Letter to the Editor response: Nygaard
            <i>et al</i>
            .: Table 1.

Towfic, Fadi; Kusko, Rebecca; Zeskind, Benjamin

doi:10.1093/biostatistics/kxw031

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…We subsequently used ComBat+Cor ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\zeta$\end{document} = 1%) to adjust for correlations introduced by the unbalanced batch-group design and found no genes were significant at 5% FDR level. However, we recognize that different models for differential expression, including mixed effects effects models, have led to deferentially expressed genes in this data set ( Towfic and others , 2017 ; Nygaard and others , 2017 ). These can be further explored in the future with ComBat+Cor, but for the sake of this work, our goal was to recreate the work of Nygaard and others (2016) .…”

Section: Resultsmentioning

confidence: 99%

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Zhang

Patil

et al. 2021

Biostatistics

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Summary Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the $\texttt{sva}$ R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html).

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Section: Resultsmentioning

confidence: 99%

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Zhang

Patil

et al. 2021

Biostatistics

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“…In summary, our integrated meta longitudinal dataset shows that technical variability in library prep exerts a much greater effect compared to batch effects between different sequencing runs with a time-lag. This suggests that such unwanted nuisance factors should be removed prior to subsequent major analyses to reduce significant confounding effects on the main effects of interest and misleading results of statistical testing of the model 14–18,24 . In the analyses of pipeline 3, we first examined the effect of the correction of systematic artifacts using the static methods, edgeR and DESeq.…”

Section: Resultsmentioning

confidence: 99%

Temporal dynamics in meta longitudinal RNA-Seq data

Baldwin

et al. 2019

Sci Rep

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Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.

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Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

2021

Genes

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Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

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Letter to the Editor response: Nygaard et al .: Table 1.

Cited by 3 publications

References 8 publications

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Temporal dynamics in meta longitudinal RNA-Seq data

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

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