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Seurat objects - conjunctiva single-cell atlas

We provide here the Seurat objects for: 1) the whole conjunctiva atlas 2) Goblet cells subclusters 3) tuft cells subclusters 4) basal cells subclusters. All objects can be obtained with the code provided on Github.

opencc-by-4.0Nov 2023View details →

Datasets for the study "A Pan-Cancer Single Cell Panorama of Human Natural Killer Cells"

File "comb_CD56_CD16_NK.h5ad" contains processed expression data and raw count data used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".File "comb_CD56_CD16_NK_blood.h5ad" contains processed expression data used in the analysis of circulating NK cells and annotations such as "meta_tissue" and "cellType".File "Integrated_CD16_NK cells.h5ad" contains processed expression data of CD56dimCD16hi NK cells used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".File "Integrated_CD56_NK cells.h5ad" contains processed expression data of CD56brightCD16lo NK cells used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".

opencc-by-4.0Nov 2023View details →

Integrated single-cell RNA-sequencing data of unwounded and wounded mouse skin and fibroblasts.

This repository contains the .h5ad files that store the integrated scRNA-seq data we generated for the work, Almet et al. (2023), "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing", to be published in the Journal of Investigative Dermatology.The integrated* files contain both raw counts, normalized counts, as well as unspliced and spliced count estimates that were obtained using kallisto|bustools and velocyto. We integrated the data from the following published datasets:<ol><li><a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>: Unwounded P21 mice and small wound P21 + 7 mice</li><li><a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020)</a>: Unwounded P49 mice and small wound P49 + 4 mice</li><li><a href="https://doi.org/10.1038/s41467-018-08247-x)">Guerrero-Juarez et al. (2019)</a>: Large wound day 12 mice</li><li><a href="https://doi.org/10.1016/j.stem.2020.07.008">Abbasi et al. (2020)</a>: Large wound day 14 mice</li><li><a href="https://doi.org/10.1126/sciadv.aay3704">Gay et al. (2020)</a>: Large wound fibrotic (hairless) and regenerative (hair follicle neogenesis) day 18 mice</li></ol>The unwounded_* files were used to briefly integrated unwounded skin scRNA-seq from mouse models of different ages that have been used to analyze wound healing in <a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020),</a> <a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>, and <a href="https://doi.org/10.1016/j.celrep.2022.111155">Vu et al. (2022)</a>, which generated scRNA-seq for unwounded skin from mice aged P21, P49, and P616, respectively. The data can be loaded using the Python package Scanpy or AnnData, but you can also load it in R if you use zellkonverter.

Data and code: A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells

If you have questions, please contact the first autor via the email lauraluzia@gmail.com Link to the bioRxiv preprint -> https://www.biorxiv.org/content/10.1101/2023.06.23.546324v1

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

ConDecon is a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. This repository contains the raw data of the benchmarking analyses presented in the original publication using the pipeline of Avila-Cobos et al. (10.1038/s41467-020-19015-1). We used this pipeline to evaluate the ability of ConDecon and 17 other deconvolution methods to infer discrete cell type abundances in bulk tissues. The compressed file in this repository contains the synthetic bulk data, ground truth cell type proportions, and the predicted cell type proportions for each method and dataset associated with these analyses. Additional details can be found in the Methods section of the ConDecon publication.

opencc-by-4.0Oct 2022View details →

Raw Data and Scripts used in Regulation of single-cell heterogeneity of capsular polysaccharide synthesis in a human gut symbiont

Raw data used in this publication.  Single-cell analysis of promoter inversions reveals differential inversion rates as a determinant of bacterial population heterogeneity.   Libx.zip contains raw sequencing reads scripts.zip contains code for analysis of reads and growth curve data SequencingRawDataFilesIndex.xls contains a description of all the raw data in each libx.zip.

A method to investigate Pathological Variant load of Mitochondrial DNA in single human Mesenchyma stem cells

Mesenchymal stem cells (MSCs) are the parent cells to many cells of the musculoskeletal system including osteoblasts. Previous work has shown that in mouse models mitochondrial DNA (mtDNA) pathological variants lead to dysfunction of the respiratory chain of these osteoblasts and the premature development of age related osteoporosis. An increased rate of respiratory chain deficiency has also been observed in human osteoblasts. mtDNA mutates at an enhanced rate compared to nuclear DNA and has been linked to the ageing process in humans.  We have developed and optimised an experimental pipeline to isolate single MSCs using flow cytometry before performing next generation sequencing to analyse the pathogenic variant load of 13 patients aged 22-88 to assess if pathogenic variants exist in mesenchymal stem cells and if there are age related changes. Across all patients and all cells mtDNA pathological variants were present at similarly low levels. As per previous studies we found increased pathogenic variants within the mtDNA control region (D-Loop). However overall, there was no significant difference in the distribution of variants across the rest of the genome in all patients. Although a higher proportion of non-synonymous variants were seen this was not statistically significant. We have shown that it is possible to isolate and sequence individual MSCs and observe mtDNA pathological variants. This gives a snapshot in time of the variant load for each patient. Due to low sample numbers compared to other studies investigate mtDNA in other cell types, we did not observe any age related increase in pathological variants. However this adds further evidence that clonally expanded, somatic mtDNA variants are common and could contribute to age related disease including osteoporosis.

Datasets for Muscle : semi-non negative joint decomposition of multiple single cell tensors

This link contains the processed single cell Hi-C (scHi-C) and DNA methylation datasets of <a href="https://www.nature.com/articles/s41592-019-0502-z">Li et al. 2019</a> and <a href="https://www.nature.com/articles/s41586-020-03182-8">Liu et al. 2021. </a>in two separate folders. The datasets are used for the <a href="https://github.com/kp223/Muscle">Github page</a> of <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=gUgGLY0AAAAJ&citation_for_view=gUgGLY0AAAAJ:u5HHmVD_uO8C">Muscle</a>, which employs a semi-non negative joint tensor decomposition framework for multi-omics data analysis, incorporating scHi-C tensors. The datasets are uploaded in '.qs' format in R (gzipped for the Liu et al. 2021 data), and the chromosome size files are in text format. Further details about the data can be found on the GitHub page. The files

openSep 2023View details →

Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution

Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration" StructureST_berghei_livercontains data generated during stpipeline analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4)  infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h. <ul><li>count contains gene expression matrix output from stpipeline in .tsv format</li><li>spotfiles contains coordinate files for count matrices</li><li>images contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.</li><li>masks contains image masks for hepaquery analysis</li><li>distances contains distance measurements from original section sorted by timepoint as well as combined across timepoints</li><li>cluster contains clustering information across spatial positions used in spatial enrichment analysis</li></ul>STUtiility_mus_pb_ST.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in ST_berghei_liverh5adcontains anndata files of ST data (normalized read counts), spot information, distance measurements, images and masks generated using the hepaquery package. visium_berghei_livercontains data generated with the spaceranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h. <ul><li>V10S29-135_A1 contains spaceranger output for section 1 for infected and control sections at 38h post-infection</li><li>V10S29-135_B1 contains spaceranger output for section 1 for infected and control sections at 12h post-infection </li><li>V10S29-135_C1 contains spaceranger output for section 1 for infected and control sections at 24h post-infection </li><li>V10S29-135_D1 contains spaceranger output for section 2 for infected and control sections at 38h post-infection </li></ul>se_visium.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in visium_berghei_liversnSeq_berghei_livercontains data generated with the cellranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.cellranger_cnt_out contains feature count matrix information from cell ranger outputfinal_merged_curated_annotations_270623.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in snSeq_berghei_liver.tar.gzraw images.zip contains raw images for supplementary figures 20-22adjusted images.zip contains brightness and contrast adjusted images for supplementary figures 20-22

opencc-by-4.0Apr 2023View details →

DELVE: Feature selection for preserving biological trajectories in single-cell data

Contains preprocessed single-cell data and metadata for feature selection. Preprocessed adata objects can be accessed using the read_h5ad function in anndata. Also contains the source data files for reproducing the Figures and Supplementary Figures referenced in the manuscript. <ul> <li>The RPE iterative indirect immunofluorescence imaging dataset (adata_RPE.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/10.1016/j.cels.2021.10.007</a>) was originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4525425">https://doi.org/10.5281/zenodo.4525425</a>).</li> <li>The PDAC iterative indirect immunofluorescence imaging datasets (listed below) were originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.7860332">https://doi.org/10.5281/zenodo.7860332</a>). <ul> <li>adata_PDAC_BxPC3_control.h5ad</li> <li>adata_PDAC_CFPAC_control.h5ad</li> <li>adata_PDAC_HPAC_control.h5ad</li> <li>adata_PDAC_MiaPaCa_control.h5ad</li> <li>adata_PDAC_Pa01C_control.h5ad</li> <li>adata_PDAC_Pa02C_control.h5ad</li> <li>adata_PDAC_Pa16C_control.h5ad</li> <li>adata_PDAC_PANC1_control.h5ad</li> <li>adata_PDAC_UM53_control.h5ad</li> </ul> </li> <li>The CD8+ T cell differentiation dataset (adata_CD8.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/</a><a href="https://doi.org/10.1126/sciimmunol.aaz6894">10.1126/sciimmunol.aaz6894</a>) was originally downloaded from the Gene Expression Omnibus under the accession code GSE131847 (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131847</a>).</li> <li>The definitive endoderm differentiation dataset (adata_DE.h5ad) contains multiplexed single-cell RNA sequencing data profiling the differentiation of human embryonic stem cells into the definitive endoderm. </li> </ul>

Training dataset for label-free microfluidic droplet classification in single cell functional assays

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Comprehensive single-cell atlas of the mouse retina

Abstract: Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.   Data description: 1. MRCA: scRNA-seq of the mouse retina - all cells This file contains the full 330K single cells of the MRCA. 2. MRCA: scRNA-seq of the mouse retina - bipolar cell subclass This file contains 147K single cells for bipolar cell subcass of the MRCA. 3. MRCA: scRNA-seq of the mouse retina - RGC subclass This file contains 77K single cells for retinal ganglion cell subclass of the MRCA. 4. MRCA: scRNA-seq of the mouse retina - amacrine cell subclass This file contains 43K single cells for amacrine cell subclass of the MRCA.

Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"

These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics". These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.

Single Cell CPTAC Renal Cell Carcinoma

These data include a subset of single-cell samples from the CPTAC Renal Cell Carcinoma data processed using the following steps: <ol> <li>Loom files were read into R and converted into SingleCellExperiment objects.</li> <li>Ensembl gene ID's were matched to HGNC symbols, chromosome name, starting position, and ending position via Biomart using the scater R package.</li> <li>Size factors were computed using the scran R package.</li> <li>UMAP dimensions were computed using the scater R package.</li> <li>Probes without matching HGNC symbols were removed.</li> <li>Where duplicate HGNC symbols were present, the gene with the maximum normalized range was retained.</li> <li>Cell types were inferred using the scMRMA R package.</li> <li>Cells with less than or equal to 1,000 features were removed.</li> <li>Cells with mitochondrial reads greater than or equal to 50% were removed.</li> <li>Expression data for podocytes and macrophages were saved separately for each sample.</li> <li>For podocytes and macrophages for each sample, expression data were projected onto the first 100 principal components using the irlba R package. The results are available from CPTAC_RCC_PCA.zip.</li> <li>For macrophages for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the M0 markers CSF1R, CD14, CD68, and CD11B, the M1 markers CD86, MARC0, CXCL9, CXCL10, CXCL11, NOS2, SOCS1, and CD64, and the M2 markers TGM2, CD23, ARG1, CCL22, CD163, and CD206 (from PMC8268869). Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory from M0 -> M1 -> M2 was evident using these markers were retained.</li> <li>For podocytes for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the dedifferentiation markers DACH1 (from PMC5908116) and PTPRO (from PMID9639039. Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained. These pseudotime assignments and the macrophage assignments are available from CPTAC_RCC_pseudotime_all_cells.zip.</li> <li>For podocytes and macrophages for each sample, 10-fold cross-validation matrices for expression, PCA, and pseudotime were generated across 5 random splits, for a total of 50 files per cell type, per sample. These files are available from CPTAC_RCC_expression_all_cells.zip.</li> <li>Expression data were subset to include 63 randomly-selected podocytes and 63 randomly-selected macrophages to ensure balanced data. These expression data are available from CPTAC_RCC_expression.zip. Pseudotimes were also subset and are available in CPTAC_RCC_pseudotime.zip.</li> <li>Expression data were projected onto the first 100 principal components. These data are available from CPTAC_RCC_expression_dimReduced.zip.</li> </ol>

Single Cell CPTAC GBM

These data include a subset of single-cell samples from the CPTAC Glioblastoma data processed using the following steps: <ol> <li>Loom files were read into R and converted into SingleCellExperiment objects.</li> <li>Ensembl gene ID's were matched to HGNC symbols, chromosome name, starting position, and ending position via Biomart using the scater R package.</li> <li>Size factors were computed using the scran R package.</li> <li>UMAP dimensions were computed using the scater R package.</li> <li>Probes without matching HGNC symbols were removed.</li> <li>Where duplicate HGNC symbols were present, the gene with the maximum normalized range was retained.</li> <li>Cell types were inferred using the scMRMA R package.</li> <li>Cells with less than or equal to 1,000 features were removed.</li> <li>Cells with mitochondrial reads greater than or equal to 50% were removed.</li> <li>Expression data for macrophages, oligodendrocytes, neurons, and interneurons were saved separately for each sample.</li> <li>For macrophages, oligodendrocytes, neurons, and interneurons for each sample, expression data were projected onto the first 100 principal components using the irlba R package. The results are available from CPTAC_GBM_PCA.zip.</li> <li>For macrophages for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the M0 markers CSF1R, CD14, CD68, and CD11B, the M1 markers CD86, MARC0, CXCL9, CXCL10, CXCL11, NOS2, SOCS1, and CD64, and the M2 markers TGM2, CD23, ARG1, CCL22, CD163, and CD206 (from PMC8268869). Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory from M0 -> M1 -> M2 was evident using these markers were retained.</li> <li>For neurons and interneurons for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the proliferation markers NOTCH1 and HES (from PM28157712). Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained.</li> <li>For oligodendrocytes for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of myelin components MBP and MOG (from PM37055795). Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained. These pseudotime assignments (and macrophage, neuron, and interneuron assignments) are available from CPTAC_GBM_pseudotime_all_cells.zip.</li> <li>For macrophages, neurons, interneurons, and oligodendrocytes for each sample, 10-fold cross-validation matrices for expression, PCA, and pseudotime were generated across 5 random splits, for a total of 50 files per cell type, per sample. These files are available from CPTAC_GBM_expression_all_cells.zip.</li> <li>Expression data were subset to include 63 randomly-selected macrophages, neurons, interneurons, and oligodendrocytes to ensure balanced data. These expression data are available from CPTAC_GBM_expression.zip. Pseudotimes were also subset and are available in CPTAC_GBM_pseudotime.zip.</li> <li>Expression data were projected onto the first 100 principal components. These data are available from CPTAC_GBM_expression_dimReduced.zip.</li> </ol>

opencc-by-4.0Jan 2024View details →

Single Cell CPTAC RCC

These data include a subset of single-cell samples from the CPTAC Renal Cell Carcinoma data processed using the following steps: <ol> <li>Loom files were read into R and converted into SingleCellExperiment objects.</li> <li>Ensembl gene ID's were matched to HGNC symbols, chromosome name, starting position, and ending position via Biomart using the scater R package.</li> <li>Size factors were computed using the scran R package.</li> <li>UMAP dimensions were computed using the scater R package.</li> <li>Probes without matching HGNC symbols were removed.</li> <li>Where duplicate HGNC symbols were present, the gene with the maximum normalized range was retained.</li> <li>Cell types were inferred using the scMRMA R package.</li> <li>Cells with less than or equal to 1,000 features were removed.</li> <li>Cells with mitochondrial reads greater than or equal to 50% were removed.</li> <li>Expression data for podocytes and macrophages were saved separately for each sample.</li> <li>For podocytes and macrophages for each sample, expression data were projected onto the first 100 principal components using the irlba R package. The results are available from CPTAC_RCC_PCA.zip.</li> <li>For macrophages for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the M0 markers CSF1R, CD14, CD68, and CD11B, the M1 markers CD86, MARC0, CXCL9, CXCL10, CXCL11, NOS2, SOCS1, and CD64, and the M2 markers TGM2, CD23, ARG1, CCL22, CD163, and CD206 (from PMC8268869). Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory from M0 -> M1 -> M2 was evident using these markers were retained.</li> <li>For podocytes for each sample, a differentiation trajectory was estimated using the monocle3 R package, and plots were colored by combined expression of the dedifferentiation markers DACH1 (from PMC5908116) and PTPRO (from PMID9639039. Pseudotime starting points were annotated in monocle3 using visual inspection of plots. Only samples in which a visible trajectory of dedifferentiation was evident using these markers were retained. These pseudotime assignments and the macrophage assignments are available from CPTAC_RCC_pseudotime_all_cells.zip.</li> <li>For podocytes and macrophages for each sample, 10-fold cross-validation matrices for expression, PCA, and pseudotime were generated across 5 random splits, for a total of 50 files per cell type, per sample. These files are available from CPTAC_RCC_expression_all_cells.zip.</li> <li>Expression data were subset to include 63 randomly-selected podocytes and 63 randomly-selected macrophages to ensure balanced data. These expression data are available from CPTAC_RCC_expression.zip. Pseudotimes were also subset and are available in CPTAC_RCC_pseudotime.zip.</li> <li>Expression data were projected onto the first 100 principal components. These data are available from CPTAC_RCC_expression_dimReduced.zip.</li> </ol>

single-cell imaging datasets associated with JupyterLab notebooks

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Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)

View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv). For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.

Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning

In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning". These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification.  The datasets are as follows: <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH

Material for Single-cell RNA analysis (I) BIO463 Week 9

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