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scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

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TLDR
This work presents scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n = 38) covering 200 human cell lines or cell types and 13,440 samples.
Abstract
Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n = 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/.

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PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data.

TL;DR: An online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies, and establishes a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.
Journal ArticleDOI

CancerSEA: a cancer single-cell state atlas.

TL;DR: CancerSEA is the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level, and provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single- cell datasets.
Journal ArticleDOI

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment.

TL;DR: Tumor Immune Single Cell Hub is presented, a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types and provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the Tme.
Journal ArticleDOI

Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST.

TL;DR: Through extensive benchmarks and case studies, the effectiveness of Cell BLAST is demonstrated in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types.
References
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Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells

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