Sometimes you don’t know what you’re looking for until you find it; and this is especially true when it comes to the huge datasets that can be generated using modern sequencing techniques. Now, researchers from Japan report the development of a statistical framework that can perform unbiased extraction of biologically relevant cell-cell communications from a sea of spatial gene expression data.
October 3, 2022
Sometimes you don’t know what you’re looking for until you find it; and this is especially true when it comes to the huge datasets that can be generated using modern sequencing techniques. Now, researchers from Japan report the development of a statistical framework that can perform unbiased extraction of biologically relevant cell-cell communications from a sea of spatial gene expression data.
In a study published in September in Bioinformatics, researchers from the University of Tsukuba have revealed that a new statistical analysis method can accurately identify cell-cell communication affecting gene expression at the single-cell level.
Communication between cells regulates gene expression in ways that are crucial for normal function as well as disease development. While single-cell RNA sequencing and spatially resolved transcriptomics can provide some insight into this communication, current methods for analyzing these types of data have some important limitations.
