KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis

TL;DR

KiWi is a scalable subspace clustering algorithm for gene expression data that efficiently discovers biologically relevant gene groups, including small twig clusters, and handles large datasets with improved computational performance.

Contribution

KiWi introduces a scalable OPSM-based subspace clustering method capable of identifying small, tightly co-regulated gene groups in large gene expression datasets.

Findings

Correctly groups redundant probes and experiments with clinical annotations

Differentiates real promoter sequences from controls

Shows strong association with cis-regulatory motifs

Abstract

Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.