cardelino

cardelino infers clonal tree configuration for a population of cells and assigns cells to inferred clones using single-cell RNA-seq data (scRNA-seq), integrating information from imperfect clonal trees based on bulk exome-seq data and sparse variant alleles expressed in scRNA-seq data.

Key Features:

• Clonal tree inference: Methods infer clonal tree configuration for a population of cells using scRNA-seq data and possibly other data modalities.
• Cell-to-clone assignment: Methods assign individual cells to inferred clones based on sparse variant alleles expressed in scRNA-seq data.
• Gene expression comparison: Methods explore differences in gene expression between inferred clones.
• Flexible data integration: Methods flexibly integrate information from imperfect clonal trees inferred from bulk exome-seq data with sparse variant alleles from scRNA-seq data.
• Beta-binomial error model: A flexible beta-binomial error model accounts for stochastic dropout events and systematic allelic imbalance.

Scientific Applications:

• Cancer clonal substructure analysis: Application to published cancer datasets enables reconstruction of clonal substructures and characterization of phenotypic variations between clones.
• Somatic evolution in healthy tissues: Application to matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines identified hundreds of differentially expressed genes between cells from different somatic clones, with enrichment for cell cycle and proliferation pathways.
• Phenotypic variation characterization: Enables characterization of phenotypic variations between clones that are not established by bulk and single-cell DNA sequencing approaches alone.

Methodology:

cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data, using a flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance.