FIGURE 2.

Comparison of SCENIC and the Inferelator, two scGRN prediction algorithms. The SCENIC and Inferelator algorithms both use scRNA‐seq data as their primary input. SCENIC uses a random forest clustering algorithm to identify target genes that are co‐expressed with transcription factors. It then filters the putative regulatory clusters to retain only those whose targets are enriched for the occurrence of a priori known cisregulatory elements for the relevant transcription factor. Inferelator uses a multi‐task learning algorithm to learn scGRNs from transcriptome and complementary data types that are used to estimate the activity of transcription factors. The Inferelator can accept a wide variety of complementary inputs including Chromatin Immunoprecipitation ‐ sequencing (ChIP‐seq), protein–protein interaction and can explicitly use time series data. Both algorithms produce a matrix or graph of transcription factor target interactions [Colour figure can be viewed at wileyonlinelibrary.com]