Biology subjects

Coombes, K. R.

Publications and source records attributed to Coombes, K. R..

22 records · Page 2Linked to original sources

bioRxiv2020

Oncogenetic Network Estimation with Disjunctive Bayesian Networks: Learning from Unstratified Samples while Preserving Mutual Exclusivity Relations

MotivationCancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is challenging due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways. In this paper, we present the Disjunctive Bayesian Network (DBN), a novel oncogenetic model with a phylogenetic interpretation. DBN is expressive enough to capture cancer subtypes trajectories and mutually exclusive relations between alterations from unstratified data. ResultsIn cases where the number of studied alterations is small (< 30), we provide an efficient dynamic programming implementation of an exact structure learning method that finds a best DBN in the super-exponential search space of networks. In rare cases that the number of alterations is large, we provided an efficient genetic algorithm in our software package, OncoBN. Through numerous synthetic and real data experiments, we show OncoBNs ability in inferring ground truth networks and recovering biologically meaningful progression networks. AvailabilityOncoBN is implemented in R and is available at https://github.com/phillipnicol/OncoBN.

bioinformatics↗

bioRxiv2019

Identification and comparison of genes differentially regulated by transcription factors and miRNAs

Transcription factors and microRNAs (miRNA) both play a critical role in gene regulation and in the development of many diseases such as cancer. Understanding how transcription factors and miRNAs influence gene expression is thus important to understand, but complicated due to the large and interconnected nature of the human genome. To help better understand what genes are being regulated by transcription factors and/or miRNAs we looked at it over 8000 patient samples from 32 different cancer types collected from The Cancer Genome Atlas (TCGA). We started by clustering the transcription factors and miRNAs using Thresher to reduce the number of features. Using both the mRNA and miRNA sequencing data we constructed linear models to calculate the coefficient of determination (R2) for each mRNA based on the Thresher cluster expression. We generated three types of linear models: transcription factor, miRNA and transcription factor plus miRNA. We then determined genes that were highly explained or poorly explained by each of the three models based on the genes R2 value. We performed downstream gene enrichment analysis using ToppGene on the sets of well and poorly explained genes. This identified differences in gene regulation between transcription factors and miRNAs and showed what groups of gene are differentially regulated.

systems biology↗

bioRxiv2019

Mercator: An R Package for Visualization ofDistance Matrices

SummaryUnsupervised data analysis in many scientific disciplines is based on calculating distances between observations and finding ways to visualize those distances. These kinds of unsupervised analyses help researchers uncover patterns in large-scale data sets. However, researchers can select from a vast number of different distance metrics, each designed to highlight different aspects of different data types. There are also numerous visualization methods with their own strengths and weaknesses. To help researchers perform unsupervised analyses, we developed the Mercator R package. Mercator enables users to see important patterns in their data by generating multiple visualizations using different standard algorithms, making it particularly easy to compare and contrast the results arising from different metrics. By allowing users to select the distance metric that best fits their needs, Mercator helps researchers perform unsupervised analyses that use pattern identification through computation and visual inspection.\n\nAvailability and ImplementationMercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html)\n\nContactKevin.Coombes@osumc.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises two challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure performs best when considering both accuracy and speed. We applied the method to a proteomics data set from acute myeloid leukemia patients. Proteins in the apoptosis pathway could be explained using six PCs. By clustering the proteins in PC space, we were able to replace the PCs by six \"biological components\", three of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable.

bioinformatics↗