Advertisement

A Bloody Primer: Analysis of RNA-Seq from Tissue Admixtures

  • Casey P. ShannonEmail author
  • Chen Xi Yang
  • Scott J. TebbuttEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1712)

Abstract

RNA sequencing is a powerful technology that allows for unbiased profiling of the entire transcriptome. The analysis of transcriptome profiles from heterogeneous tissues, cell admixtures with relative proportions that can vary several fold across samples, poses a significant challenge. Blood is perhaps the most egregious example. Here, we describe in detail a computational pipeline for RNA-Seq data preparation and statistical analysis, with development of a means of estimating the cell type composition of blood samples from their bulk RNA-Seq profiles. We also illustrate the importance of adjusting for the potential confounding effect of cellular heterogeneity in the context of statistical inference in a whole blood RNA-Seq dataset.

Key words

RNA-Seq Transcriptomics Whole blood Cellular heterogeneity Cell type-specific deconvolution 

References

  1. 1.
    Andrews S. FastQC – a quality control tool for high throughput sequence data. At <http://www.bioinformatics.babraham.ac.uk/projects/fastqc/>
  2. 2.
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21CrossRefPubMedGoogle Scholar
  3. 3.
    Li B, Dewey CN (2011) RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Conda – package, dependency and environment management for any language. At <http://conda.pydata.org/miniconda.html>
  5. 5.
    Köster J, Rahmann S (2012) Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28:2520–2522CrossRefPubMedGoogle Scholar
  6. 6.
    Chikina M, Zaslavsky E, Sealfon SC (2015) CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics 31:1584–1591CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Shin H, Shannon CP, Fishbane N, Ruan J, Zhou M, Balshaw R, Wilson-McManus JE, Ng RT, McManus BM, Tebbutt SJ (2014) Variation in RNA-Seq transcriptome profiles of peripheral whole blood from healthy individuals with and without globin depletion. PLoS ONE 9:e91041CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Statist Soc B 67:301–320CrossRefGoogle Scholar
  10. 10.
    Shannon CP, Balshaw R, Ng RT, Wilson-McManus JE, Keown P, McMaster R, McManus BM, Landsberg D, Isbel NM, Knoll G, Tebbutt SJ (2014) Two-stage, in silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection. PLoS ONE 9:e95224CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.Prevention of Organ Failure (PROOF) Centre of Excellence, Centre for Heart Lung InnovationUniversity of British ColumbiaVancouverCanada
  2. 2.Division of Respiratory Medicine, Department of MedicineUniversity of British ColumbiaVancouverCanada

Personalised recommendations