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.
Highlights
Modern high-throughput methods produce large volumes of -omics data
The data set of 559 myeloma patients (GSE2658) is composed of a patient enrolled in two different therapies, total therapy 2 (TT2) and total therapy 3 (TT3)
There is no difference in the expression profile between TT2 and TT3 group at starting point but the differences are expected once the treatment has been completed
Summary
Modern high-throughput methods produce large volumes of -omics data. Efficient processing of such data, in conjunction with other available biomedical datasets, is one of the main challenges facing modern biostatisticians and bioinformatics experts [1]. Classical statistical methods are adapted to process large volumes of data, or data may be preprocessed–with the use of biological knowledge–as a preliminary step in statistical processing pipelines [2] [3] [4].
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