Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification

Overview & Synopsis

Boruta is a widely used method for selecting gene panels from RNA sequencing data, and its most used implementation recommends Random Forest trees of depth 3 to 7, an untested setting. We tested whether this setting changes which genes are selected, using pan-cancer data from The Cancer Genome Atlas (10,469 tumors, 33 cancer types) and breast cancer PAM50 subtyping (981 tumors, 5 subtypes). A leakage-free protocol fitted every label-dependent step inside training folds, measured every effect against seed-to-seed variation, and held out a locked test set per task. In pan-cancer data, shallow trees changed the selection far more than a new seed did: the top-500 overlap with unlimited-depth selections was 0.37 at depth 5, against 0.84 to 0.93 between seeds at depth 5 or unlimited depth. Shallow trees confirmed 3.5 times fewer genes, missed markers of large classes and produced less accurate 50-gene panels (balanced accuracy 0.809 vs 0.889). The effect vanished once the cap reached about 60 to 75% of the depth that unrestricted trees grow to, set mainly by the number of classes, not sample size, and leaving depth uncapped cost no accuracy in either task. The effect replicated in a second cross-validation repeat and under permutation importance. About a third of pre-filtered candidates also tracked sample quality within class; a within-class filter removed them without loss of test accuracy (balanced accuracy 0.943 vs 0.942 with 1,000 genes). We recommend uncapped trees, or a cap chosen from the realized tree depth, and a within-class quality filter before Boruta.