# Classifying samples based on marker gene expression

I have a few sets of marker genes that I can classify RNA-seq samples using semi-supervised clustering. I would like to automate the process, however, I am struggling to find the ideal algorithm that could generate some kind of score for marker gene set from a given sample.

I presume that this is a standard analyses in many groups but I am not sure which method(s) are yielding good results in practice.

• There was recently a similar question on Biostars that yielded no answers: biostars.org/p/239228 – burger May 24 '17 at 20:28
• I am surprised. It seems like such an important problem. Especially with scRNA-seq gaining popularity. – GWW May 24 '17 at 20:45
• Since you mentioned scRNA-seq data, you might be interested in Buettner & al.: “Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells”. It doesn’t quite address your problem but it shows some of the issues associated with identifying cell populations in scRNA-seq in particular, which are largely smoothed out in bulk RNA-seq. – Konrad Rudolph May 26 '17 at 10:43

• Tacking on to @Peter Humberg's caveat of GeneRave being designed for microarray data, you could voom transform your counts using limma to make them microarray-like. – A_Skelton73 May 25 '17 at 9:58