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DESMOND 2.0: Identification of differentially expressed biclusters for unsupervised patient stratification
Abstract
Unsupervised patient stratification based on omics data is traditionally approached by clustering methods which may be inefficient for datasets with multiple patterns overlapping in rows and columns. Biclustering methods that are searching for submatrices with a specific pattern in a two-dimensional sample-gene matrix represent a promising alternative to conventional clustering [1]. However, in practice, the existing biclustering methods show a limited ability to robustly recover known PAM50 breast cancer subtypes [2]. This motivated us to develop DESMOND, a novel method for the identification of differentially expressed biclusters that uses interaction networks as constraints to improve the robustness of the biclustering results [3]. We applied DESMOND to two independent breast cancer cohorts (TCGA-BRCA [4] and METABRIC [5]) and confirmed that it indeed identified more robust biclusters than other methods. However, found biclusters were small in terms of genes, possibly due to incompleteness of the input network.
Here we present DESMOND 2.0 (https://github.com/ozolotareva/DESMOND2), an updated version of DESMOND which makes two significant advancements. First, it does not require the network and clusters individual genes instead of gene pairs. Second, it provides the choice of two binarization and three clustering methods for the identification of gene and sample sets. These modifications greatly improve the tool’s runtime and help to find larger biclusters in terms of genes and to recover known breast cancer subtypes more precisely than its baselines. Moreover, besides PAM50 subtypes, DESMOND identifies a rare neuroendocrine subtype, which prevalence is around 5% in both cohorts [6].
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References
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