Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma

Abstract

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.

Systematic Reconstruction of Molecular Cascades Regulating GP Development Using Single-Cell RNA-Seq

“…To broadly characterize potential heterogeneity, we measured the similarity of the 217 single-cell samples across the expression profiles of more than 9,000 sequenced genes. Consensus clustering (CC) (Swift et al, 2004;Wahyudi et al, 2011) was used to account for the correlation between the broadly similar cells throughout development of the GP. At least three cell populations were revealed after CC, among which most of the cells belonged to two large groups, and 13 cells belonged to a smaller, less similar group (Figures 1B, 1B 0 , and S3).…”