GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA
| Autor: | Zhaochen Ye, Zefang Tang, Chenwei Li, Wenjie Zhang, Fenglin Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Web server
AcademicSubjects/SCI00010 Phenylketonurias Kaplan-Meier Estimate Biology computer.software_genre 03 medical and health sciences 0302 clinical medicine Neoplasms Genetics Humans Differential expression Correlation of Data 030304 developmental biology 0303 health sciences Gene Expression Profiling Macrophages Interactive analysis Gene expression profiling Web Server Issue Correlation analysis Data mining Deconvolution computer Software 030217 neurology & neurosurgery |
| Zdroj: | Nucleic Acids Research |
| ISSN: | 1362-4962 0305-1048 |
| DOI: | 10.1093/nar/gkab418 |
| Popis: | 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/. Graphical Abstract Graphical AbstractGEPIA2021 applied CIBERSORT/EPIC/quanTIseq to deconvolute the TCGA/GTEx bulk samples with the gene signature matrix of multiple cell types. Based on the cell proportions inferred, users can perform further analysis such as proportion comparison, proportion correlation, cell type-level differential expression and survival analysis. |
| Databáze: | OpenAIRE |
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