Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods
Fig 4
The choice of best supervised machine learning algorithms to classify SSc pathway set into 8 clusters / subsets.
(A) Different supervised classification methods were evaluated using all features, top 50 features and top 100 features by recursive feature elimination process. (B) The heatmap of 8 SSc subsets was generated based on top 80 pathways selected by random forest method according to Gini index. The 8 subsets are later defined respectively by the pathways they are enriched in. Subsequently 5 pathway modules were defined by tree-cut with visual assistance so that their average levels of expression can determine the distinct features of the 8 clusters.