LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA

Photo by dawson2406 from unsplash

Abstract In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the… Click to show full abstract

Abstract In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a handy tool to perform comprehensive and complex data mining tasks. Recently, the deconvolution tools have led to revolutionary trends to resolve bulk RNA datasets at cell type-level resolution, interrogating the characteristics of different cell types in cancer and controlled cohorts became an important strategy to investigate the biological questions. Thus, we present GEPIA2021, a standalone extension of GEPIA, allowing users to perform multiple interactive analysis based on the deconvolution results, including cell type-level proportion comparison, correlation analysis, differential expression, and survival analysis. With GEPIA2021, experimental biologists could easily explore the large TCGA and GTEx datasets and validate their hypotheses in an enhanced resolution. GEPIA2021 is publicly accessible at http://gepia2021.cancer-pku.cn/.

Keywords: integrating multiple; analysis; gepia2021 integrating; gepia2021; deconvolution; gepia

Journal Title: Nucleic Acids Research
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.