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

Data integration through canonical correlation analysis and its application to OMICs research

The subject of the paper is a review of multidimensional data analysis methods, which is the canonical analysis with its various variants and its use in omics data research. The dynamic development of high-throughput methods, and with them the availability of large and constantly growing data resources, forces the development of new analytical approaches that allow the review of the analyzed processes, taking into account data from various levels of the organization of living organisms. The multidimensional perspective allows for the assessment of the analyzed phenomenon in a more realistic way, as it generally takes into account much more data (including OMICs data). Without omitting the complexity of an organism, the method simplifies the multidimensional view, finally giving the result so that the researcher can draw practical conclusions. This is particularly important in medical sciences, where the study of pathological processes is usually aimed at developing treatment regimens. One of the primary methods for studying biomedical processes in a multidimensional approach is the canonical correlation analysis (CCA) with various variants. The use of CCA unique methodologies for simultaneous analysis of multiset biomolecular data opens up new avenues for studying previously undiscovered processes and interdependencies such as e.g. in the tumor microenvironment (TME) connected to intercellular communication. Because of the huge and still untapped potential of canonical correlation, in this review available implementations of CCA techniques are presented. In particular, the possibility of using the technique of canonical correlation analysis for OMICs data is emphasized.

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

A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.

Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression

Methods based around statistics and linear algebra have been increasingly used in attempts to address emerging questions in microarray literature. Microarray technology is a long-used tool in the global analysis of gene expression, allowing for the simultaneous investigation of hundreds or thousands of genes in a sample. It is characterized by a low sample size and a large feature number created a non-square matrix, and by the incomplete rank, that can generate countless more solution in classifiers. To avoid the problem of the 'curse of dimensionality' many authors have performed feature selection or reduced the size of data matrix. In this work, we introduce a new logistic regression-based model to classify breast cancer tumor samples based on microarray expression data, including all features of gene expression and without reducing the microarray data matrix. If the user still deems it necessary to perform feature reduction, it can be done after the application of the methodology, still maintaining a good classification. This methodology allowed the correct classification of breast cancer sample data sets from Gene Expression Omnibus (GEO) data series GSE65194, GSE20711, and GSE25055, which contain the microarray data of said breast cancer samples. Classification had a minimum performance of 80% (sensitivity and specificity), and explored all possible data combinations, including breast cancer subtypes. This methodology highlighted genes not yet studied in breast cancer, some of which have been observed in Gene Regulatory Networks (GRNs). In this work we examine the patterns and features of a GRN composed of transcription factors (TFs) in MCF-7 breast cancer cell lines, providing valuable information regarding breast cancer. In particular, some genes whose αi ∗ associated parameter values revealed extreme positive and negative values, and, as such, can be identified as breast cancer prediction genes. We indicate that the PKN2, MKL1, MED23, CUL5 and GLI genes demonstrate a tumor suppressor profile, and that the MTR, ITGA2B, TELO2, MRPL9, MTTL1, WIPI1, KLHL20, PI4KB, FOLR1 and SHC1 genes demonstrate an oncogenic profile. We propose that these may serve as potential breast cancer prediction genes, and should be prioritized for further clinical studies on breast cancer. This new model allows for the assignment of values to the αi ∗ parameters associated with gene expression. It was noted that some αi ∗ parameters are associated with genes previously described as breast cancer biomarkers, as well as other genes not yet studied in relation to this disease.