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Free keywords:
Singele-cell RNA sequencing, Single-cell data analysis, Stem cell identification, Lineage inference, Pseudo-temporal ordering, Fate bias, Multipotent, RaceID3, StemID2, FateID
Abstract:
With the advent of several single-cell RNA-sequencing (scRNA-seq) techniques, it has become possible to gain novel insights into the fundamental long-standing questions in biology with an unprecedented resolution. Among the various applications of scRNA-seq, (1) discovery of novel rare cell types, (2) characterization of heterogeneity among the seemingly homogenous population of cells described by cell surface markers, (3) stem cell identification, and (4) construction of lineage trees recapitulating the process of differentiation remain at the forefront. However, given the inherent complexity of these data arising from the technical challenges involved in such assays, development of robust statistical and computational methodologies is of major interest. Therefore, here we present an in-house state-of-the-art scRNA-seq data analyses workflow for de novo lineage tree inference and stem cell identity prediction applicable to many biological processes under current investigation.
