lineage

lineage identifies endogenous lineage markers from mitochondrial reads in label-free single-cell RNA sequencing (scRNA-seq) data to enable lineage inference without exogenous barcodes or bulk assays.

Key Features:

• Label-Free Identification: Operates without exogenous barcodes or additional bulk assays for lineage marker discovery from scRNA-seq data.
• Endogenous Marker Screening: Identifies informative single-cell mitochondrial RNA mutations as endogenous markers for lineage inference.
• Low Cross-Entropy Subspaces Identification: Integrates identification of low cross-entropy subspaces, considering mutation type and subspace-subspace cross-entropy of features to improve marker selection.
• Consensus Clustering Method: Combines feature subspace separation with a consensus clustering approach to cluster cells based on lineage-related characteristics.
• Computational Efficiency and Biological Accuracy: Demonstrated superior biological accuracy and computational efficiency in comparative studies on standard datasets.

Scientific Applications:

• Lineage Inference: Uses mitochondrial RNA mutations as markers to infer cell fate and developmental pathways at single-cell resolution.
• Cancer Research: Applied to a label-free scRNA-seq dataset of BRAF-mutated cancer cells to identify genes associated with resistance to BRAF inhibitors.

Methodology:

Marker selection via separation of feature subspaces and identification of low cross-entropy subspaces that consider mutation type and subspace-subspace cross-entropy, followed by consensus clustering of cells using the resulting feature subspaces.