Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing.

Here, researchers from Memorial Sloan Kettering Cancer Center present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. Thry find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. They have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

Effect of uniform processing and batch effect removal on expression levels in GTEx and TCGA


Two-dimensional plots are shown of principal components calculated by performing PCA of the gene expression values of bladder, prostate, and thyroid samples from GTEx and TCGA. (a) PCA of the level 3 data, i.e. the expression data from GTEx and TCGA. GTEx expression data was quantile normalized. (b) PCA of the expression data after uniform processing through our pipeline, before batch bias correction.(c)PCA of the expression data after uniform processing through our pipeline, after batch bias correction.

Wang Q et al. (2017) Enabling cross-study analysis of RNA-Sequencing data. bioRXiv [Epub ahead of print]. [abstract]

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