Dataset Information

A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

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 is an essential step in differential expression (DE) analysis. The normalization step of existing DE detection algorithms is usually ad hoc and performed only once 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 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 penalty 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 and real data studies show that the proposed model and algorithms perform better than or comparably to existing methods in terms of detection power and false-positive rate. The performance gain increases with increasingly larger sample size or higher signal to noise ratio, and is more significant when a large proportion of genes are differentially expressed in an asymmetric manner.

SUBMITTER: Liu K

PROVIDER: S-EPMC6686202 | biostudies-literature | 2019 Mar-Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

Liu Kefei K Ye Jieping J Yang Yang Y Shen Li L Jiang Hui H

IEEE/ACM transactions on computational biology and bioinformatics 20180108 2

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 is an essential step in differential expression (DE) analysis. The normalization step of existing DE detection algorithms is usually ad hoc and performed only once 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 ...[more]

PMID: 29993952

Similar Datasets

Project description:A generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.We found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.The best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.

Dataset Information

A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

Publications

A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets