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首页> 外文期刊>Genomics >Use of Partial Least Squares improves the efficacy of removing unwanted variability in differential expression analyses based on RNA-Seq data
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Use of Partial Least Squares improves the efficacy of removing unwanted variability in differential expression analyses based on RNA-Seq data

机译：使用部分最小二乘来提高基于RNA-SEQ数据去除差异表达分析中的不需要变异性的功效

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摘要

RNA-Seq technology has revolutionized the face of gene expression profiling by generating read count data measuring the transcript abundances for each queried gene on multiple experimental subjects. But on the downside, the underlying technical artefacts and hidden biological profiles of the samples generate a wide variety of latent effects that may potentially distort the actual transcript/gene expression signals. Standard normalization techniques fail to correct for these hidden variables and lead to flawed downstream analyses. In this work I demonstrate the use of Partial Least Squares (built as an R package 'SVAPLSseq') to correct for the traces of extraneous variability in RNA-Seq data. A novel and thorough comparative analysis of the PLS based method is presented along with some of the other popularly used approaches for latent variable correction in RNA-Seq. Overall, the method is found to achieve a substantially improved estimation of the hidden effect signatures in the RNA-Seq transcriptome expression landscape compared to other available techniques.
机译：RNA-SEQ技术通过在多个实验对象上产生针对每个查询基因的读数数据来彻底改变基因表达分析。但是在缺点，样品的潜在技术人工制品和隐藏的生物学谱产生了各种各样的潜在效应，可能潜在扭曲实际的转录物/基因表达信号。标准归一化技术无法纠正这些隐藏变量，并导致缺陷下游分析。在这项工作中，我展示了使用部分最小二乘（作为R包装'SVAPLSSEQ'）的使用，以校正RNA-SEQ数据中的无关可变量的痕迹。对基于PLS的方法的一种新颖和彻底的比较分析以及RNA-SEQ中的一些其他普遍使用的潜在可变校正方法。总体而言，与其他可用技术相比，发现该方法在RNA-SEQ转录组表达式景观中显着改善了隐性效果鉴定。

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