MetaTiME

MetaTiME identifies meta-components from single-cell RNA sequencing (scRNA-seq) data to characterize cell types, cell states, and signaling activities within the tumor immune microenvironment (TME).

Key Features:

• Data-driven meta-components: Learns meta-components from millions of TME single cells to encapsulate independent components of gene expression.
• Biological interpretability: Meta-components represent distinct cell types, cell states, and signaling activities within the TME.
• Cross-tumor integration: Integrates multiple scRNA-seq datasets from different cancer types to identify common cell types and states across tumors.
• Annotation and projection: Provides annotation of cell states and signature continuums by projecting new scRNA-seq data onto the MetaTiME space.
• Epigenetic linkage: Incorporates epigenetics data to uncover transcriptional regulators associated with different cell states.

Scientific Applications:

• Tumor immunity profiling: Characterizes cellular states and signaling activities relevant to tumor immune microenvironments.
• Cancer immunotherapy target and biomarker discovery: Supports identification of transcriptional regulators, therapeutic targets, and biomarkers for immunotherapy.
• Non-cancerous cell heterogeneity analysis: Enables exploration of heterogeneity among non-cancerous cells in tumors and their contributions to progression and treatment response.
• Cross-tumor comparative studies: Facilitates identification of universal patterns of cell types and states across diverse cancer contexts.

Methodology:

Leverages millions of TME scRNA-seq cells in a data-driven approach to identify meta-components that capture independent gene expression components; integrates multiple scRNA-seq datasets across cancer types; projects data onto the MetaTiME space for annotation; incorporates epigenetics data to link transcriptional regulators to cell states.