Fig. 2: A schematic overview of scRNA-seq analysis pipelines.

scRNA-seq data are inherently noisy with confounding factors, such as technical and biological variables. After sequencing, alignment and de-duplication are performed to quantify an initial gene expression profile matrix. Next, normalization is performed with raw expression data using various statistical methods. Additional QC can be performed when using spike-ins by inspecting the mapping ratio to discard low-quality cells. Finally, the normalized matrix is then subjected to main analysis through clustering of cells to identify subtypes. Cell trajectories can be inferred based on these data and by detecting differentially expressed genes between clusters