CONICS

CONICS maps single-cell RNA sequencing (scRNA-seq) gene expression and quantifies copy-number alterations (CNAs) to assign expression profiles to tumor sub-clones and characterize clonal architecture and evolutionary dynamics.

Key Features:

• Copy-Number Alteration Quantification: Quantifies copy-number alterations (CNAs) directly from scRNA-seq data to assess genomic instability within tumor cells.
• Robust Separation of Cell Populations: Provides routines to separate neoplastic cells from tumor-infiltrating stromal cells for focused cancer cell analysis.
• Inter-Clone Differential Expression Analysis: Compares gene expression between distinct tumor clones to identify clonal-specific expression patterns.
• Intra-Clone Co-Expression Analysis: Analyzes gene co-expression within individual clones to explore functional networks and pathways active in sub-clones.

Scientific Applications:

• Tumor Heterogeneity: Maps single-cell expression to tumor sub-clones to characterize intra-tumor heterogeneity at the transcriptional and genomic levels.
• Tumor Phylogeny and Clonal Evolution: Integrates expression and genomic mutation data to relate gene expression patterns to tumor phylogenies and clonal evolution.
• Progression, Metastasis, and Resistance Mechanisms: Identifies clonal-specific expression signatures relevant to tumor progression, metastasis, and therapeutic resistance.

Methodology:

Implemented in Python and R; performs noise reduction and normalization of scRNA-seq data; quantifies CNAs from scRNA-seq; integrates genomic mutation information to map expression to tumor sub-clones; includes routines for separating neoplastic from stromal cells and for inter-clone differential expression and intra-clone co-expression analyses.