Preprocess raw read count data to make expression values comparable across cells and genes. Use Gene Markers and Score Cells to estimate the cell cycle phase of each cell and regress out this confouding factor.
Discovering new marker genes is a core step in the analysis of single-cell sequencing data. Use Louvain clustering to find cell populations and visualize the cell landscape in a t-SNE projection. Examine the contents of each cluster by finding differentially expressed genes and their related functions according to the Gene Ontology database.
Single-cell RNA sequencing protocols enable measuring the transcriptome on a single-cell resolution. We explore the different cell types in a blood sample by a integrating existing knowledge on known marker genes and a t-SNE visualization, focusing on interactive exploration of data.