Regularization and grouping -omics data by GCA method: A transcriptomic case

Abstract

The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to maximize the tau-Kendall or rho-Spearman coefficients, which makes it possible to arrange rows and columns in such a way that the most similar ones remain in each other’s neighbourhood. In this way, the GCA algorithm highlights regularities in the data matrix. The ranked data can then be grouped using the GCCA method, and after that aggregated in clusters, providing a representation that is easier to analyze–especially in the case of large sets of gene expression profiles. Regularization of transcriptomic data, which is presented in this manuscript, has enabled division of the data set into column clusters (representing genes) and row clusters (representing patients). Subsequently, rows were aggregated (based on medians) to visualise the gene expression profiles for patients with Multiple Myeloma in each collection. The presented analysis became the starting point for characterisation of differentiated genes and biochemical processes in which they are involved. GCA analysis may provide an alternative analytical method to support differentiation and analysis of gene expression profiles characterising individual groups of patients.

OmicsON – Integration of omics data with molecular networks and statistical procedures

“…One of the most effective ways to achieve improved results is to combine basic statistical techniques (also called gold standard methods) with innovative approaches like regulated canonical analysis (rCCA) [29] or regularization and grouping data by GCA method [30] [31]. This ensures effective analysis of data in which the number of variables significantly exceeds the number of observations [29,32] [12].…”

Ciencia abierta como una nueva forma de hacer investigación

“…Si bien se ha demostrado que los artículos en acceso abierto reciben 18% más de citas (Piwowar et al, 2018), la práctica de ciencia abierta depende en gran medida de la creación de infraestructura de colaboración que permita la inclusión de herramientas digitales como plataformas para el trabajo conjunto, así como estándares de calidad y sistemas de evaluación similares. En cuanto a la documentación y registro abierto, los cuales son fundamentales para asegurar la reproducibilidad del conocimiento científico, no siempre se encuentran disponibles a causa de una falla u omisión disciplinar, pero también de políticas poco claras o de algún interés comercial para el uso de la data.…”

A Review on Initialization Methods for Nonnegative Matrix Factorization: Towards Omics Data Experiments

“…• CUR-based initialization: the factor matrix W is constructed as a submatrix (with a small number of actual columns) of the data matrix X. In this way, values are obtained that are more interpretable from a biological point of view (and usually to the same extent as the original data) [50,51]. CUR-based mechanisms differ in the "statistical" way of selecting columns (or rows, if we refer to the initialization of the factor H) from the matrix X.…”