Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

<p>Abstract</p> <p>Background</p> <p>We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes.</p> <p>Results</p> <p>We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes.</p> <p>Conclusion</p> <p>We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.</p

Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack

It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Here, we extend the model introduced by Menezes et al (2009) to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations, yields a test to help separate true associations from noise and can include confounders. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package

Analysing multiple types of molecular profiles simultaneously: Connecting the needles in the haystack

Background: It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Methods and results: Here, we extend the model introduced by Menezes et al. 2009 to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations and yields a test to help separate true associations from noise. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Conclusions: Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package

DEVELOPMENT OF BIOINFORMATICS TOOLS AND ALGORITHMS FOR IDENTIFYING PATHWAY REGULATORS, INFERRING GENE REGULATORY RELATIONSHIPS AND VISUALIZING GENE EXPRESSION DATA

In the era of genetics and genomics, the advent of big data is transforming the field of biology into a data-intensive discipline. Novel computational algorithms and software tools are in demand to address the data analysis challenges in this growing field. This dissertation comprises the development of a novel algorithm, web-based data analysis tools, and a data visualization platform. Triple Gene Mutual Interaction (TGMI) algorithm, presented in Chapter 2 is an innovative approach to identify key regulatory transcription factors (TFs) that govern a particular biological pathway or a process through interaction among three genes in a triple gene block, which consists of a pair of pathway genes and a TF. The identification of key TFs controlling a biological pathway or a process allows biologists to understand the complex regulatory mechanisms in living organisms. TF-Miner, presented in Chapter 3, is a high-throughput gene expression data analysis web application that was developed by integrating two highly efficient algorithms; TF-cluster and TF-Finder. TF-Cluster can be used to obtain collaborative TFs that coordinately control a biological pathway or a process using genome-wide expression data. On the other hand, TF-Finder can identify regulatory TFs involved in or associated with a specific biological pathway or a process using Adaptive Sparse Canonical Correlation Analysis (ASCCA). Chapter 4 presents ExactSearch; a suffix tree based motif search algorithm, implemented in a web-based tool. This tool can identify the locations of a set of motif sequences in a set of target promoter sequences. ExactSearch also provides the functionality to search for a set of motif sequences in flanking regions from 50 plant genomes, which we have incorporated into the web tool. Chapter 5 presents STTM JBrowse; a web-based RNA-Seq data visualization system built using the JBrowse open source platform. STTM JBrowse is a unified repository to share/produce visualizations created from large RNA-Seq datasets generated from a variety of model and crop plants in which miRNAs were destroyed using Short Tandem Target Mimic (STTM) Technology

Multimodal data analysis applied to a medical setting

Complex diseases, such as cancer, have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. It is also important to capture the interaction between different types of cells, an important indicator of disease status. To that end, it is crucial to quantify and utilize the spatial distribution of various cell types within the examined tissue at different scales. We employ Ripley's K-statistic, a traditional feature employed in geographical information systems, which captures spatial distribution patterns of individual point sets and interactions between multiple point sets. We propose to improve the histopathology image features by incorporating this descriptor to capture the spatial distribution of the cells, and interactions between lymphocytes and epithelial cells. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of 0.736 (p approx 1e-14) and 0.471, (p approx 7e-3) for CCA and Sparse CCA, respectively, of several image features with expression of PAM50 genes, known to be linked to outcome. Sparse CCA, an extension of CCA based on sparsity, revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. The utility of the Ripley's K-statistic on 710 TCGA breast invasive carcinoma (BRCA) patients' histopathology images in the context of imaging-genetics is demonstrated by its superior correlations with gene expressions. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer, and the importance of capturing spatial features at multiple scales

Comprehensive Analysis of Gene-Environmental Interactions with Temporal Gene Expression Profiles in Pseudomonas aeruginosa

To explore gene-environment interactions, based on temporal gene expression information, we analyzed gene and treatment information intensively and inferred interaction networks accordingly. The main idea is that gene expression reflects the response of genes to environmental factors, assuming that variations of gene expression occur under different conditions. Then we classified experimental conditions into several subgroups based on the similarity of temporal gene expression profiles. This procedure is useful because it allows us to combine diverse gene expression data as they become available, and, especially, allowing us to lay the regulatory relationships on a concrete biological basis. By estimating the activation points, we can visualize the gene behavior, and obtain a consensus gene activation order, and hence describe conditional regulatory relationships. The estimation of activation points and building of synthetic genetic networks may result in important new insights in the ongoing endeavor to understand the complex network of gene regulation