ROTS: An R package for reproducibility-optimized statistical testing

Abstract

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts

“…39 The normalized data were further transformed using the voom approach in the limma R-package. 40 R package ROTS 41 was used for performing the statistical testing, and a false discovery rate < 0.05 and an absolute fold change >2 were required for detecting the differentially expressed genes between the intact and ORX groups. The hierarchical clustering of the scaled normalized expression values of differentially expressed AR-regulated genes was performed using euclidean distance and Ward's method, implemented in the R package pheatmap.…”

The PIWI protein Aubergine recruits eIF3 to activate translation in the germ plasm

“…To account for batch effects, ComBat from the R package sva was used. After quality controls, differential expression analysis was done using Reproducibility-Optimized Test Statistic (ROTS) 74 for each different comparison. P-values and FDR were extracted and plotted using self-written R scripts.…”

Gene Biomarker Prediction in Glioma by Integrating scRNA-seq Data and the Gene Regulatory Network

“…In the identi ed cell types, we combined the entire single-cell gene expression data after M3Drop feature selection and divided all cells into two groups for each cell type: the cells that belonged to the cell type and the remaining cells. The ROTS method [42] was performed to obtain the marker genes for each cell type.…”

“…We identi ed 121 regulation meta modules (RMMs) in the speci c regulatory network, and then the RMMs were considered as single-cell features to obtain the speci c regulation expression matrix. Next, we used hybrid clustering to identify the cell types, and reproducibility-optimized test statistic (ROTS) method [42] was used to identify the marker genes of different cell types. The process of cell type identi cation fully considered the differences among tumor samples from malignant cells and the effect of transcriptional regulatory mechanisms on gene expression pro les.…”