scCancer

scCancer performs processing and analysis of droplet-based single-cell RNA sequencing (scRNA-seq) data to identify tumor cell populations and characterize intra-tumor heterogeneity.

Key Features:

• Comprehensive Quality Control Metrics: Provides a suite of quality-control metrics for filtering low-quality cells and technical artifacts in scRNA-seq data.
• Data-Driven Machine Learning Algorithm: Implements a data-driven machine learning algorithm to identify major cell populations within the tumor microenvironment.
• Malignancy Score Estimation: Estimates malignancy scores to classify cells as malignant or non-malignant.
• Intra-Tumor Heterogeneity Analysis: Assesses cell cycle status, stemness, and gene signature patterns to analyze intra-tumor heterogeneity.
• Graphic Report Generation: Generates comprehensive graphic reports summarizing performed analyses.

Scientific Applications:

• Transcriptomic Heterogeneity Dissection: Dissects transcriptomic heterogeneities at single-cell resolution to uncover tumor subpopulations.
• Therapeutic Target and Resistance Analysis: Supports identification of potential therapeutic targets and mechanisms of treatment resistance through cell-type and signature analysis.
• Tumor Microenvironment Characterization: Characterizes tumor microenvironments to inform strategies for modulating immune responses or targeting specific cell populations.

Methodology:

Workflows explicitly include quality control, a data-driven machine learning algorithm for cell population identification, malignancy score estimation, assessment of cell cycle status, stemness and gene signatures for intra-tumor heterogeneity analysis, and generation of graphic reports for droplet-based scRNA-seq datasets.