Single-cell transcriptomics

Single-cell technologies are based on separating individual cells and measuring their molecular characteristics, which brings great opportunity to investigate cellular heterogeneity in terms of gene expression and other functions between various tissues and conditions. Unfortunately, single-cell level gene expression data are more variable and noisier than standard RNAseq data because the transcriptional signal is not averaged across cells, thus proper processing and analysis are needed. In this chapter, we present consequent steps of data preprocessing that leads to an increase in the signal-to-noise ratio and discuss possible scenarios of data analysis, including finding differentially expressed genes between groups of cells, clustering of similar cell types, or cell trajectory inference. In addition, using the breast cancer cell line data acquired with the 10X Chromium sequencing platform, we visualized certain steps of data analysis. Despite the wide range of methods introduced within the last years, there are still no standards used in everyday practice.