TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.

Abstract

Biclustering has emerged as a promising approach for analyzing high-dimensional expression data, offering unique advantages in uncovering localized co-expression patterns that traditional clustering methods often miss and thus facilitating advancements in complex disease research and other biomedical applications. However, state-of-the-art methods identify distinct patterns at the expense of losing information about specific patterns, some of which have been used to define cancer subtypes or reflect the progression of a disease or cellular processes. Additionally, these methods exhibit poor effectiveness in noisy environments. To address these limitations, we propose the bucket trend-preserving (BTP) pattern, a novel generalization of existing patterns. And we have developed an algorithm, TransBic, to extract significant biclusters of BTP-patterns. Specifically, TransBic transforms the problem into identifying common multipartite acyclic tournament subdigraphs shared by distinct subsets of acyclic tournament digraphs derived from a given expression matrix. Compared with prominent tools, TransBic demonstrates superior performance in identifying biclusters of all non-row-constant patterns, especially under noise and data fluctuations. Furthermore, TransBic successfully identifies the most disease-related pathways for type 2 diabetes (T2D), colorectal cancer, hepatocellular carcinoma, and breast cancer, outperforming other tools in this regard. Different from previous generalizations, BTP-patterns capture specific up-regulation and down-regulation dynamics. Through targeted analysis of BTP-patterns in T2D expression data, TransBic uncovers biological processes affected by disease risk factors, extending the application of trend-preserving biclustering in expression data analysis.