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11,105 results for “single cells”
Generating VISA scores on glioma bulk RNAseq and single cell RNAseq glioma
<p>Tissue stiffness is collectively determined by the stiffness of ECM and cells. Gliomas are characterized by dysregulated expression of ECM proteins, ECM crosslinking enzymes, and aberrant cellular contractility.While The RNA sequencing datasets have been comprehensively analyzed to interrogate tumor genomes, epigenomes, and transcriptomes, we realized an untapped potential: using transcriptomic data coupled with analysis of gene expression associated with ECM and actomyosin contractility to establish a bioinformatic tool, which we named VIrtual Stiffness Algorithm (VISA), capable of inferring tissue stiffness.</p> <p>This data repository is for reproducing VISA scores on glioma bulk RNAseq and single cell RNAseq glioma.</p>
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
<p>Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.</p>
Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses
<p>Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed <em>Trellis</em> — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy.</p> <p> </p> <p>This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis</p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Deep model predictive control of gene expression in thousands of single cells
<p>Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures.</p> <p>See our preprint here: <a href="https://www.biorxiv.org/content/10.1101/2022.10.28.514305">biorxiv.org/content/10.1101/2022.10.28.514305</a></p> <p>And our code repository here: <a href="https://gitlab.com/dunloplab/deepcellcontrol">gitlab.com/dunloplab/deepcellcontrol</a></p> <p><strong>Contents:</strong></p> <ul> <li><em>datasets.zip</em>: Formatted experimental data used to train and validate fluorescence forecasting models.</li> <li><em>experiments.zip:</em> Processed data for all control experiments.</li> <li><em>misc.zip</em>: Files necessary to reproduce some figures or to exactly reproduce some of our results.</li> <li><em>models.zip</em>: Trained neural network models and associated files.</li> </ul>
DINO4cells example single cells
<p>This dataset contains 15k single cells taken from the Human Protein Project, as well as a csv file with their metadata.</p>
Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
<p>All total 152 datasets are collected in the benchmarking study.</p> <p>Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels.</p> <p>There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).</p>
BSSE_QGF_61757 L010-L016 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61757 L010 to L016</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61757 L001-L009 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61757 L001 to L009</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61756 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61756</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61759 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61759</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61758 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61758</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
Single Cell RNA sequencing data of ADT treated Prostate cancer patients
<p>The data was generated from a study that was conducted according to guidelines approved by the Review Board at the University of Texas Southwestern Medical Center. We procured patient biopsy samples from two distinct studies. The first is titled "Tissue Collection and Results Gathering for Radiotherapy Patients & Healthy Individuals" (STU 072010-098), and the second is a Phase I Clinical Study on Stereotactic Ablative Radiotherapy (SABR) for Pelvic and Prostate Areas in High-Risk Prostate Cancer Patients (STU062014-027). The single-cell RNA sequencing (scRNA-seq) took place in Dr. Douglas Strand's laboratory, adhering to the method outlined in Henry et al<sup>1</sup>. We used a 1-hour treatment with 5mg/ml of collagenase type I, 10mM of ROCK inhibitor, and 1mg of DNase. Barcode labeling for 3' GEX was done using a 10X machine, and the sequencing process utilized an Illumina NextSeq 500 device.</p> <p> </p> <p>1. Henry, G. H., Malewska, A., Joseph, D. B., Malladi, V. S., Lee, J., Torrealba, J., ... & Strand, D. W. (2018). A cellular anatomy of the normal adult human prostate and prostatic urethra. <em>Cell reports</em>, <em>25</em>(12), 3530-3542.</p>
Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. </p> <p>The data is as follows:</p> <p><strong>scATAC</strong>: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.</p> <p><strong>scRNA</strong>: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). </p> <p><strong>multiome</strong>: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes. </p>
ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>.</p> <p>`data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details.</p> <p><strong>Models:</strong></p> <p><em>Loading the model:</em></p> <p>The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13.</p> <p>To load the models in tf1.14:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> <p>In tf2:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> <p>If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.</p> <p> </p> <p><em>Usage:</em></p> <p>The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar:</p> <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> <p>The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions:</p> <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> <p>If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as:</p> <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> <p>To obtain predicted per-base predicted counts (with or without bias):</p> <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> <p>Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.</p> <p> </p> <p><em>Note:</em></p> <p>All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.</p> <p> </p> <p><em>Folds:</em></p> <p>The splits used for training the different folds are as below:</p> Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 <p>Remaining chromosomes were used as the training chromosome for each fold.</p>
ALA induced transcriptome and single cell microscopy of M.tuberculosis
<p> <strong>Single cell microscopy of porphyeins in vegetative and dormant <em>Mycobacterium tuberculosis</em>. Confocal fluorescence microscopy, life-time measurements, and microspectrofluorimetry. </strong>Fluorescence lifetime measurements were performed on a PicoQuant MicroTime 200 confocal scanning system (Pico-Quant GmbH, Berlin, Germany) based on an Olympus IX-71 inverted fluorescence microscope (Japan). SymphoTime® software was used for data collection. Fluorescence spectra were recorded in the confocal mode of the MicroTime 200 system using a Shamrock 163 spectrograph with a Newton DU-970 camera (Andor, UK).</p> <p><strong>Transcriptomic analysis of cells of <em>M. tuberculosis </em>in a vegetative state and under transition into dormant state upon administration of exogenous ALA.</strong> The quality of the resulting libraries was checked using the Fragment Analyzer. Quantitative analysis was performed by qPCR. After quality control and assessment of DNA quantity, the pool of libraries was sequenced on an Illumina NovaSeq 6000 instrument (length of reads - 150 bp on both sides of the fragments). FASTQ files were generated using bcl2fastq v2.20 Conversion Software (Illumina). The quality data string record format is Phred 33. As a result, 1,107,614,502 reads were received. </p> <pre>This study was funded by Russian Science Foundation grant 19-15-00324.</pre>
Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts
<p>This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>:<br> "Single-cell gene expression prediction from DNA sequence at large contexts"</p> <p>Structure:</p> <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul> <p> </p> <p>"Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."</p>
Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs
<p>This repository contains the data and code needed to recreate figures in the main and supplementary text of the paper "Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs" by the same authors. Instructions for running the code are provided in the README.md file.</p> <p>See also <a href="https://github.com/lskfs/ascidian-endostyle">https://github.com/lskfs/ascidian-endostyle</a>.</p>
v0.92 single cell Eye in a Disk (scEiaD) dataset for PLAE.nei.nih.gov
<p>Unified (integrated) pan study ocular single cell transcriptome</p>
COVID-19 vaccination single cell datasets
<p>The datasets presented here comprise the sequencing data featured in the research paper titled: "Multimodal single-cell datasets characterize antigen-specific CD8<sup>+</sup> T cells across SARS-CoV-2 vaccination and infection": https://www.nature.com/articles/s41590-023-01608-9</p> <p>Peripheral Blood Mononuclear Cell (PBMC) samples utilized for both CITE-seq and ASAP-seq were systematically collected at four distinct time intervals:</p> <ul> <li>Pre-vaccination (Day 0)</li> <li>Post-primary vaccination (Day 2 and Day 10.</li> <li>Seven days post-boost vaccination (Day 28).</li> </ul> <p>The count matrix folder contains count matrices for each experimental type, specifically CITE-seq, ASAP-seq, and ECCITE-seq. In addition, we have included the fully integrated, processed Seurat objects for downstream analysis.</p> <p>Details of the content within the count matrix folder are as follows:</p> <ul> <li>The RNA, ATAC, and TCR modality outputs were generated using the 10x Cellranger pipeline.</li> <li>HTO and ADT modalities were mapped with Alevin.</li> </ul> <p>Outlined below are the three processed single-cell datasets:</p> <ol> <li>PBMC_vaccine_CITE.rds: 3' RNA and surface proteins (173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ASAP.rds: Chromatin accessibility and surface proteins (173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ECCITE_TCR.rds: 5' RNA, surface proteins (137 TotalSeq-C antibodies), TCR and dextramer loaded with peptides of SARS-CoV-2 spike protein.</li> </ol> <ul> <li>antigen_module_genes.rds: This file contains the vaccine-induced gene sets.</li> <li>antigen_module_peaks.rds: This file contains the DE peaks specific for vaccine-induced cells. </li> </ul> <p>To map the scRNA-seq query dataset onto our CITE-seq reference:</p> <pre><code>library(Seurat) PBMC_CITE <- readRDS("/zenedo/PBMC_vaccine_CITE.rds") query_scRNA <- readRDS("/home/xx/your_own_data.rds") anchors <- FindTransferAnchors( reference = PBMC_CITE, query = query_scRNA, normalization.method = "SCT", k.anchor = 5, reference.reduction = "spca", dims = 1:50) query_scRNA <- MapQuery( anchorset = anchors, query = query_scRNA, reference = PBMC_CITE, refdata = list( l1 = "celltypel1", l2 = "celltypel2", l3 = "celltypel3"), reference.reduction = "spca", reduction.model = "wnn.umap") </code></pre> <p>To use the scATAC-seq data, please run the commands below to update the path of the fragment file for the object. </p> <pre><code>Vaccine_ASAP <- readRDS("PBMC_vaccine_ASAP.rds") # remove fragment file information Fragments(Vaccine_ASAP) <- NULL # Update the path of the fragment file Fragments(Vaccine_ASAP) <- CreateFragmentObject(path = "download/PBMC_vaccine_ASAP_fragments.tsv.gz", cells = Cells(Vaccine_ASAP))</code></pre> <p> </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.