FEM: mining biological meaning from cell level in single-cell RNA sequencing data.BACKGROUND
 We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes.  The algorithm converts the gene expression matrix (GEM) into a FEM.  The FEM algorithm can provide insight on the biological significance of a single cell.  We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).
FEM
Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used.  However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded.



GSE
However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded.



scRNA-SEQ