TIMER is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. This version of webserver provides immune infiltrates' abundances estimated by multiple immune deconvolution methods, and allows users to generate high-quality figures dynamically to explore tumor immunological, clinical and genomic features comprehensively.
Geting started by exploring:Note: the estimation scores of TIMER2.0 might differ slightly from previous versions of TIMER due to the different number of available TCGA samples used for batch correction in the current run.
Tutorial:Taiwen Li#, Jingxin Fu#, Zexian Zeng, David Cohen, Jing Li, Qianming Chen, Bo Li, and X. Shirley Liu. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Research 2020 [DOI] [PubMed]
Taiwen Li, Jingyu Fan, Binbin Wang, Nicole Traugh, Qianming Chen, Jun S. Liu, Bo Li, X. Shirley Liu. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Research. 2017;77(21):e108-e110. [DOI] [PubMed]
Bo Li, Eric Severson, Jean-Christophe Pignon, Haoquan Zhao, Taiwen Li, Jesse Novak, Peng Jiang, Hui Shen, Jon C. Aster, Scott Rodig, Sabina Signoretti, Jun S. Liu, X. Shirley Liu. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology. 2016;17(1):174. [DOI] [PubMed]
Taiwen Li:
litaiwen@scu.edu.cn
Jingxin Fu:
jxfu@ds.dfci.harvard.edu
X Shirley Liu:
xsliu@ds.dfci.harvard.edu
TIMER v2.0 | © X Shirley Liu Lab 2020 | Dana Farber Cancer Institute
Modules exploring the association between immune infiltrates and genomic changes or clinical outcome in TCGA:
The pop-up function by click the cell will display Kaplan-Meier curves for the corresponding immune infiltrates and cancer types. The infiltration level is divided into low and high levels which could be adjusted by a user-defined slider. The hazard ratio and p value for Cox model and the log-rank p value for KM curve will be shown on the KM curve plot.
Modules exploring associations between gene expression and tumor features in TCGA: