voomSOM: voom-based Self-Organizing Maps for Clustering RNA-seq Data

What is it about?

The article presents a new clustering approach, voomSOM, that combines voom and self-organizing maps with k-means, k-medoid, and hierarchical clustering algorithms to cluster RNA-seq data. The voom method transforms the RNA-seq count data into a log-cpm matrix, and the SOM algorithm generates a codebook used in downstream analysis. The approach is evaluated on simulated and real datasets and performs similarly or better than other methods.

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Photo by Sangharsh Lohakare on Unsplash

Photo by Sangharsh Lohakare on Unsplash

Why is it important?

The integration of voom and SOM enhances the clustering algorithms' performance in overdispersed RNA-seq data. The proposed voomSOM approach is an efficient and novel clustering method for RNA-seq data.