Intends to identify distribution patterns in cell populations thanks to single-cell transcriptome study. FVFC uses gene coexpression network analysis (GCNA) to detect modules of genes with similar expression profiles and summarize them into eigengenes, which allows users to explore the distribution of cells interactively, interpret the gene features and generate new hypothesis. It also provides an interactive visualization using a clustering index parameter which helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). The method was tested thanks to two large single-cell studies.
College of Software, Nankai University, Tianjin, China; Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA; The CCC Biomedical Informatics Shared Resource, The Ohio State University, Columbus, OH, USA
FVFC funding source(s)
Supported by Human Frontier Science Program; the NCI ITCR U01CA188547; and the National Natural Science Foundation of China (61572265).