CHARTS

CHARTS characterizes tumor subpopulations in publicly available single-cell RNA sequencing (scRNA-seq) datasets to enable comparative analysis of cellular heterogeneity in cancer.

Key Features:

• Integration of public scRNA-seq datasets: Aggregates multiple publicly accessible single-cell RNA sequencing cancer datasets for unified analysis.
• Comprehensive analysis pipeline: Performs individual gene expression profiling, cell type identification, malignancy status assessment, and differential expression analysis within tumor subpopulations.
• Gene set enrichment analysis: Conducts gene set enrichment analyses on cell subpopulations across datasets to identify active biological pathways.
• Cross-sample comparison: Enables comparison of gene expression patterns and cellular characteristics of tumor subpopulations across different samples and datasets.

Scientific Applications:

• Tumor heterogeneity analysis: Enables detailed investigation of intratumoral cellular heterogeneity relevant to disease progression, metastasis, drug resistance, and immune evasion.
• Comparative molecular profiling: Identifies shared and unique molecular features among tumor subpopulations across datasets.
• Pathway and functional inference: Reveals biological pathways and processes active within specific tumor subtypes using gene set enrichment.
• Malignancy and cell type assessment: Facilitates assessment of malignancy status and cell type composition within tumor samples.

Methodology:

Integrates data from various sources and compares gene expression patterns and cellular characteristics across samples to identify unique or shared features among tumor cells.