Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis.

ABSTRACT: Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154

SUBMITTER: Dos Santos FRC 

PROVIDER: S-EPMC8210018 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature