Exon inclusion signatures enable accurate estimation of splicing factor activity

Splicing factors control exon inclusion in messenger RNA, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we propose that splicing factor activity can be estimated by interpreting changes in exon inclusion. To see if this is the case we benchmarked methods to construct splicing factor→exon networks and calculate splicing factor activity. Combining RNA-seq perturbation-based networks with VIPER (virtual inference of protein activity by enriched regulon analysis) accurately captures splicing factor activation modulated by different regulatory layers. This approach consolidates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements. In this dataset, we share our results from analyzing the BJ fibroblast-based model for carcinogenesis through proteomics and phosphoproteomics.