FEM

FEM transforms single-cell RNA sequencing (scRNA-SEQ) Gene Expression Matrices (GEM) into Functional Expression Matrices (FEM) to preserve all expressed genes and enable cell-level functional interpretation.

Key Features:

• Functional Expression Matrix (FEM) algorithm: Converts the traditional Gene Expression Matrix (GEM) into a Functional Expression Matrix (FEM) that incorporates data from all expressed genes within each cell.
• Integration with GEM and gene set enrichment (GSE): Produces FEM outputs compatible with GEM-based downstream analyses such as gene set enrichment (GSE).
• Cell clustering and cell-type specific function annotation: Enables cell clustering and annotation of cell-type specific functions and has been applied to peripheral blood mononuclear cells, human liver, and human pancreas datasets.
• Comprehensive data utilization: Retains information from all expressed genes in scRNA-SEQ datasets to maximize the information extracted per cell.

Scientific Applications:

• Biological significance discovery: Facilitates identification of biological functions of genes that are differentially expressed at the single-cell level.
• Enhanced data utilization: Enables more complete use of scRNA-SEQ expression data to provide deeper insights into cellular functions and interactions.

Methodology:

Conversion of GEM to a Functional Expression Matrix (FEM) and provision of FEM outputs compatible with gene set enrichment (GSE) analyses, with the transformed matrices used for cell clustering and cell-type specific function annotation.