GEMLI

GEMLI infers cellular lineages and heritable gene expression patterns from single-cell RNA sequencing (scRNA-seq) data to assign transcriptomes to lineage trees and identify small to medium-sized cell lineages.

Key Features:

• Lineage Tree Assignment: Assigns single-cell transcriptomes to cellular lineage trees from scRNA-seq data.
• Heritable Gene Expression Analysis: Detects heritable gene expression patterns and distinguishes symmetric versus asymmetric cell fate decisions.
• Multicellular Structure Reconstruction: Reconstructs individual multicellular structures from pooled scRNA-seq datasets.
• Memory-based Prediction Algorithms: Uses memory-based prediction algorithms to identify lineage-specific gene expression patterns without prior lineage information.
• Disease-associated Expression Changes: Has revealed gene expression changes at the onset of cancer invasiveness in human breast cancer biopsies.
• Universal Applicability: Applicable to study small cell lineages across diverse physiological and pathological contexts, including in vivo studies.

Scientific Applications:

• Developmental Biology: Infers lineage relationships and differentiation dynamics during development from scRNA-seq data.
• Regenerative Medicine: Analyzes lineage dynamics and cell fate decisions relevant to tissue regeneration.
• Oncology and Cancer Research: Identifies lineage-associated gene expression changes and early markers of invasiveness, exemplified in human breast cancer biopsies.
• In Vivo Lineage Studies: Supports study of small cell lineages in in vivo experimental contexts.

Methodology:

Leverages memory-based prediction algorithms to analyze scRNA-seq datasets, identifying lineage-specific gene expression patterns without prior lineage information and enabling assignment of transcriptomes to lineage trees and reconstruction of cellular structures from pooled datasets.