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371 results for “single cell genomics”
The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma
<p>The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry<sup>TM</sup> (IMC<sup>TM</sup>) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.</p>
Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior
<p>The datasets used in the paper "Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior". A detailed description of these datasets is available at https://github.com/jaydu1/VITAE/tree/master/data.</p>
Clonal decomposition and DNA replication states defined by scaled single cell genome sequencing
<p><strong>OV2295 Tables</strong></p> <p>ov2295_breakpoint_counts.csv.gz: Table of breakpoint counts per cell</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>cell_id: identifier for the cell</li> <li>read_count: number of reads</li> <li>library_id: identifier for the DNA library</li> <li>sample_id: identifier for the sequenced sample</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> </ul> <p>ov2295_cell_cn.csv.gz: Table of cell specific copy number</p> <ul> <li>cell_id: identifier for the cell</li> <li>sample_id: identifier for the sequenced sample</li> <li>library_id: identifier for the DNA library</li> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>reads: number of reads</li> <li>copy: raw normalized copy number</li> <li>state: copy number state</li> <li>gc: percent gc of the bin</li> <li>map: average mappability of the bin</li> </ul> <p>ov2295_cell_metrics.csv.gz: Table of cell metrics</p> <ul> <li>cell_id: identifier of the cell</li> <li>unpaired_mapped_reads: number of unpaired mapped reads</li> <li>paired_mapped_reads: number of mapped reads that were properly paired</li> <li>unpaired_duplicate_reads: number of unpaired duplicated reads</li> <li>paired_duplicate_reads: number of paired reads that were also marked as duplicate</li> <li>unmapped_reads: number of unmapped reads</li> <li>percent_duplicate_reads: percentage of duplicate reads</li> <li>estimated_library_size: scaled total number of mapped reads</li> <li>total_reads: total number of reads, regardless of mapping status</li> <li>total_mapped_reads: total number of mapped reads</li> <li>total_duplicate_reads: number of duplicate reads</li> <li>total_properly_paired: number of properly paired reads</li> <li>coverage_breadth: percentage of genome covered by some read</li> <li>coverage_depth: average reads per nucleotide position in the genome</li> <li>median_insert_size: median insert size between paired reads</li> <li>mean_insert_size: mean insert size between paired reads</li> <li>standard_deviation_insert_size: standard deviation of the insert size between paired reads</li> <li>index_sequence: index sequence of the adaptor sequence</li> <li>column: column of the cell on the nanowell chip</li> <li>img_col: column of the cell from the perspective of the microscope</li> <li>index_i5: id of the i5 index adapter sequence</li> <li>sample_type: type of the sample</li> <li>primer_i7: id of the i5 index primer sequence</li> <li>experimental_condition: experimental treatment of the cell, includes controls</li> <li>index_i7: id of the i7 index adapter sequence</li> <li>cell_call: living/dead classification of the cell based on staining usually, C1 == living, C2 == dead</li> <li>sample_id: name of the sample</li> <li>primer_i5: id of the i5 index primer sequence</li> <li>row: row of the cell on the nanowell chip</li> <li>library_id: identifier for the DNA library</li> <li>index: ignored</li> <li>multiplier: during parameter searching, the set [1..6] that was chosen</li> <li>MSRSI_non_integerness: median of segment residuals from segment integer copy number states</li> <li>MBRSI_dispersion_non_integerness: median of bin residuals from segment integer copy number states</li> <li>MBRSM_dispersion: median of bin residuals from segment median copy number values</li> <li>autocorrelation_hmmcopy: hmmcopy copy autocorrelation</li> <li>cv_hmmcopy: ignored</li> <li>empty_bins_hmmcopy: number of empty bins in hmmcopy</li> <li>mad_hmmcopy: median absolute deviation of hmmcopy copy</li> <li>mean_hmmcopy_reads_per_bin: mean reads per hmmcopy bin</li> <li>median_hmmcopy_reads_per_bin: median reads per hmmcopy bin</li> <li>std_hmmcopy_reads_per_bin: standard deviation value of reads in hmmcopy bins</li> <li>total_halfiness: summed halfiness penality score of the cell</li> <li>total_mapped_reads_hmmcopy: total mapped reads in all hmmcopy bins</li> <li>scaled_halfiness: summed scaled halfiness penalty score of the cell</li> <li>mean_state_mads: mean value for all median absolute deviation scores for each state</li> <li>mean_state_vars: variance value for all median absolute deviation scores for each state</li> <li>mad_neutral_state: median absolute deviation score of the neutral 2 copy state</li> <li>breakpoints: number of breakpoints, as indicated by state changes not at the ends of chromosomes</li> <li>mean_copy: mean hmmcopy copy value</li> <li>state_mode: the most commonly occuring state</li> <li>log_likelihood: hmmcopy log likelihood for the cell</li> <li>true_multiplier: the exact decimal value used to scale the copy number for segmentation</li> <li>order: order of the cell in the hierarchical clustering tree</li> <li>quality: random forest classifier proability score that cell is good</li> </ul> <p>ov2295_clone_alleles.csv.gz: Table of clone specific allele data</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>hap_label: haplotype block identifier</li> <li>clone_id: clone identifier</li> <li>allele_1_sum: number of reads for allele 1 of the haplotype block</li> <li>allele_2_sum: number of reads for allele 2 of the haplotype block</li> <li>total_counts_sum: total reads for the haplotype block</li> </ul> <p>ov2295_clone_breakpoints.csv.gz: Table of breakpoints per clone for OV2295 samples. Columns:</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> <li>clone_id: clone identifier</li> <li>read_count: number of reads</li> <li>is_present: presence=1, absent=0</li> </ul> <p>ov2295_clone_clusters.csv.gz: Table of cell clusters as putative clones</p> <ul> <li>cell_id: identifier for the cell</li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_cn.csv.gz: Table of allele specific copy number per clone for OV2295 samples. Columns:</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>total_cn: HMMCopy predicted total copy number </li> <li>minor_cn: HMM predicted minor copy number </li> <li>major_cn: HMM predicted major copy number </li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_snvs.csv.gz: Table of SNVs per clone for OV2295 samples. Columns:</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>clone_id: clone identifier</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>total_counts: total number of reads at this position</li> <li>is_present: presence=0, absent=1</li> <li>is_het: is heterozygous</li> <li>is_hom: is homozygous for the alternate</li> </ul> <p>ov2295_nodes.csv.gz: Table of phylogenetic information for SNV evolution</p> <ul> <li>variant_id: identifier for the SNV as chrom:coord:ref:alt</li> <li>node: node in the phylogenetic tree</li> <li>loss: probability the SNV was lost at this node</li> <li>origin: probability the SNV originated at this node</li> <li>presence: probability the SNV is present at this node</li> <li>ml_origin: binary indicator the SNV originated at this node</li> <li>ml_presence: binary indicator the SNV is present at this node</li> <li>ml_loss: binary indicator the SNV was lost at this node</li> </ul> <p>ov2295_snv_counts.csv.gz: Table of SNV counts</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>cell_id: identifier for the cell</li> <li>total_counts: total number of reads at this position</li> <li>sample_id: identifier for the sequenced sample</li> </ul> <p>ov2295_tree.pickle: Phylogenetic tree in python pickle format. Requires installation of the stochastic dollo code at: https://bitbucket.org/dranew/dollo, version 0.4.2.</p> <p>Note the following sample mapping: ‘SA922’: ‘OV2295(R2)’, ‘SA921’: ‘TOV2295(R)’, ‘SA1090’: ‘OV2295’,</p> <p><strong>Plots</strong></p> <p>ov_supp_clone_allele_cn.png: Clone allele ratios for each OV2295 sample.</p> <p>ov_supp_clone_total_cn.png: Clone copy number for each OV2295 sample.</p> <p>ov_supp_sample_total_cn.png: Bulk copy number for each OV2295 sample.</p> <p>ov_supp_sample_allele_cn.png: Bulk allele ratios for each OV2295 sample.</p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
VeloCycle-estimated cell cycle phases of single cells from a genome-scale perturb-seq performed in K562
<p>Continuous cell cycle phase position between 0 and 2π estimated by <em>VeloCycle </em>(Lederer et al., <em>Nature Methods</em> 2024) for single cells of the perturb-seq performed in the K562 CML cell line by Replogle et al., Cell 2022. </p> <p>Underlies the cell cycle imbalances inferred in Pulver & Forey et al., 2024.</p> <p>Method manuscript exerpt:</p> <p>"For analysis on the genome-wide perturb-seq dataset of K562 cells (Replogle <em>et al.</em>, 2022), a transfer learning approach was applied. Condition-independent estimation of the periodic Fourier series components would be especially challenging on Perturb-seq knockdown conditions containing either (1) very few cells or (2) cells belonging to just one phase of the cell cycle. To infer accurate cell cycle phases for these cells, we first performed manifold-learning for 5,000 training steps to estimate the gene harmonic coefficients (ν0, ν1sin, ν1cos) on a larger set of non-targeting control (NT) K562 cells (75,328 cells), which are more evenly distributed throughout the various phases of the cell cycle. Next, we ran manifold-learning again for 5,000 training steps, but on the entire perturb-seq dataset of 1,971,608 cells and 4,127 gene knockdown conditions (with at least 75 cells per condition). This time, we conditioned <em>VeloCycle</em> on the gene harmonic coefficients learned in the first step. This allowed cells belonging to each stratified gene knockdown condition to be assigned to a position on the cell cycle manifold, while restricting those assignments such that they were based on gene expression patterns earned on a larger and more informative dataset (allowing for batch effect expression differences)."</p>
GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.
<p><strong>Background</strong></p> <p>Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes. </p> <p> </p> <p><strong>Methods and Results</strong></p> <p>To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (<em>SKI</em>, <em>KANK2</em>, <em>SORT1</em>), endothelial cells (<em>SLC44A1</em>, <em>ATP2B1</em>), and macrophages (<em>APOE</em>, <em>HNRNPUL1</em>). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes.</p> <p><br> <strong>Conclusion</strong></p> <p>We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.</p> <p> </p>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
<p>Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. </p>
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</p>
SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data
<p>Processed data for the manuscript "" available on bioRxiv at ""</p> <p>Data is available for the following samples</p> <ol> <li>CD34+ Multiome data : 2 replicates </li> <li>T-cell depleted bone marrow Multiome data: 2 replicates </li> </ol> <p> </p> <p>The following counts and fragments files are available for each replicate </p> <ol> <li><sample>_filtered_feature_bc_matrix.h5: Feature counts from CellRanger ARC</li> <li><sample>_atac_fragments.tsv.gz: ATAC fragments file from Cellranger ARC</li> <li><sample>_atac_fragments.tsv.gz.tbi: Index files for ATAC fragments file from Cellranger ARC</li> </ol> <p> </p> <p>The following scanpy anndata objects are also available</p> <ol> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of CD34+ hematopoietic stem and progenitor cells </li> <li>cd34_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of of CD34+ hematopoietic stem and progenitor cells.</li> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of T-cell depleted bone marrow dataset.</li> <li>bm_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of T-cell depleted bone marrow dataset.</li> </ol>
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
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Input data of manuscript "CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells"
<p>This is the directory containing input data necessary to reproduce analyses presented in the manuscript:</p> <blockquote> <p><strong>CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells</strong><br> Shadi Darvish Shafighi, Szymon M Kiełbasa, Julieta Sepúlveda Yáñez, Ramin Monajemi, Davy Cats, Hailiang Mei, Roberta Menafra, Susan Kloet, Hendrik Veelken, Cornelis A.M. van Bergen, Ewa Szczurek</p> </blockquote>
High quality genomes produced from single MinION flow cells clarify polyploid and demographic histories of critically endangered Fraxinus (ash) species
<p>With populations of threatened and endangered species declining worldwide, efforts are being made to generate high-quality genomic records of these species before they are lost forever. Here, we demonstrate that data from single Oxford Nanopore Technologies (ONT) MinION flow cells can, even in the absence of highly accurate short DNA-read polishing, produce high-quality <em>de novo</em> plant genome assemblies adequate for downstream analyses, such as synteny and ploidy evaluations, paleodemographic analyses, and phylogenomics. This study focuses on three North American ash tree species in the genus <em>Fraxinus</em> (Oleaceae) that were recently added to the International Union for Conservation of Nature (IUCN) Red List as critically endangered. Our results support a whole genome triplication at the base of the Oleaceae as well as a subsequent whole genome duplication shared by <em>Syringa</em>, <em>Osmanthus</em>, <em>Olea, and Fraxinus</em>. Finally, we demonstrate the use of ONT long-read sequencing data to reveal patterns in demographic history.</p>
Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al
<p>This repository provides the processed data necessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>"</p> <p>This includes the following:</p> <ul> <li>Single cell whole genome sequencing <ul> <li>Allele specific copy number profiles</li> <li>SNV counts per cell</li> <li>Structural variant counts per cell</li> <li>QC metrics</li> <li>clone assignments</li> <li>phylogenetic trees computed with sitka</li> <li>benchmarking results vs other methods</li> </ul> </li> <li>bulk whole genome sequencing <ul> <li>copy number profiles</li> <li>SNVs</li> </ul> </li> <li>10X single cell RNA sequencing <ul> <li>count matrices</li> <li>seurat Rdata objects</li> </ul> </li> <li>analysis tables <ul> <li>downstream processed results used to generate figures</li> </ul> </li> <li>oxford nanopore <ul> <li>phasing results</li> </ul> </li> </ul> <p> </p> <p>For further information please feel free to get in touch with Marc Williams (william1 [at] mskcc.org)</p> <p> </p>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 2/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 2/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 5/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 5/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 1/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 1/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 3/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 3/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 4/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 4/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
Resolving Organoid Brain Region Identities by Mapping Single-Cell Genomic Data to Reference Atlases
<p>Data underlying the figures in the publication “Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases”, published in <em>Cell Stem Cell, </em><strong>2021</strong><em>, </em>28, 1148–1159.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1934590921000655">https://www.sciencedirect.com/science/article/pii/S1934590921000655</a></p> <p>Table of contents:</p> <p><strong>1. patscreen_srt.rds</strong>; Numerical data for <em>Figure 7</em>: RNA-seq data of a patterning screen in organoids with an array of small molecules. The dataset is in the rds data format, which can be opened in the R programming language using the function `readRDS()`. Once opened, the dataset is a Seurat object (https://satijalab.org/seurat/) and contains both the transcript counts and the metadata for all samples in the screen. The raw data used in figure 7 was also deposited in ArrayExpress (<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/</a>)</p>
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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.