Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq

Wang, Sufang; Hu, Congzhan; Zhang, Nu; Gribskov, Michael; Yang, Hui

doi:10.1007/s12539-020-00383-7

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Shiny is an open-source R package that allows users to build interactive web applications directly from R, which can be deployed via a dedicated Shiny server or on a custom server. Several teams have recently used this tool to perform RNA-Seq [ 27 ] or transcriptomic [ 28 ] gene expression analyses, or to specifically analyze the performance of a diagnostic test [ 29 ]. However, StatiCAL is the first Shiny tool specifically designed to perform various statistical analyses dedicated to oncology.…”

Section: Discussionmentioning

confidence: 99%

StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization

Pace-Loscos,

Gal,

Contu

et al. 2024

BMC Bioinformatics

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Background In the realm of biomedical research, the growing volume, diversity and quantity of data has escalated the demand for statistical analysis as it is indispensable for synthesizing, interpreting, and publishing data. Hence the need for accessible analysis tools drastically increased. StatiCAL emerges as a user-friendly solution, enabling researchers to conduct basic analyses without necessitating extensive programming expertise. Results StatiCAL includes divers functionalities: data management, visualization on variables and statistical analysis. Data management functionalities allow the user to freely add or remove variables, to select sub-population and to visualise selected data to better perform the analysis. With this tool, users can freely perform statistical analysis such as descriptive, graphical, univariate, and multivariate analysis. All of this can be performed without the need to learn R coding as the software is a graphical user interface where all the action can be performed by clicking a button. Conclusions StatiCAL represents a valuable contribution to the field of biomedical research. By being open-access and by providing an intuitive interface with robust features, StatiCAL allow researchers to gain autonomy in conducting their projects.

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Section: Discussionmentioning

confidence: 99%

StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization

Pace-Loscos,

Gal,

Contu

et al. 2024

BMC Bioinformatics

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“…We revised platforms and software solutions that have been developed to provide the user with tools to quantify RNAseq raw data and contrast this quantified data in differential expression analysis. The revision includes tools that generate scientific plots, carry out clustering, principal component, and overrepresentation analysis [ 1 , 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 , 32 , 35 , 41 ]. However, the presented tools require either some programming expertise, or manual installation of software, or send potentially confidential data via the web to dedicated servers.…”

Section: Discussionmentioning

confidence: 99%

“…Among others, they provide means to plot the data, carry out clustering, and conduct principal component and overrepresentation analyses. A number of these tools specialize in RNAseq analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], most of which consume the raw gene expression count data produced by standard gene expression quantifiers [ 22 , 23 , 24 , 25 ] and enable the user to identify differentially expressed genes [ 6 , 7 , 8 , 9 , 11 , 12 , 13 , 15 , 16 , 20 , 21 , 26 , 27 ] and review the results in form of comprehensive reports and/or plots [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 18 , 19 , 20 , 21 , 26 , 27 , 28 ]. Some [ 7 , 8 , 9 ,…”

Section: Introductionmentioning

confidence: 99%

“…A number of these tools specialize in RNAseq analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], most of which consume the raw gene expression count data produced by standard gene expression quantifiers [ 22 , 23 , 24 , 25 ] and enable the user to identify differentially expressed genes [ 6 , 7 , 8 , 9 , 11 , 12 , 13 , 15 , 16 , 20 , 21 , 26 , 27 ] and review the results in form of comprehensive reports and/or plots [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 18 , 19 , 20 , 21 , 26 , 27 , 28 ]. Some [ 7 , 8 , 9 , 10 , 11 ,…”

Section: Introductionmentioning

confidence: 99%

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GXP: Analyze and Plot Plant Omics Data in Web Browsers

Eiteneuer

Velasco

Atemia

et al. 2022

Plants

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Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction)

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Hbpgeo: An Application System for Analyzing and Visualizing Gene Expression Data of Hypertension

LI,

TIAN,

YANG

et al. 2024

J. Mech. Med. Biol.

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The analysis of gene expression data using existing methods and tools has become a fundamental approach for researchers to explore the mechanisms of hypertension. However, many researchers lack programming skills or statistical knowledge. Although gene expression data is widely available, they originate from various experiments with different standards and quality levels, causing inconvenience to researchers. While tools and databases with interactive interfaces exist for data analysis, they may have limited applicability. This study aimed to construct High Blood Pressure Gene Expression Omnibus (HBPGEO), a program based on the R Shiny platform. It integrated the discovery of differentially expressed genes, hub gene identification, gene function analysis, and more. It allowed users to select suitable methods and set different parameters during analysis, visualize results interactively, and download the analyzed results. The study successfully utilized HBPGEO to identify hypertension-related biomarkers.

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Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq

Cited by 3 publications

References 26 publications

StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization

StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization

GXP: Analyze and Plot Plant Omics Data in Web Browsers

Hbpgeo: An Application System for Analyzing and Visualizing Gene Expression Data of Hypertension

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