Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from large and complex RNA-seq studies. Technical replicates together with negative and positive control genes are key tools for carrying out this task. We show how to proceed when technical replicates are unavailable.
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References
Gauss, C. F. & Stewart, G. W. Theory of the combination of observations least subject to errors, Part One, Part Two, Supplement. Classics in Applied Mathematics https://doi.org/10.1137/1.9781611971248 (SIAM, 1995). English translation of Gauss’s classic 1823 work in which, amongst much else, systematic errors are noted.
Ku, H. H. Precision Measurement and Calibration. Volume 1. Statistical Concepts and Procedures (National Bureau of Standards, 1969). A collection of papers dealing with random and systematic errors in the context of the art and science of measurement.
Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010). A review article whose title says it all.
Molania, R. et al. A new normalization for Nanostring nCounter gene expression data. Nucleic Acids Res. 47, 6073–6083 (2019). This paper presents RUV-III and includes some examples using technical replicates and others using pseudo-replicates.
Vallejos, C. A. et al. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017). A critical review of the task of normalization in the context of single cell RNA-seq, with much relevance to bulk RNA-seq.
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This is a summary of: Molania, R. et al. Removing unwanted variation from large-scale RNA sequencing data with PRPS. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01440-w (2022)
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Normalizing cancer RNA-seq data for library size, tumor purity and batch effects. Nat Biotechnol 41, 27–28 (2023). https://doi.org/10.1038/s41587-022-01441-9
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DOI: https://doi.org/10.1038/s41587-022-01441-9
