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      cdev: a ground-truth based measure to evaluate RNA-seq normalization performance

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          Abstract

          Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

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          Most cited references62

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                4 October 2021
                2021
                : 9
                : e12233
                Affiliations
                [1 ]School of Computing, University of Utah , Salt Lake City, UT, United States of America
                [2 ]Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham , Birmingham, AL, United States of America
                Article
                12233
                10.7717/peerj.12233
                8496462
                34707933
                e69f7d1e-3d1e-456c-a6c7-981290ec5f9c
                ©2021 Tran and Might

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 24 March 2021
                : 9 September 2021
                Funding
                Funded by: The National Science Foundation
                Award ID: CAREER grant 1350344
                This work has been supported by the National Science Foundation (CAREER grant 1350344 to Matthew Might). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Computational Biology
                Data Science

                rna-seq,benchmarking,assessment,transcriptomics,transcriptomic profiling,normalization,svd,spike-ins,performance measure,public datasets

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