Unit-free and robust detection of differential expression from RNA-Seq data

TL;DR

This paper introduces a unified, unit-free statistical model for detecting differential gene expression from RNA-Seq data that also performs normalization, effectively handling cases with high proportions of differentially expressed genes.

Contribution

The proposed method jointly normalizes and detects differential expression in RNA-Seq data, independent of expression measurement units, and is effective even with high asymmetric differential expression.

Findings

Reliable normalization and detection when over 50% of genes are differentially expressed

Outperforms existing methods on simulated and real datasets

Effective in asymmetric differential expression scenarios

Abstract

Ultra high-throughput sequencing of transcriptomes (RNA-Seq) is a widely used method for quantifying gene expression levels due to its low cost, high accuracy and wide dynamic range for detection. However, the nature of RNA-Seq makes it nearly impossible to provide absolute measurements of transcript abundances. Several units or data summarization methods for transcript quantification have been proposed in the past to account for differences in transcript lengths and sequencing depths across different genes and different samples. Nevertheless, further between-sample normalization is still needed for reliable detection of differentially expressed genes. In this paper we propose a unified statistical model for joint detection of differential gene expression and between-sample normalization. Our method is independent of the unit in which gene expression levels are summarized. We also introduce an efficient algorithm for model fitting. Due to the L0-penalized likelihood used in our model, it is able to reliably normalize the data and detect differential gene expression in some cases when more than $50\%$ of the genes are differentially expressed in an asymmetric manner. We compare our method with existing methods using simulated and real data sets.