PenDA, a rank-based method for personalized differential analysis: Application to lung cancer.

ABSTRACT: The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and

SUBMITTER: Richard M 

PROVIDER: S-EPMC7274464 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature