Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates

Abstract

A major application of RNA-Seq is to perform differential gene expression analysis. Many tools exist to analyze differentially expressed genes in the presence of biological replicates. Frequently, however, RNA-Seq experiments have no or very few biological replicates and development of methods for detecting differentially expressed genes in these scenarios is still an active research area. In this paper we introduce a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. We compared IsoDE against four existing methods (Fisher’s exact test, GFOLD, edgeR and Cuffdiff) on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. Experiments on MAQC RNA-Seq datasets without replicates show that IsoDE has consistently high accuracy as defined by the qPCR ground truth, frequently higher than that of the compared methods, particularly for low coverage data and at lower fold change thresholds. In experiments on RNA-Seq datasets with up to 7 replicates, IsoDE has also achieved high accuracy. Furthermore, unlike GFOLD and edgeR, IsoDE accuracy varies smoothly with the number of replicates, and is relatively uniform across the entire range of gene expression levels. The proposed non-parametric method based on bootstrapping has practical running time, and achieves robust performance over a broad range of technologies, number of replicates, sequencing depths, and minimum fold change thresholds.

Figure 1

Sensitivity, PPV, and F-Score of IsoDE-Match (M = 200 bootstrap samples per condition) on the Illumina MAQC data, with varying bootstrap support threshold.

Figure 2

Running times (in seconds) of IsoDE-Match with M = 200 and IsoDE-All with M = 20 on several MAQC datasets. The indicated number of reads represents the total number of mapped reads over both conditions of each dataset, for more information on the datasets see Table S1.

Figure 3

Sensitivity, PPV, F-Score, and accuracy of IsoDE-All (with 20 bootstrap runs per condition), edgeR, and GFOLD on the Illumina MCF-7 data with minimum fold change of 1 and varying number of replicates.

Figure 4

Sensitivity, PPV, and F-Score of IsoDE-All (with 20 bootstrap runs per condition), edgeR, and GFOLD on the Illumina MCF-7 data, computed for quintiles of expressed genes after sorting in non-decreasing order of average FPKM for IsoDE and GFOLD and average count of uniquely aligned reads for edgeR. First quintile of edgeR had 0 differentially expressed genes according to the ground truth (obtained by using all 7 replicates).