Publishes on Molecular Biology Techniques and Applications, Single-cell and spatial transcriptomics, Gene expression and cancer classification. 60 papers and 18.5k citations.
Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.
Nature Communications 8: Article number:15308 (2017); Published: 18 May 2017; Updated: 16 June 2017 The original version of this Article contained an error in the spelling of the author Zemin Zhang, which was incorrectly given as Zeming Zhang. This has now been corrected in both the PDF and HTML versions of the Article.
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/.
Abstract Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain 1,2 , but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS 3,4 , to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm 3 , mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas 1 , imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB 5,6 . Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.