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Classification algorithms using RNA-Sequencing (RNA-Seq) data as input areused in a variety of biological applications. By nature, RNA-Seq data issubject to uncontrolled fluctuations both within and especially acrossdatasets, which presents a major difficulty for a trained classifier togeneralize to an external dataset. Replacing raw gene counts with the rank ofgene counts inside an observation has proven effective to mitigate thisproblem. However, the rank of a feature is by definition relative to all otherfeatures, including highly variable features that introduce noise in theranking. To address this problem and obtain more robust ranks, we propose alogistic regression model, optirank, which learns simultaneously the parametersof the model and the genes to use as a reference set in the ranking. We showthe effectiveness of this method on simulated data. We also consider realclassification tasks, which present different kinds of distribution shiftsbetween train and test data. Those tasks concern a variety of applications,such as cancer of unknown primary classification, identification of specificgene signatures, and determination of cell type in single-cell RNA-Seqdatasets. On those real tasks, optirank performs at least as well as thevanilla logistic regression on classical ranks, while producing sparsersolutions. In addition, to increase the robustness against dataset shifts, wepropose a multi-source learning scheme and demonstrate its effectiveness whenused in combination with rank-based classifiers.

Optirank: classification for RNA-Seq data with optimal ranking reference genes