Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods
Fig 2
Schematic view of data processing and analysis.
(A) 9 SSc studies were used in the current meta-analysis. (B) Preprocessed gene expression matrix of each study was projected into pathway space using GSVA algorithm (C) 9 SSc studies were projected into one pathway enrichment table (D) Multiple filters were applied to remove pathways of constant enrichment scores. (E) Consensus clustering procedure was applied and (F) optimal number of subsets were determined. (G) Machine learning procedure was used to determine best pathway modules at maximum accuracy. (H) Genes were extracted from selected top pathways modules that differentiate SSc subtypes and (I) subsequent network analysis was applied to select important gene regulators behind each subset.