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.
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References
Andrews S. FastQC – a quality control tool for high throughput sequence data. At <http://www.bioinformatics.babraham.ac.uk/projects/fastqc/>
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 29:15–21
Li B, Dewey CN (2011) RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323
Conda – package, dependency and environment management for any language. At <http://conda.pydata.org/miniconda.html>
Köster J, Rahmann S (2012) Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28:2520–2522
Chikina M, Zaslavsky E, Sealfon SC (2015) CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics 31:1584–1591
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:e91041
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–e47
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Statist Soc B 67:301–320
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:e95224
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Shannon, C.P., Yang, C.X., Tebbutt, S.J. (2018). A Bloody Primer: Analysis of RNA-Seq from Tissue Admixtures. In: Head, S., Ordoukhanian, P., Salomon, D. (eds) Next Generation Sequencing. Methods in Molecular Biology, vol 1712. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7514-3_12
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DOI: https://doi.org/10.1007/978-1-4939-7514-3_12
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Publisher Name: Humana Press, New York, NY
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Online ISBN: 978-1-4939-7514-3
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