Identifying Differentially Expressed Genes in RNA Sequencing Data with Small Labelled Samples

RNA-seq, including bulk RNA-seq and single-cell RNA-seq, is a next-generation sequencing-based RNA profiling method capable of measuring gene expression patterns with high resolution, and has gradually become an essential tool for the analysis of differential gene expression at the whole transcriptome level. Differential gene identification is a key problem in many biological studies such as disease genetics. Two-sample location test methods are widely used in case-control studies to identify the significant differential genes. However, due to the high cost of labelled data collection, many studies face the small sample problem since there is only small labelled data available, for which the traditional methods often lose power. To address this issue, we propose a novel rank-based nonparametric test method called WMW-A test based on
W
ilcoxon-
M
ann-
W
hitiney test by introducing a three-sample statistic through another
a
uxiliary sample, which is either given or generated in form of unlabelled data. By combining the case, control and auxiliary samples together, we construct a three-sample WMW-A statistic based on the gap between the average ranks of the case and control samples in the combined samples. The extensive simulation experiments and real applications on different gene expression datasets, including one bulk RNA-seq dataset and two single cell RNA-seq datasets, show that the WMW-A test could significantly improve the test power for two-sample problem with small sample sizes, by either available or generated auxiliary data. The applications on two real small SARS-CoV-2 datasets further show the improvement of WMW-A test for differentially expressed gene identification with small labelled samples.