GSE200531: Single-cell RNA-seq uncovers the pathological alterations of pancreatic islets in type 2 diabetes mice

scRNA-seq Viewer
Info Studies were incorporated in a barcode, matrix, feature format. This format can be found on the 10x Genomics website for processing of single cell studies to obtain the gene expression matrices. For each study, the metadata incorporated in GEO were manually curated into profiles and the samples were separated based on applicable groups and conditions. The expression data from the cells for the samples within the same profile and condition were aggregated into an expression matrix with the cell barcodes having the sample name appended to it to ensure unique cell names.

Description (from GEO)

Submission Date: Apr 09, 2022

Summary: Pancreatic islet beta cell heterogeneity has been identified, which plays a pivotal role in the pathological alterations of pancreatic islets in type 2 diabetes (T2D) mice. However, pathological alterations of beta cells in type 2 diabetes (T2D) mice remain to be investigated. We isolated pancreatic islets from the control and T2D mice and conducted scRNA-seq analysis using the 10x Genomics platform. Pathological alterations of beta cells in T2D were also explored.

GEO Accession ID: GSE200531

PMID: 36339258

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Info Preprocessing and downstream analysis were computed using the scanpy Python library and the steps of processing followed the Seurat vignette. Cells and genes with no expression or very low expression were removed from the dataset based on a predefined threshold. The data was then normalized across the expression within the cells and log normalized. The top 2000 highly variable genes were extracted to be used for downstream analysis. For each of these aggregated data matrices, the clusters were computed using the leiden algorithm. Scanpy was then used to compute the PCA, t-SNE, and UMAPs. The points in the plots are labelled by their corresponding cell type labels. The cell type labels were computed using the wilcoxon method as the differential gene expression method. The top 250 genes were then used for enrichment analysis against the CellMarker library in order to determine the most appropriate cell type label with the lowest p-value.
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