SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
By Minzhe Guo, Hui Wang, S. Steven Potter, Jeffrey A. Whitsett, Yan Xu. PLoS Comput. Biol., 2015
326 citations recorded by the source corpus.
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. 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. 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. 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. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. 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. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/d991ffc7a8e0b2055276000cf02c4b8823c7a90a
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In Cancer Research
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Citations in the corpus
- Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Once sub-populations are identified, it will be very interesting to determine if they correspond to a known cell type; toward this end, a cell-type-enrichment analysis approach similar to gene-set-enrichment analysis has been developed as part of SINCERA [72].”
- SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “This data set has been used extensively for benchmarking purposes 13,14,20,27–31 and contains the main cell types in hippocampus and somatosensory cortex, namely neurons (pyramidal excitatory neurons, and interneurons), glia (astrocytes, oligodendrocytes, microglia), and endothelial cells.”
- DrImpute: imputing dropout events in single cell RNA sequencing data (2017, methodology): “4 ([13], [11], [15], [16], [28], [19], [20], [21], [22]).”