Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

The Use of Biomass of Apricot Kernels as a Material for the Extraction of Methylene Blue from Aqueous Media

The possibility of using a sorption material based on apricot kernels for the extraction of methylene blue (MG) dye from aqueous media is considered. The maximum sorption capacity of the material was established – 0.58 mmol/g (185.6 mg/g). The obtained isotherm was processed within the framework of monomolecular and polymolecular adsorption models. The equations that most accurately describe the isotherm are determined, and the correlation coefficients are calculated. It has been shown that the sorption isotherm of the MG dye by the crushed apricot kernel (CCA) is best described by the Langmuir (R2 = 0.9724) and Dubinin-Radushkevich (R2 = 0.9818) models. However, when comparing the plots of the function A = f(Ce) of the sorption processes of the dye by the CCA sorbent according to the models under study with the experimental dependence, it was found that the sorption process is most accurately described by the sorption models of Dubinin-Radushkevich and Temkin. Using the equations of the Langmuir and Dubinin-Radushkevich models, the thermodynamic parameters of the process were calculated: the sorption energy (E = 8.066 kJ/ mol) and the Gibbs energy (∆Go = -8.597 kJ/mol).

A Vision on Extensions of Canonical Correlation Analysis for Data Interpretation Applications in Compatible Semantic Communication

: A comprehensive approach to canonical correlation analysis (CCA) techniques that explicitly enhance data interpretation by encountering semantic barriers in communication is proposed. For a consolidated and technology dependent network infrastructure, the concept of inclusive CCA (such as linear CCA, sparse CCA and kernel CCA) asserts the inclusion of statistical correlational analysis in semantic communication. To the extent that there exist potential inconsistencies due to redundancy and misinterpretation of data attributes, compatibility with respect to data interpretation may defer. Hence, CCA as a statistical technique incorporates both symmetric as well as asymmetric multivariate data analysis to help delineate the incompatibility caused due to subtle semantic defects. A singular value decomposition (SVD) based latent semantic indexing (LSI) method is substantiated upon a linear dataset and simulation results are canonically analyzed for the same. Favorably, the p-value analysis from the t-test validates the significance of the application of extensions of CCA in the field of semantic communication.

Robust kernel canonical correlation analysis to detect gene-gene co-associations: A case study in genetics

The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene–gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a robust kernel (cross)-covariance operator potentially enables the use of a robust kernel CCA, which is studied in this paper. We first propose an influence function-based estimator for the variance of the KCCU. We then present a non-parametric robust KCCU, which is designed for dealing with contaminated data. The robust KCCU is less sensitive to noise than KCCU. We investigate the proposed method using both synthesized and real data from the Mind Clinical Imaging Consortium (MCIC). We show through simulation studies that the power of the proposed methods is a monotonically increasing function of sample size, and the robust test statistics bring incremental gains in power. To demonstrate the advantage of the robust kernel CCA, we study MCIC data among 22,442 candidate Schizophrenia genes for gene–gene co-associations. We select 768 genes with strong evidence for shedding light on gene–gene interaction networks for Schizophrenia. By performing gene ontology enrichment analysis, pathway analysis, gene–gene network and other studies, the proposed robust methods can find undiscovered genes in addition to significant gene pairs, and demonstrate superior performance over several of current approaches.

A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application

In this paper, an orthogonal regularized kernel canonical correlation analysis algorithm (ORKCCA) is proposed. ORCCA algorithm can deal with the linear relationships between two groups of random variables. But if the linear relationships between two groups of random variables do not exist, the performance of ORCCA algorithm will not work well. Linear orthogonal regularized CCA algorithm is extended to nonlinear space by introducing the kernel method into CCA. Simulation experimental results on both artificial and handwritten numerals databases show that the proposed method outperforms ORCCA for the nonlinear problems.