Pablo A Jaskowiak, Ivan G. Costa and Ricardo JGB Campello
RNA-Seq is becoming the standard technology for large-scale gene expression level measurements, as it offers a number of advantages over microarrays. Standards for RNA-Seq data analysis are, however, in its infancy when compared to those of microarrays. Clustering, which is essential for understanding gene expression data, has been widely investigated w.r.t microarrays. In what concerns the clustering of RNA-Seq data, however, a number of questions remain open, resulting in a lack of guidelines to practitioners. Here we evaluate computational steps relevant for clustering cancer samples via an empirical analysis of 15 RNA-seq datasets. Our evaluation considers strategies regarding expression estimates, number of genes after non-specific filtering and data transformations. We evaluate the performance of four clustering algorithms and twelve distance measures, which are commonly used for gene expression analysis. Results support that clustering cancer samples based on a gene quantification should be preferred. The use of non-specific filtering leading to a small number of features (1,000) presents, in general, superior results. Data should be log-transformed previously to cluster analysis. Regarding the choice of clustering algorithms, Average-Linkage and k-medoids provide, in general, superior recoveries. Although specific cases can benefit from a careful selection of a distance measure, Symmetric Rank-Magnitude correlation provides consistent and sound results in different scenarios.
Datasets are available upon request.
All results in tabular format (csv) can be found Tabular.
Additional plots can be found supplement.