A Unified Model for Differential Expression Analysis of RNA-seq Data via L1-Penalized Linear Regression

TL;DR

This paper introduces a unified statistical model that simultaneously normalizes RNA-seq data and detects differential gene expression, improving accuracy over traditional methods by jointly estimating normalization factors and expression differences.

Contribution

It proposes a joint normalization and differential expression detection model using L1-penalized linear regression, addressing limitations of existing ad hoc normalization approaches.

Findings

Outperforms existing methods in detection power

Reduces false-positive rates in differential expression analysis

Effective when many genes are differentially expressed

Abstract

The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization of counts is an essential step in differential expression (DE) analysis. The normalization of existing DE detection algorithms is ad hoc and performed once for all prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2-norm for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented lagrangian method to solve it. Simulation studies show that the proposed model and algorithms outperform existing methods in terms of detection power and false-positive rate when more than half of the genes are differentially expressed and/or when the up- and down-regulated genes among DE genes are unbalanced in amount.