The “Gene Cube”: A Novel Approach to Three-dimensional Clustering of Gene Expression Data

Abstract

Background: A very popular technique for isolating significant genes from cancerous tissues is the application of various clustering algorithms on data obtained by DNA microarray experiments. Aim: The objective of the present work is to take into consideration the chromosomal identity of every gene before the clustering, by creating a three-dimensional structure of the form Chromosomes×Genes×Samples. Further on, the k-Means algorithm and a triclustering technique called δ- TRIMAX, are applied independently on the structure. Materials and Methods: The present algorithm was developed using the Python programming language (v. 3.5.1). For this work, we used two distinct public datasets containing healthy control samples and tissue samples from bladder cancer patients. Background correction was performed by subtracting the median global background from the median local Background from the signal intensity. The quantile normalization method has been applied for sample normalization. Three known algorithms have been applied for testing the “gene cube”, a classical k-means, a transformed 3D k-means and the δ-TRIMAX. Results: Our proposed data structure consists of a 3D matrix of the form Chromosomes×Genes×Samples. Clustering analysis of that structure manifested very good results as we were able to identify gene expression patterns among samples, genes and chromosomes. Discussion: to the best of our knowledge, this is the first time that such a structure is reported and it consists of a useful tool towards gene classification from high-throughput gene expression experiments. Conclusion: Such approaches could prove useful towards the understanding of disease mechanics and tumors in particular.

PASS: Protein Annotation Surveillance Site for Protein Annotation Using Homologous Clusters, NLP, and Sequence Similarity Networks

“…One novel approach by ( Abnousi et al, 2018 ) uses an alignment-free technique to cluster protein sequences. Another novel approach uses a 3-dimensional method to cluster gene expression data ( Lambrou et al, 2019 ). For our work, we used the pClust method, in part because it worked well for our iterative approach.…”

Prediction of lncRNA–Protein Interactions via the Multiple Information Integration

“…For example, Link prediction paradigms have been used to predict drug targets (Munir et al, 2019;Srivastava et al, 2019;Zeng et al, 2019Zeng et al, , 2020Ru et al, 2020;Wang et al, 2020), enhancer promoter interactions (Hong et al, 2019;Cai et al, 2020a), disease genes (Zeng et al, 2017a;Ji et al, 2019;Kuang et al, 2019;Wang et al, 2019;Peng et al, 2020), link prediction (Xiao et al, 2018(Xiao et al, , 2019(Xiao et al, , 2020, circular RNAs (Zeng et al, 2017b;Xiao et al, 2019), microRNAs (miRNAs) (Xiao et al, 2018(Xiao et al, , 2020Zeng et al, 2018;Hajieghrari et al, 2019;Jeyaram et al, 2019;, and peptide recognition (Bai et al, 2019;Cai et al, 2020b;Fu et al, 2020;Zhang and Zou, 2020). In addition, computational intelligence such as evolutionary algorithms (Song et al, 2020a,b) and unsupervised learning (Lambrou et al, 2019;Noureen et al, 2019;Zhang L. et al, 2019;Zou et al, 2020) can be applied to the field of bioinformatics. Given the efficient performance of machine learning methods in predicting lncRNA-protein interactions, the number of researchers considering machine learning methods as the first choice for predicting lncRNAprotein interactions have been increasing.…”

WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy

“…Gene expression data contains gene activity information, and it reflects the current physiological state of the cell, for example, whether the drug is effective on the cell, etc. It plays important roles in clinical diagnosis and drug efficacy judgment, such as assisting diagnosis and revealing disease occurrence mechanism (Lambrou et al, 2019). Gene expression data is rather complex, large in volume and grows fast.…”