Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data.

ABSTRACT:

Background

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis.

Results

We find that analysis of large RNA-

SUBMITTER: Paulson JN 

PROVIDER: S-EPMC5627434 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature