TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

By Dongqing Sun, Jin Wang, Ya Han, Xin Dong, Jun Ge, Rongbin Zheng, Xiaoying Shi, Binbin Wang, Ziyi Li, Pengfei Ren and 6 more. bioRxiv, 2020

894 citations recorded by the source corpus.

Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors, but also posed computational challenges on how to integrate and utilize the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly two million cells from 76 high-quality tumor datasets across 28 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, malignant cell classification, cell clustering, cell-type annotation, differential expression analysis, and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching, and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

Subfields: Biophysics, Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f9acf5cd47af3a0bde22ec39d82369561f3a076e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ya Han
2. Yuting Wang
3. Xin Dong
4. Zhaoyang Liu
5. Jiali Yue
6. Dongqing Sun
7. Haiyun Wang
8. Taiwen Li

Citations in the corpus

• TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment (2022, methodology): “Previously, we developed TISCH (14), an scRNA-seq database focused on the scRNA-seq datasets from the TME.”