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11,105 results for “single cells”
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
Mammary single-cell RNA-seq analysis and prostate cancer survival as a function of H2AFJ expression for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Antagonism between viral infection and innate immunity at the single-cell level -- Immunostaining Imaging Dataset
<p>This dataset accompanies the article "Antagonism between viral infection and innate immunity at the single-cell level", at the time of submission available as a <a href="https://doi.org/10.1101/2022.11.18.517110">preprint</a>.</p>
Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model
<p>Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95 95 350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.</p>
Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).
<p>This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis.</p> <p>CyTOF data:</p> <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> <p>scRNA-seq data:</p> <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>
Multiplexed histology of COVID-19 post-mortem lung samples - Single-cell Mean Fluorescence Intensities
<p>Data table containing single-cell mean fluorescence intensities (MFI) of all markers analyzed by multiplexed histology in all COVID-19 post-mortem lung samples and non-COVID-related pneumonia controls (14 lung samples, stratified based on disease duration into control, acute, chronic and prolonged). It contains information at the single-cell level about approx 50 proteins in around 40.000 lung cells.</p> <p>Data shown has been arcsin(h) transformed with a co-factor of 0.2. Additionally, cells expressing less than 0.15 MFI of all markers have been labeled as non-defined and excluded from the data set.</p> <p>Seurat package 4.0.0 was used in R to perform mean centering and scaling, followed by PCA, and reduced the dimensions of the data to the top 11 principal components. UMAP was initialized in this PCA space to visualize the data on reduced UMAP dimensions. The cells were clustered on PCA space using the SNN algorithm implemented as <em>FindNeighbors</em> and <em>FindClusters </em>with <em>n.epochs = 500</em> and default parameters (<em>res = 0.8</em>). We obtained 26 clusters that we merged to get relevant populations for our analysis based on canonical lineage markers. We ended up with 8 cell clusters that were manually annotated based on cell-type-specific markers found to be differentially expressed.</p> <p> </p> <p> </p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion
<p>Data for the publication <strong>Single-Cell Profiling of CD8<sup>+</sup> T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion</strong></p>
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
<p>This archive contains all the code and data to reproduce the results of the associated manuscript: Piovani <em>et al</em>, "Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories". We provide filtered scRNA-seq matrices, protein fasta files for each specie used to run SAMap and GenERA as well as the R-code used to generate the datasets and the jupyter notebook to generate SAMap results. In addition we provide the final Seurat objects and all analysis results which can be consulted without re-running the code.</p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
Single-cell information extracted from IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a></p><ul><li><strong>gated_cells.zip:</strong> contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li><strong>spe.rds:</strong> SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li><strong>images.rds:</strong> CytoImageList object containing the spillover-corrected images.</li><li><strong>masks.rds:</strong> CytoImageList object containing the segmentation masks.</li></ul>
Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"
<p>Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).</p>
Reads-per-UMI tables across single-cell RNA sequencing protocols
<p>Data analyzed in <a href="https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1">Lause, Ziegenhain et al. (2023)</a>.</p> <p>Code to obtain these tables from public data sources is available on <a href="https://github.com/berenslab/read-normalization">github</a>.</p> <p> </p> <p>Each row in the table is a UMI-tag detected in a certain cell (column RG) attached to a molecule from a specific gene (column GE) with a certain barcode (column UB). Column N gives the number of times the UMI was detected for that gene and cell.</p> <p>Data sources and protocols are given with the respective file names below.</p> <p><strong>Johnsson2022_Smartseq3_PE.hd1.txt.gz</strong>: Mouse fibroblasts profiled with <strong>Smart-seq3</strong> paired-end; accession E-MTAB-10148, sample plate2,<br> <a href="https://doi.org/10.1038/s41588-022-01014-1">Paper</a><br> <br> <strong>Hagemann-Jensen2020_Smartseq3_SE.hd1.txt.gz: </strong>Mouse fibroblasts profiled with <strong>Smart-seq3</strong> single-end; accession E-MTAB-8735, sample Smartseq3.Fibroblasts.smFISH<br> <a href="https://doi.org/10.1038/s41587-020-0497-0">Paper</a><br> <br> <strong>Hagemann-Jensen2022_Smartseq3xpress.hd1.txt.gz: </strong>HEK293 cells profiled with <strong>Smart-seq3Xpress</strong>; accession E-MTAB-11467.<br> <a href="https://www.biorxiv.org/content/10.1101/2021.07.10.451889v1">Paper</a><br> <br> <strong>Ziegenhain2017.hd1.txt.gz: </strong>Mouse embryonic stem cells profiled by <strong>CEL-seq2, Drop-seq, MARS-seq, </strong>and<strong> SCRB-seq</strong>; GEO accession GSE75790<br> <a href="https://doi.org/10.1016/j.molcel.2017.01.023">Paper</a></p>
PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes
<p>Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"</p> <p> </p>
Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression
<p>Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata.</p> <p>Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata.</p> <p>Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata.</p> <p>Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read().</p> <p>Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions.</p> <p><br> Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row.</p> <p>Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL.</p> <p>Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.</p>
Processed data of single cell RNAseq of 8 human cell lines
<p>Cell annotation and UMI count matrix for 8 human cell lines:</p> <ul> <li>HCT116</li> <li>IMR90</li> <li>A549</li> <li>Ramos</li> <li>H1437</li> <li>HEK293</li> <li>K562</li> <li>Jurkat</li> </ul> <p>The data set is part of the publication in https://rdcu.be/bYEsu</p> <p> </p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.