MetroTIME: Pan-cancer single-cell metabolism and regulatory analyses


Cite us!

Ke Tang, Ya Han, Dongqing Sun, Xin Dong, Tong Han, Hailin Wei, Junjie Hu, Zhaoyang Liu, Qiu Wu*, Chenfei Wang*. Pan-cancer single-cell metabolism and regulatory analyses identifies microenvironment metabolic subtypes associated with tumor immunity. [DOI] [PubMed]

Note:

Code related to the analyses in the MertoTIME can be found on GitHub at https://github.com/Tangke98/MetroTiME

Contact:

Ke Tang: cocot@tongji.edu.cn
Ya Han: hanyaya@tongji.edu.cn
Qiu Wu: qiu_wu@tongji.edu.cn
Chenfei Wang: 08chenfeiwang@tongji.edu.cn


MetroTIME | Chenfei Wang Lab 2024

沪ICP备2024096254-1


Instruction: The MetaModule Score module allows users to explore metabolic subtypes in depth, beginning with clustering results for each metabolic subtype and information on their cell origins, dataset sources, and clinical relevance. In the MetaModule Score Exploration section, users can delve into gene expression and MetaModule scores across individual cells. Additionally, we provide the top 100 MetaModules specifically upregulated within each metabolic subtype.

Individual MetaModule Score Exploration


Top Differential Expressed MetaModules Across Each Metabolic Subtype


Download Current Data


Instruction: The MetaRegulon Score module enables users to explore the mechanisms of metabolic regulation within and between cells for each metabolic subtype. Transcription factors (TFs) and signaling components (genes) serve as cell-intrinsic factors, while ligands represent cell-extrinsic factors. In the top section, users can compare regulatory differences and similarities across metabolic subtypes. Additionally, detailed information is provided for the selected MetaRegulon within the specified metabolic subtype.

Download Current Data


Download Current Data


Instruction: The MetaRegulon Score module enables users to explore the mechanisms of metabolic regulation within and between cells for each metabolic subtype. Transcription factors (TFs) and signaling components (genes) serve as cell-intrinsic factors, while ligands represent cell-extrinsic factors. In the top section, users can compare regulatory differences and similarities across metabolic subtypes. Additionally, detailed information is provided for the selected MetaRegulon within the specified metabolic subtype.

Hallmarks

Functions

Download Current Data

Download Current Data


Instruction: The Patient Outcome module allows users to explore the clinical relevance of specific MetaModules and their upstream MetaRegulons. For each MetaModule, we calculate patient-specific scores using the cal_MetaModule function within MetroSCREEN on TCGA data. In the MetaRegulon Score module, we leverage single-cell data to infer MetaRegulons associated with each MetaModule in fibroblasts and myeloid cells, hypothesizing that these MetaRegulons regulate similar metabolic modules across different cell types. This analysis focuses on the top 30 MetaRegulons predicted for each MetaModule in single-cell data. Patient samples are grouped according to the median values of both MetaModule scores and MetaRegulon gene expression levels. Univariate Cox regression is then applied to evaluate the relationship between these scores and survival times across various cancer types. Additionally, Kaplan-Meier survival curves are used to compare high and low score groups based on their median values.

MetaModules Clinical Association

MetaModules Immunosuppressive Association

MetaModule and MetaRegulon Clinical Relevance Exploration



Instruction: The ICB Response module enables users to explore the relevance of immune checkpoint blockade (ICB) for specific MetaModules. The ICB dataset, which includes data from 296 patients, is available for download. For each MetaModule, we calculate MetaModule scores across these patients using the cal_MetaModule function within MetroSCREEN. To assess differences between responsive and non-responsive patients, we compute a differential score by subtracting the MetaModule score of responsive patients from that of non-responsive patients. Additionally, we have included literature-supported biomarkers to help users more effectively identify the most relevant MetaModules.

MetaModules ICB Response Prediction





Instruction: The Drug Response module enables users to explore the relevance of drug response for specific MetaModules. Users can download the AUC values of cancer cell lines, which represent drug response. For each MetaModule, we calculate the Pearson correlation between the MetaModule score and the AUC values of various drugs. A negative correlation indicates that the MetaModule is responsive to the drug. To enhance visualization, we display the reverse Pearson correlation values and showcase the top 50 drugs to which each MetaModule is most responsive.