Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

Abstract

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

Integration strategies of multi-omics data for machine learning analysis

“…In that aspect, benchmark studies are also particularly useful and should be done more frequently. With the notable exception of Herrmann et al (2020) [168] which focused on survival prediction methods for multi-omics data, most benchmarks focus on clustering and dimensionality reduction methods [14] , [26] , [27] , [135] , [169] , [170] , [171] . Thorough comparisons of other ML models have not been made for multi-omics datasets, and we have yet to know if the deep learning prowess made in other fields of pattern recognition can be reproduced in bioinformatics [172] .…”

A survey on data integration for multi-omics sample clustering

“…Low-Rank Approximation based multi-omics data clustering LRAcluster [81,82] is a probabilistic approach for dimensionality reduction. The methodology was originally developed to integrate four high-dimensional omic data for the identification of different cancer subtypes.…”

Multiplexed Gene Engineering Based on dCas9 and gRNA-tRNA Array Encoded on Single Transcript

“…Eukaryotic gene expression is a complex process controlled at multiple levels, including epigenetic, transcriptional and posttranscriptional regulation. High-throughput technologies have been used with multidimensional genomic datasets to study the crosslayer regulatory interplay [1,2]. Confirmation of the multidimensional network by using molecular biology methods is important to understand and elucidate the pathogenesis of cancer and other diseases [3][4][5].…”