Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the transcriptome of individual cells. However, the data from scRNASeq experiments is often both noisy and high dimensional, making the computational analysis non-trivial. Here researchers from the Karolinska Institutet provide an overview of different experimental protocols and the most popular methods for facilitating the computational analysis. They focus on approaches for identifying biologically important genes, projecting data into lower dimensions and clustering data into putative cell-populations. Finally they discuss approaches to validation and biological interpretation of the identified cell-types or cell-states.
Overview of methods covered in this review
Colour indicates which parts of the expression matrix are adjusted after each step, for instance feature selection only removes rows from the expression matrix, whereas dimensionality reduction calculates a new matrix composed of meta-features. Preprocessing steps not covered in detail in this review include quality control and normalization. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)