Biology subjects

Glass, K.

Publications and source records attributed to Glass, K..

8 recordsLinked to original sources

bioRxiv2018

New Statistical Methods for Constructing Robust Differential Correlation Networks

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fishers Z-transformation test is usually used. However, the Fishers Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fishers Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

bioinformatics↗

bioRxiv2018

Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism processes

Significant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

systems biology↗

bioRxiv2017

Understanding Tissue-specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

genomics↗

bioRxiv2016

Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

systems biology↗

bioRxiv2016

A network-based approach to eQTL interpretation and SNP functional characterization

Expression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only include cis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified both cis- and trans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.

genomics↗

bioRxiv2016

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.\n\nHighlightsO_LIOverview of assumptions used in preprocessing and normalization\nC_LIO_LIPipeline for preprocessing, quality control, and normalization of large heterogeneous data\nC_LIO_LIA Bioconductor package for the YARN pipeline and easy manipulation of count data\nC_LIO_LIPreprocessed GTEx data set using the YARN pipeline available as a resource\nC_LI

bioinformatics↗

bioRxiv2016

Transcriptional landscape of cell lines and their tissues of origin

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell lines transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models.\n\nHighlightsO_LICell lines differ from their source tissues in gene expression and regulation\nC_LIO_LIDistinct cell lines share altered patterns of cell cycle regulation\nC_LIO_LICell cycle genes are less strongly targeted by repressive TFs in cell lines\nC_LIO_LICell lines share expression with their source tissue, but at reduced levels\nC_LI

genomics↗

bioRxiv2016

Sexual dimorphism in gene expression and regulatory networks across human tissues

Sexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks.\n\nHighlightsO_LISexual dimorphism manifests in both gene expression and gene regulatory networks\nC_LIO_LISubstantial sexual dimorphism in regulatory networks was found in several tissues\nC_LIO_LIMany differentially regulated genes are not differentially expressed\nC_LIO_LISex hormone receptors do not exhibit sexually dimorphic expression in most tissues\nC_LI

genomics↗