Voomsom: Voom-Based Self-Organizing Maps For Clustering 
RNA-Sequencing Data



Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several cluster-ing approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed.



In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering al-gorithms such as k-means, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering.



We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm val-ues to get the codebook used in the downstream analysis. Next, we calculate the weighted dis-tance matrices using the sample quality weights obtained from voom transformation and code-books from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical cluster-ing algorithms to cluster samples.



The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similar-ly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in over-dispersed RNA-seq data.



The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.
