Computational approaches for interpreting scRNA-seq data

FEBS Letters
Raghd RostomGozde Kar

Abstract

The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis.

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Related Concepts

Metazoa
Gene Expression
Disease Clustering
Sequence Determinations, RNA
Computational Molecular Biology
Single-Cell Analysis
Genes
RNA, Small Cytoplasmic
Inference
High Throughput Analysis

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