scMuffin

scMuffin analyzes single-cell gene expression to dissect cellular heterogeneity in solid tumors and associate genomic aberrations with phenotypic states.

Key Features:

• Marker set expression calculation: Computes marker set expressions to distinguish normal from tumoral conditions at single-cell resolution.
• Pathway activity evaluation: Assesses pathway activity to interpret functional implications in tumor biology.
• Trajectory analysis: Infers cell state transitions and potential lineage relationships over pseudo-time.
• Copy Number Variation (CNV) assessment: Detects CNVs to link genomic alterations with cellular phenotypes.
• Transcriptional complexity and proliferation analysis: Quantifies transcriptional complexity and proliferation states to characterize cellular dynamics.
• Cell identity characterization: Integrates multiple analyses to define and refine cell identities within solid tumors.
• Clustering and subtype differentiation: Implements diverse clustering strategies to identify cell subtypes and subtle expression-state differences.
• Linking genomic aberrations to phenotypes: Associates genomic aberrations, including chromosomal amplifications, with invasive and other phenotypic signatures.

Scientific Applications:

• High-grade glioma single-cell analysis: Applied to public single-cell expression datasets from human high-grade gliomas to interrogate tumor heterogeneity.
• Genomic-phenotypic association: Identified associations between chromosomal amplifications and invasive tumor phenotypes.
• Detection of tumor-initiating-like cells: Supported the identification of cells with characteristics akin to tumor-initiating cells within tumor populations.

Methodology:

Implemented as an R package that performs marker set expression calculation, pathway activity evaluation, trajectory analysis, CNV assessment, transcriptional complexity and proliferation analysis, and clustering to link genomic aberrations with phenotypes.