6/29/2023 0 Comments Step seq ipa![]() The interpreted data from this study are publically available via our website ( ) and LungMAP website ( ).įunding: This work was supported by the National Heart, Lung, and Blood Institute of National Institutes of Health (, grants U01HL110964 (LRRC), U01HL122642 (LungMAP), and R01HL105433. The raw data have been submitted to GEO (, Accession number GSE69761). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: The source code of SINCERA with reproducible demonstrations can be found at CCHMC PBGE website. ![]() Received: ApAccepted: SeptemPublished: November 24, 2015Ĭopyright: © 2015 Guo et al. PLoS Comput Biol 11(11):Įditor: Andreas Prlic, UCSD, UNITED STATES Ĭitation: Guo M, Wang H, Potter SS, Whitsett JA, Xu Y (2015) SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. The pipeline supports the analysis for: 1) the distinction and identification of major cell types 2) the identification of cell type specific gene signatures and 3) the determination of driving forces of given cell types. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo.
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