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894 results for “single-cell RNA-seq”
Single-Cell RNA-Seq Identifies Pathways and Genes Contributing to the Hyperandrogenemia Associated with Polycystic Ovary Syndrome
<p>Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by hyperandrogenemia of ovarian thecal cell origin, resulting in anovulation/oligo-ovulation and infertility. Our previous studies established that ovarian theca cells isolated and propagated from ovaries of normal ovulatory women and women with PCOS, have distinctive molecular and cellular signatures that underlie the increased androgen biosynthesis in PCOS. To evaluate differences between gene expression in single cells from passaged cultures of theca cells from ovaries of normal ovulatory women and women with PCOS, we performed single-cell RNA sequencing (scRNA-seq). Results from these studies revealed differentially expressed pathways and genes involved in the acquisition of cholesterol, the precursor of steroid hormones, and steroidogenesis. Bulk RNA-seq and microarray studies confirmed the theca cell differential gene expression profiles. The expression profiles appear to be directed largely by increased levels or activity of the transcription factors SREBF1, which regulates genes involved in cholesterol acquisition (<em>LDLR, LIPA, NPC1, CYP11A1, FDX1, FDXR)</em> and GATA6, which regulates expression of genes encoding steroidogenic enzymes (<em>CYP17A1) </em>in concert with other differentially expressed transcription factors (<em>SP1</em>, <em>NR5A2</em>). This study provides insights into the molecular mechanisms underlying the hyperandrogenemia associated with PCOS, and highlights potential targets for molecular diagnosis and therapeutic intervention.</p> <p><strong>scRNA-seq data in the form of 10x Cell Ranger files are available for the following samples:</strong></p> <p>PCOS affected - Mc03, Mc10, Mc16, Mc26, Mc27</p> <p>Normal cycling women - Mc02, Mc06, Mc31, Mc40, Mc50</p> <p>A F in the sample name indicates forskolin treatment and a C indicates untreated control samples.</p> <p> </p>
Immune single-cell RNA-seq data from PyMT-M tumor and its peripheral blood samples
<p><em>Immune single-cell RNA-seq data collection and preprocessing</em></p> <p>Blood and tumor samples were harvested from PyMT-M tumor-bearing mice. Blood samples (n=3) are collected retro-orbitally using caliper tubes and processed with red blood cell lysis buffer (Tonbo Biosciences) before library preparation. A tumor sample are dissociated with Tumor Dissociation Kit following the manufacturer’s instructions (Miltenyi Biotec). After isolation and filtering through a 70µm filter, CD45+DAPI- cells were sorted using FACSAria cell sorter (BD Biosciences) at the Cytometry and Cell Sorting Core. The single-cell libraries were prepared using Chromium Controller (10X Genomics) at the Single Cell Genomics Core and sequenced using NovaSeq 6000 at the Genomics and RNA Profiling Core of Baylor College of Medicine. The FASTQ files were processed using Cell Ranger pipelines (10X Genomics) to generate feature-barcode matrices.</p> <p><em>Integrating immune single-cell RNA-seq data from the blood and tumor of PyMT-M mouse</em></p> <p>We followed the Seurat tutorial on single-cell RNA integration from <a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>. Specifically, both datasets were library-size normalized and log-scaled. Then, variable genes from both datasets were extracted, and overlapped variable genes were used as anchors to integrate the two datasets to generate a combined immune single-cell RNA-seq dataset.</p> <p> </p> <p><em>Immune cell type annotation using SingleR</em></p> <p>After having the integrated immune single-cell RNA-sequencing data, we performed the standard pipeline for clustering, including scaling the expression data, performing dimension reduction using PCA and UMAP, and finding clusters by a shared nearest neighbor (SNN) modularity optimization (<a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>). Then, we used the R package <em>SingleR </em>to assign cell-type labels to each identified cluster using the <em>ImmGen</em> reference data from the Immunological Genome Project.</p> <p> </p>
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
<p>The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.</p>
Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves
<p>Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves</p>
Raw and normalized count data for "Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling"
<p>SingleCellExperiment objects containing raw and normalized counts, as well as reduced dimension representations and cell type annotations for both the follicular lymphoma samples (sce_follicular_annotated_final.rds) and high grade serous ovarian cancer samples (sce_hgsc_annotated_final.rds) as detailed in the paper, as well as reactive lymph node data (sce_RLN_normalized.rds).</p>
Supplementary Material for the paper entitled "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data"
<p>This repo contain supplementary tables from the manuscript entitled: "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data". Clustering is a common way to identify cell types in single-cell RNA-sequencing (scRNA-seq) data. Unfortunately, current methods (i) require users to make human-in-the-loop decisions, which adds significant runtime to bioinformatic analyses, and (ii) reuse the same data twice when testing for differentially expressed genes, which can lead to an increased number of false discoveries. In this work, we overcome these limitations with NCLUSION: a Bayesian nonparametric method that simultaneously clusters cells and selects marker genes. NCLUSION operates without user-defined heuristics to set model parameters and leverages variational expectation-maximization (EM) for posterior inference which allows it to scale well up to 1 million cells. By analyzing publicly available datasets, we illustrate that NCLUSION matches the state-of-the-art clustering performance of competing approaches, achieves improved computational efficiency, and directly enables identification of biologically relevant gene sets driving cluster definitions.</p>
Single-cell RNA-seq dataset and code for human colorectal cancer liver metastasis study
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Supplementary information to "ScRNA-IMM: Single-cell RNA-Seq Imputation method using Mean/Median Imputation"
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An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types
<p>The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic<br> regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections<br> to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely<br> to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been<br> adequately classified functionally, its molecular classification is currently still insufficient.</p> <p>To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN<br> single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we<br> functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data<br> and existing single-cell data of a PVN-projection target region.</p>
Healthy woodchuck genome with viral sequences appended used for single-cell RNA-seq analysis
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CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal - Appendix
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scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data
<p><span>In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, specific evaluation for different contamination levels is lacking. Here, we show that DecontX and CellBender under-correct highly-contaminating genes, while SoupX and scAR over-correct lowly-/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly-contaminating genes while avoiding over-correction of other genes.</span></p>
A Pan-Cancer Single-Cell RNA-seq Atlas of Intratumoral B Cells
<p>Seurat object associated with publication 'A Pan-Cancer Single-Cell RNA-seq Atlas of Intratumoral B Cells' (Fitzsimons et al. Cancer Cell 2024)</p>
GO Enrichment Analysis on Single-Cell RNA-Seq Data
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Single-cell RNA-seq unveils fibroblast-t cell interplay in muscle-invasive bladder cancer
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Single-cell RNA-seq uncovers unique gene expression profiles in the spleen of hypoxic mice
GEO Series GSE219259. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Single-cell RNA-seq investigation of skin and blood T cells from patients with cutaneous T-cell lymphoma
GEO Series GSE224449. Homo sapiens. 1878 samples. Type: Expression profiling by high throughput sequencing.
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [ddSEQ]
GEO Series GSE163788. Mus musculus; Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
10X Genomics single-cell RNA-Seq data set of CreER mice
GEO Series GSE162713. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
single-cell RNA-seq profile of microglia under acute Csf1r inhibition and early repopulation in adult brain
GEO Series GSE150169. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.