Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
Fig 9
scAAnet identified 4 GEPs using microglia in the prefrontal cortex dataset.
(a) UMAP of the observed scRNA-seq data colored by microglia subclusters. (b) UMAP of the observed scRNA-seq data colored by AD pathology group. (c) UMAP of the observed scRNA-seq data colored by sex. Microglia subclusters, AD pathology group and sex are provided by the original paper. Black dots in a, b and c are locations of cells that have the largest usage of the corresponding GEP (marked in Arabic numerals). (d) UMAPs colored by inferred cell usage for each GEP. (e) Heatmap showing the enrichment of identified DEGs in each GEP across sets of marker genes of the four microglia subclusters. Colors are negative log of adjusted p-values from hypergeometric tests. P-values were adjusted by Bonferroni over all GEPs and subclusters. (f) Heatmap showing the percentage of cells with usage > 25% of each GEP (rows) in each subcluster and AD group (columns). (g) Heatmap showing the percentage of cells with usage > 25% of each GEP (rows) in each subcluster and sex (columns). Colors of subclusters, AD group and sex in f and g are coded in the same way as colors in a, b and c.