We therefore developed an analysis method based on independent components analysis (ICA)(Figure 1B–E). ICA reduces large datasets to a smaller number of dimensions in which entities (here cells) have score distributions that are maximally structured – as measured by deviation from a normal distribution (generally due to a spiky or clustered distribution of the cells in that dimension) – and statistically independent (Hyvärinen, 1999). Each of the inferred independent components (ICs) is a weighted combination of many genes (the weight of each gene’s contribution to an IC is the gene “loading”) and each cell is given a score for each IC (cell loading). This score reflects the degree to which the constellation of genes encoded by the IC is more or less expressed in that cell as compared to the average cell in the analysis. Each cell’s gene-expression profile is a weighted sum of ICs.
