Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Motivation: Marker genes, defined as genes that are expressed primarily in a single-cell type, can be identified from the single-cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results: To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list.

Automated summary

Marker genes, primarily expressed in a single-cell type, are highly correlated in bulk data, allowing for their identification through correlation patterns. A new algorithm has been developed to detect these marker genes by combining published information and bulk transcriptome data, refining the list of known marker genes based on the correlation structure of the bulk data.