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      TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

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          Abstract

          Objective

          Differential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users.

          Results

          Here, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use ( https://infinityloop.shinyapps.io/TCC-GUI/). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.

          Electronic supplementary material

          The online version of this article (10.1186/s13104-019-4179-2) contains supplementary material, which is available to authorized users.

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          Most cited references11

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          TCC: an R package for comparing tag count data with robust normalization strategies

          Background Differential expression analysis based on “next-generation” sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy. Results TCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC. Conclusion DEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.
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            Points of Significance: Principal component analysis

            PCA helps you interpret your data, but it will not always find the important patterns.
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              The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster.

              Here we present a statistically rigorous approach to quantifying microarray expression data that allows the relative effects of multiple classes of treatment to be compared and incorporates analytical methods that are common to quantitative genetics. From the magnitude of gene effects and contributions of variance components, we find that gene expression in adult flies is affected most strongly by sex, less so by genotype and only weakly by age (for 1- and 6-wk flies); in addition, sex x genotype interactions may be present for as much as 10% of the Drosophila transcriptome. This interpretation is compromised to some extent by statistical issues relating to power and experimental design. Nevertheless, we show that changes in expression as small as 1.2-fold can be highly significant. Genotypic contributions to transcriptional variance may be of a similar magnitude to those relating to some quantitative phenotypes and should be considered when assessing the significance of experimental treatments.
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                Author and article information

                Contributors
                suwei@bi.a.u-tokyo.ac.jp
                wukong@bi.a.u-tokyo.ac.jp
                shimizu@bi.a.u-tokyo.ac.jp
                kadota@bi.a.u-tokyo.ac.jp
                Journal
                BMC Res Notes
                BMC Res Notes
                BMC Research Notes
                BioMed Central (London )
                1756-0500
                13 March 2019
                13 March 2019
                2019
                : 12
                : 133
                Affiliations
                [1 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Graduate School of Agricultural and Life Sciences, , The University of Tokyo, ; Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-8657 Japan
                [2 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Collaborative Research Institute for Innovative Microbiology, , The University of Tokyo, ; Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-8657 Japan
                Author information
                http://orcid.org/0000-0002-3907-4336
                Article
                4179
                10.1186/s13104-019-4179-2
                6417217
                30867032
                4c094dae-25a3-4ac5-b36d-7ad81b1f0d93
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 January 2019
                : 11 March 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: JP15K06919
                Award ID: JP18K11521
                Award Recipient :
                Categories
                Research Note
                Custom metadata
                © The Author(s) 2019

                Medicine
                rna-seq,bioinformatics,differential expression analysis,shiny app
                Medicine
                rna-seq, bioinformatics, differential expression analysis, shiny app

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