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. 2017 Oct 3;18(1):437.
doi: 10.1186/s12859-017-1847-x.

Tissue-aware RNA-Seq Processing and Normalization for Heterogeneous and Sparse Data

Affiliations
Free PMC article

Tissue-aware RNA-Seq Processing and Normalization for Heterogeneous and Sparse Data

Joseph N Paulson et al. BMC Bioinformatics. .
Free PMC article

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-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project.

Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

Keywords: Filtering; GTEx; Normalization; Preprocessing; Quality control; RNA-Seq.

Conflict of interest statement

Ethics approval and consent to participate

This work was conducted under dbGaP approved protocol #9112 (accession phs000424.v6.p1).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Preprocessing workflow for large, heterogeneous RNA-Seq data sets, as applied to the GTEx data. The boxes on the right show the number of samples, genes, and tissue types at each step. First, samples were filtered using PCoA with Y-chromosome genes to test for correct annotation of the sex of each sample. PCoA was used to group or separate samples derived from related tissue regions. Genes were filtered to select a normalization gene set to preserve robust, tissue-dependent expression. Finally, the data were normalized using a global count distribution method to support cross-tissue comparison while minimizing within-group variability
Fig. 2
Fig. 2
PCoA analysis allows for grouping of subregions for greater power. Scatterplots of the first and second principal coordinates from principal coordinate analysis on major tissue regions. a Aorta, coronary artery, and tibial artery form distinct clusters. b Skin samples from two regions group together but are distinct from fibroblast cell lines, a result that holds up (c) when removing the fibroblasts
Fig. 3
Fig. 3
Six highly expressed tissue-specific genes that are removed upon tissue-agnostic filtering. Boxplots of continuity-corrected log2 counts for six tissue-specific genes (a-f). These genes are retained when considering tissue-specificity and not when filtering in an unsupervised manner. Colors represent different tissues. Examples include (a) MUC7, (b) REG3A, (c) AHSG, (d) GKN1, (e) SMCP, and (f) NPPB
Fig. 4
Fig. 4
Using a tissue-defined reference lowers root mean squared error. Boxplots of the RMSE comparing the log-transformed quantiles of each sample to the reference defined using (left) all tissues and samples and the (right) reference defined using samples of the same tissue

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