24,054 research outputs found
GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data
Background: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. Results: We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. Conclusions: GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.Department of Agriculture, Food and the MarineEuropean Commission - Seventh Framework Programme (FP7)Science Foundation IrelandUniversity College Dubli
Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling
While current food science research mainly focuses on microbial changes in food products that lead to foodborne illnesses, meat spoilage remains as an unsolved problem for the meat industry. This can result in important economic losses, food waste and loss of consumer confidence in the meat market. Gram-negative bacteria involved in meat spoilage are aerobes or facultative anaerobes. These represent the group with the greatest meat spoilage potential, where Pseudomonas tend to dominate the microbial consortium under refrigeration and aerobic conditions. Identifying stress response genes under different environmental conditions can help researchers gain an understanding of how Pseudomonas adapts to current packaging and storage conditions. We examined the gene expression profile of Pseudomonas putida KT2440, which plays an important role in the spoilage of meat products. Gene expression profiles were evaluated to select the most differentially expressed genes at different temperatures (30 °C and 10 °C) and decreasing glucose concentrations, in order to identify key genes actively involved with the spoilage process. A total of 739 and 1269 were found to be differentially expressed at 30 °C and 10 °C respectively; of which 430 and 568 genes were overexpressed, and 309 and 701 genes were repressed at 30 °C and 10 °C respectively
Gains in Power from Structured Two-Sample Tests of Means on Graphs
We consider multivariate two-sample tests of means, where the location shift
between the two populations is expected to be related to a known graph
structure. An important application of such tests is the detection of
differentially expressed genes between two patient populations, as shifts in
expression levels are expected to be coherent with the structure of graphs
reflecting gene properties such as biological process, molecular function,
regulation, or metabolism. For a fixed graph of interest, we demonstrate that
accounting for graph structure can yield more powerful tests under the
assumption of smooth distribution shift on the graph. We also investigate the
identification of non-homogeneous subgraphs of a given large graph, which poses
both computational and multiple testing problems. The relevance and benefits of
the proposed approach are illustrated on synthetic data and on breast cancer
gene expression data analyzed in context of KEGG pathways
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
Comparing large covariance matrices has important applications in modern
genomics, where scientists are often interested in understanding whether
relationships (e.g., dependencies or co-regulations) among a large number of
genes vary between different biological states. We propose a computationally
fast procedure for testing the equality of two large covariance matrices when
the dimensions of the covariance matrices are much larger than the sample
sizes. A distinguishing feature of the new procedure is that it imposes no
structural assumptions on the unknown covariance matrices. Hence the test is
robust with respect to various complex dependence structures that frequently
arise in genomics. We prove that the proposed procedure is asymptotically valid
under weak moment conditions. As an interesting application, we derive a new
gene clustering algorithm which shares the same nice property of avoiding
restrictive structural assumptions for high-dimensional genomics data. Using an
asthma gene expression dataset, we illustrate how the new test helps compare
the covariance matrices of the genes across different gene sets/pathways
between the disease group and the control group, and how the gene clustering
algorithm provides new insights on the way gene clustering patterns differ
between the two groups. The proposed methods have been implemented in an
R-package HDtest and is available on CRAN.Comment: The original title dated back to May 2015 is "Bootstrap Tests on High
Dimensional Covariance Matrices with Applications to Understanding Gene
Clustering
- …