Fig. 3: Overview of computational workflow for single-cell analyses on human AT.

The workflow begins with aligning reads to the genome using, for example, Cell Ranger. Ambient RNA is removed using tools such as CellBender, SoupX or DecontX and doublets are identified and removed using scDblFinder, scdx, DoubletFinder or scrublet. Cells and genes are filtered on the basis of QC metrics. Data are then combined and integrated using Seurat or Scanpy, with integration methods such as Harmony, canonical correlation analysis (CCA) and reciprocal principal component analysis (RPCA). Clusters and marker genes are identified using Seurat, Scanpy or Liger. Data can be mapped to reference datasets using Azimuth or CellTypist. Additional analyses include trajectory analysis with slingshot or monocole3, differential expression analysis using pseudobulk, and prediction of cell–cell interactions with CellphoneDB or Cellchat. Listed programmes are suggestions and not exhaustive.