240 | 241 | 242 | Timing Information --------------------------------------------------------- 243 | Partitioning Time: 0.004sec 244 | I/O Time: 0.004sec 245 | ******************************************************************************* 246 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_HGPA.part.5 247 | # 248 | 249 | INFO: Cluster_Ensembles: cluster_ensembles: HGPA at 0.000178067785293. 250 | ***** 251 | 252 | ***** 253 | INFO: Cluster_Ensembles: MCLA: consensus clustering using MCLA. 254 | INFO: Cluster_Ensembles: MCLA: preparing graph for meta-clustering. 255 | INFO: Cluster_Ensembles: MCLA: done filling hypergraph adjacency matrix. Starting computation of Jaccard similarity matrix. 256 | INFO: Cluster_Ensembles: MCLA: starting computation of Jaccard similarity matrix. 257 | INFO: Cluster_Ensembles: MCLA: done computing the matrix of pairwise Jaccard similarity scores. 258 | 259 | # 260 | INFO: Cluster_Ensembles: wgraph: writing wgraph_MCLA. 261 | # 262 | 263 | # 264 | INFO: Cluster_Ensembles: sgraph: calling gpmetis for graph partitioning. 265 | ****************************************************************************** 266 | METIS 5.0 Copyright 1998-13, Regents of the University of Minnesota 267 | (HEAD: , Built on: Sep 15 2017, 09:03:02) 268 | size of idx_t: 32bits, real_t: 32bits, idx_t *: 64bits 269 | 270 | Graph Information ----------------------------------------------------------- 271 | Name: ./wgraph_MCLA, #Vertices: 250, #Edges: 6125, #Parts: 5 272 | 273 | Options --------------------------------------------------------------------- 274 | ptype=kway, objtype=cut, ctype=shem, rtype=greedy, iptype=metisrb 275 | dbglvl=0, ufactor=1.030, no2hop=NO, minconn=NO, contig=NO, nooutput=NO 276 | seed=-1, niter=10, ncuts=1 277 | 278 | Direct k-way Partitioning --------------------------------------------------- 279 | - Edgecut: 139000, communication volume: 150. 280 | 281 | - Balance: 282 | constraint #0: 1.020 out of 0.037 283 | 284 | - Most overweight partition: 285 | pid: 0, actual: 652500, desired: 640000, ratio: 1.02. 286 | 287 | - Subdomain connectivity: max: 3, min: 0, avg: 1.60 288 | 289 | - The original graph had 5 connected components and the resulting 290 | partitioning after removing the cut edges has 8 components. 291 | Timing Information ---------------------------------------------------------- 292 | I/O: 0.001 sec 293 | Partitioning: 0.001 sec (METIS time) 294 | Reporting: 0.000 sec 295 | 296 | Memory Information ---------------------------------------------------------- 297 | Max memory used: 0.232 MB 298 | ****************************************************************************** 299 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_MCLA.part.5 300 | # 301 | INFO: Cluster_Ensembles: MCLA: delivering 4 clusters. 302 | INFO: Cluster_Ensembles: MCLA: average posterior probability is 0.360500625232 303 | 304 | INFO: Cluster_Ensembles: cluster_ensembles: MCLA at 0.935544511804. 305 | ***** 306 | 307 | ECLAIR INFO 2017-09-15 09:09:35: done with computing the means and variances for the pair of distances along each minimum spanning tree between the groups of cells underlying each cluster of the ensemble clustering. The full probability distributions are available and stored in HDF5 format and available for further analysis. 308 | 309 | ECLAIR INFO 2017-09-15 09:09:35: the various runs of subsampling and clustering took 2.5613 seconds, ensemble clustering took 1.0971 seconds, while calculating the distribution of pairwise distances took 0.4174 seconds. The whole process lasted 4.5862 seconds 310 | 311 | ***************************************** 312 | ***************************************** -------------------------------------------------------------------------------- /comparisons/simulated_data/stemID/README.md: -------------------------------------------------------------------------------- 1 | # Comparison with stemID 2 2 | 3 | Reproduce the comparison with stemID 2, the successor of stemID [(Grün *et al.*, Cell. Stem Cell, 4 | 2016)](https://doi.org/10.1016/j.stem.2016.05.010), 5 | by running [*RaceID3_StemID2_sample.R*](RaceID3_StemID2_sample.R). 6 | ``` 7 | time Rscript RaceID3_StemID2_sample.R 8 | ``` 9 | See [*logfile_X_krumsiek11_blobs_shifted.txt*](*logfile_X_krumsiek11_blobs_shifted.txt*) for the logging output that we produced. 10 | 11 | We acknowledge D. Grün for providing the code via Email; parameters were chosen 12 | by D. Grün so that stemID would produce sensible results. The code is an 13 | improved version of the original stemID, which is available from https://github.com/dgrun/StemID/. 14 | 15 | We note that we also tried running the code on the simpler data file *X_krumsiek11_shifted.csv* with the same parameters, but were not able to do so as this resulted in errors. -------------------------------------------------------------------------------- /comparisons/simulated_data/stemID/logfile_X_krumsiek11_blobs_shifted.txt: -------------------------------------------------------------------------------- 1 | (py35) Alexs-MBP:stemID alexwolf$ time Rscript RaceID3_StemID2_sample.R 2 | Loading required package: tsne 3 | Loading required package: pheatmap 4 | Loading required package: MASS 5 | Loading required package: cluster 6 | Loading required package: mclust 7 | Package 'mclust' version 5.3 8 | Type 'citation("mclust")' for citing this R package in publications. 9 | Loading required package: flexmix 10 | Loading required package: lattice 11 | Loading required package: fpc 12 | Loading required package: amap 13 | Loading required package: RColorBrewer 14 | Loading required package: locfit 15 | locfit 1.5-9.1 2013-03-22 16 | Loading required package: vegan 17 | Loading required package: permute 18 | This is vegan 2.4-4 19 | Loading required package: Rtsne 20 | Loading required package: scran 21 | Loading required package: BiocParallel 22 | Loading required package: methods 23 | Loading required package: scater 24 | Loading required package: Biobase 25 | Loading required package: BiocGenerics 26 | Loading required package: parallel 27 | 28 | Attaching package: ‘BiocGenerics’ 29 | 30 | The following objects are masked from ‘package:parallel’: 31 | 32 | clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, 33 | clusterExport, clusterMap, parApply, parCapply, parLapply, 34 | parLapplyLB, parRapply, parSapply, parSapplyLB 35 | 36 | The following object is masked from ‘package:flexmix’: 37 | 38 | unique 39 | 40 | The following objects are masked from ‘package:stats’: 41 | 42 | IQR, mad, xtabs 43 | 44 | The following objects are masked from ‘package:base’: 45 | 46 | anyDuplicated, append, as.data.frame, cbind, colnames, do.call, 47 | duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, 48 | is.unsorted, lapply, lengths, Map, mapply, match, mget, order, 49 | paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, 50 | Reduce, rownames, sapply, setdiff, sort, table, tapply, union, 51 | unique, unsplit, which, which.max, which.min 52 | 53 | Welcome to Bioconductor 54 | 55 | Vignettes contain introductory material; view with 56 | 'browseVignettes()'. To cite Bioconductor, see 57 | 'citation("Biobase")', and for packages 'citation("pkgname")'. 58 | 59 | Loading required package: ggplot2 60 | 61 | Attaching package: ‘scater’ 62 | 63 | The following object is masked from ‘package:stats’: 64 | 65 | filter 66 | 67 | Loading required package: DESeq2 68 | Loading required package: S4Vectors 69 | Loading required package: stats4 70 | 71 | Attaching package: ‘S4Vectors’ 72 | 73 | The following object is masked from ‘package:scater’: 74 | 75 | rename 76 | 77 | The following objects are masked from ‘package:base’: 78 | 79 | colMeans, colSums, expand.grid, rowMeans, rowSums 80 | 81 | Loading required package: IRanges 82 | Loading required package: GenomicRanges 83 | Loading required package: GenomeInfoDb 84 | Loading required package: SummarizedExperiment 85 | 86 | Attaching package: ‘DESeq2’ 87 | 88 | The following object is masked from ‘package:scater’: 89 | 90 | fpkm 91 | 92 | Loading required package: randomForest 93 | randomForest 4.6-12 94 | Type rfNews() to see new features/changes/bug fixes. 95 | 96 | Attaching package: ‘randomForest’ 97 | 98 | The following object is masked from ‘package:ggplot2’: 99 | 100 | margin 101 | 102 | The following object is masked from ‘package:Biobase’: 103 | 104 | combine 105 | 106 | The following object is masked from ‘package:BiocGenerics’: 107 | 108 | combine 109 | 110 | Warning messages: 111 | 1: package ‘BiocParallel’ was built under R version 3.3.3 112 | 2: package ‘S4Vectors’ was built under R version 3.3.3 113 | 3: package ‘IRanges’ was built under R version 3.3.3 114 | 4: package ‘GenomicRanges’ was built under R version 3.3.3 115 | k = 1 116 | k = 2 117 | k = 3 118 | k = 4 119 | k = 5 120 | k = 6 121 | k = 7 122 | k = 8 123 | k = 9 124 | k = 10 125 | k = 11 126 | k = 12 127 | k = 13 128 | k = 14 129 | k = 15 130 | k = 16 131 | k = 17 132 | k = 18 133 | k = 19 134 | k = 20 135 | k = 21 136 | k = 22 137 | k = 23 138 | k = 24 139 | k = 25 140 | k = 26 141 | k = 27 142 | k = 28 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1985 | pdishuffle: 1791 1986 | pdishuffle: 1792 1987 | pdishuffle: 1793 1988 | pdishuffle: 1794 1989 | pdishuffle: 1795 1990 | pdishuffle: 1796 1991 | pdishuffle: 1797 1992 | pdishuffle: 1798 1993 | pdishuffle: 1799 1994 | pdishuffle: 1800 1995 | pdishuffle: 1801 1996 | pdishuffle: 1802 1997 | pdishuffle: 1803 1998 | pdishuffle: 1804 1999 | pdishuffle: 1805 2000 | pdishuffle: 1806 2001 | pdishuffle: 1807 2002 | pdishuffle: 1808 2003 | pdishuffle: 1809 2004 | pdishuffle: 1810 2005 | pdishuffle: 1811 2006 | pdishuffle: 1812 2007 | pdishuffle: 1813 2008 | pdishuffle: 1814 2009 | pdishuffle: 1815 2010 | pdishuffle: 1816 2011 | pdishuffle: 1817 2012 | pdishuffle: 1818 2013 | pdishuffle: 1819 2014 | pdishuffle: 1820 2015 | pdishuffle: 1821 2016 | pdishuffle: 1822 2017 | pdishuffle: 1823 2018 | pdishuffle: 1824 2019 | pdishuffle: 1825 2020 | pdishuffle: 1826 2021 | pdishuffle: 1827 2022 | pdishuffle: 1828 2023 | pdishuffle: 1829 2024 | pdishuffle: 1830 2025 | pdishuffle: 1831 2026 | 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2068 | pdishuffle: 1874 2069 | pdishuffle: 1875 2070 | pdishuffle: 1876 2071 | pdishuffle: 1877 2072 | pdishuffle: 1878 2073 | pdishuffle: 1879 2074 | pdishuffle: 1880 2075 | pdishuffle: 1881 2076 | pdishuffle: 1882 2077 | pdishuffle: 1883 2078 | pdishuffle: 1884 2079 | pdishuffle: 1885 2080 | pdishuffle: 1886 2081 | pdishuffle: 1887 2082 | pdishuffle: 1888 2083 | pdishuffle: 1889 2084 | pdishuffle: 1890 2085 | pdishuffle: 1891 2086 | pdishuffle: 1892 2087 | pdishuffle: 1893 2088 | pdishuffle: 1894 2089 | pdishuffle: 1895 2090 | pdishuffle: 1896 2091 | pdishuffle: 1897 2092 | pdishuffle: 1898 2093 | pdishuffle: 1899 2094 | pdishuffle: 1900 2095 | pdishuffle: 1901 2096 | pdishuffle: 1902 2097 | pdishuffle: 1903 2098 | pdishuffle: 1904 2099 | pdishuffle: 1905 2100 | pdishuffle: 1906 2101 | pdishuffle: 1907 2102 | pdishuffle: 1908 2103 | pdishuffle: 1909 2104 | pdishuffle: 1910 2105 | pdishuffle: 1911 2106 | pdishuffle: 1912 2107 | pdishuffle: 1913 2108 | pdishuffle: 1914 2109 | pdishuffle: 1915 2110 | pdishuffle: 1916 2111 | pdishuffle: 1917 2112 | pdishuffle: 1918 2113 | pdishuffle: 1919 2114 | pdishuffle: 1920 2115 | pdishuffle: 1921 2116 | pdishuffle: 1922 2117 | pdishuffle: 1923 2118 | pdishuffle: 1924 2119 | pdishuffle: 1925 2120 | pdishuffle: 1926 2121 | pdishuffle: 1927 2122 | pdishuffle: 1928 2123 | pdishuffle: 1929 2124 | pdishuffle: 1930 2125 | pdishuffle: 1931 2126 | pdishuffle: 1932 2127 | pdishuffle: 1933 2128 | pdishuffle: 1934 2129 | pdishuffle: 1935 2130 | pdishuffle: 1936 2131 | pdishuffle: 1937 2132 | pdishuffle: 1938 2133 | pdishuffle: 1939 2134 | pdishuffle: 1940 2135 | pdishuffle: 1941 2136 | pdishuffle: 1942 2137 | pdishuffle: 1943 2138 | pdishuffle: 1944 2139 | pdishuffle: 1945 2140 | pdishuffle: 1946 2141 | pdishuffle: 1947 2142 | pdishuffle: 1948 2143 | pdishuffle: 1949 2144 | pdishuffle: 1950 2145 | pdishuffle: 1951 2146 | pdishuffle: 1952 2147 | pdishuffle: 1953 2148 | pdishuffle: 1954 2149 | pdishuffle: 1955 2150 | pdishuffle: 1956 2151 | pdishuffle: 1957 2152 | pdishuffle: 1958 2153 | pdishuffle: 1959 2154 | pdishuffle: 1960 2155 | pdishuffle: 1961 2156 | pdishuffle: 1962 2157 | pdishuffle: 1963 2158 | pdishuffle: 1964 2159 | pdishuffle: 1965 2160 | pdishuffle: 1966 2161 | pdishuffle: 1967 2162 | pdishuffle: 1968 2163 | pdishuffle: 1969 2164 | pdishuffle: 1970 2165 | pdishuffle: 1971 2166 | pdishuffle: 1972 2167 | pdishuffle: 1973 2168 | pdishuffle: 1974 2169 | pdishuffle: 1975 2170 | pdishuffle: 1976 2171 | pdishuffle: 1977 2172 | pdishuffle: 1978 2173 | pdishuffle: 1979 2174 | pdishuffle: 1980 2175 | pdishuffle: 1981 2176 | pdishuffle: 1982 2177 | pdishuffle: 1983 2178 | pdishuffle: 1984 2179 | pdishuffle: 1985 2180 | pdishuffle: 1986 2181 | pdishuffle: 1987 2182 | pdishuffle: 1988 2183 | pdishuffle: 1989 2184 | pdishuffle: 1990 2185 | pdishuffle: 1991 2186 | pdishuffle: 1992 2187 | pdishuffle: 1993 2188 | pdishuffle: 1994 2189 | pdishuffle: 1995 2190 | pdishuffle: 1996 2191 | pdishuffle: 1997 2192 | pdishuffle: 1998 2193 | pdishuffle: 1999 2194 | pdishuffle: 2000 2195 | 2196 | real 17m40.559s 2197 | user 16m16.743s 2198 | sys 1m12.594s --------------------------------------------------------------------------------