3,377 research outputs found
Principal component gene set enrichment (PCGSE)
Motivation: Although principal component analysis (PCA) is widely used for
the dimensional reduction of biomedical data, interpretation of PCA results
remains daunting. Most existing methods attempt to explain each principal
component (PC) in terms of a small number of variables by generating
approximate PCs with few non-zero loadings. Although useful when just a few
variables dominate the population PCs, these methods are often inadequate for
characterizing the PCs of high-dimensional genomic data. For genomic data,
reproducible and biologically meaningful PC interpretation requires methods
based on the combined signal of functionally related sets of genes. While gene
set testing methods have been widely used in supervised settings to quantify
the association of groups of genes with clinical outcomes, these methods have
seen only limited application for testing the enrichment of gene sets relative
to sample PCs. Results: We describe a novel approach, principal component gene
set enrichment (PCGSE), for computing the statistical association between gene
sets and the PCs of genomic data. The PCGSE method performs a two-stage
competitive gene set test using the correlation between each gene and each PC
as the gene-level test statistic with flexible choice of both the gene set test
statistic and the method used to compute the null distribution of the gene set
statistic. Using simulated data with simulated gene sets and real gene
expression data with curated gene sets, we demonstrate that biologically
meaningful and computationally efficient results can be obtained from a simple
parametric version of the PCGSE method that performs a correlation-adjusted
two-sample t-test between the gene-level test statistics for gene set members
and genes not in the set. Availability:
http://cran.r-project.org/web/packages/PCGSE/index.html Contact:
[email protected] or [email protected]
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
Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments
BACKGROUND: Numerous nonparametric approaches have been proposed in literature to detect differential gene expression in the setting of two user-defined groups. However, there is a lack of nonparametric procedures to analyze microarray data with multiple factors attributing to the gene expression. Furthermore, incorporating interaction effects in the analysis of microarray data has long been of great interest to biological scientists, little of which has been investigated in the nonparametric framework. RESULTS: In this paper, we propose a set of nonparametric tests to detect treatment effects, clinical covariate effects, and interaction effects for multifactorial microarray data. When the distribution of expression data is skewed or heavy-tailed, the rank tests are substantially more powerful than the competing parametric F tests. On the other hand, in the case of light or medium-tailed distributions, the rank tests appear to be marginally less powerful than the parametric competitors. CONCLUSION: The proposed rank tests enable us to detect differential gene expression and establish interaction effects for microarray data with various non-normally distributed expression measurements across genome. In the presence of outliers, they are advantageous alternative approaches to the existing parametric F tests due to the robustness feature
Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review
A variety of genome-wide profiling techniques are available to probe
complementary aspects of genome structure and function. Integrative analysis of
heterogeneous data sources can reveal higher-level interactions that cannot be
detected based on individual observations. A standard integration task in
cancer studies is to identify altered genomic regions that induce changes in
the expression of the associated genes based on joint analysis of genome-wide
gene expression and copy number profiling measurements. In this review, we
provide a comparison among various modeling procedures for integrating
genome-wide profiling data of gene copy number and transcriptional alterations
and highlight common approaches to genomic data integration. A transparent
benchmarking procedure is introduced to quantitatively compare the cancer gene
prioritization performance of the alternative methods. The benchmarking
algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin
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Camera: a competitive gene set test accounting for inter-gene correlation
Competitive gene set tests are commonly used in molecular pathway analysis to test for enrichment of a particular gene annotation category amongst the differential expression results from a microarray experiment. Existing gene set tests that rely on gene permutation are shown here to be extremely sensitive to inter-gene correlation. Several data sets are analyzed to show that inter-gene correlation is non-ignorable even for experiments on homogeneous cell populations using genetically identical model organisms. A new gene set test procedure (CAMERA) is proposed based on the idea of estimating the inter-gene correlation from the data, and using it to adjust the gene set test statistic. An efficient procedure is developed for estimating the inter-gene correlation and characterizing its precision. CAMERA is shown to control the type I error rate correctly regardless of inter-gene correlations, yet retains excellent power for detecting genuine differential expression. Analysis of breast cancer data shows that CAMERA recovers known relationships between tumor subtypes in very convincing terms. CAMERA can be used to analyze specified sets or as a pathway analysis tool using a database of molecular signatures.Statistic
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods
Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation
Measuring reproducibility of high-throughput experiments
Reproducibility is essential to reliable scientific discovery in
high-throughput experiments. In this work we propose a unified approach to
measure the reproducibility of findings identified from replicate experiments
and identify putative discoveries using reproducibility. Unlike the usual
scalar measures of reproducibility, our approach creates a curve, which
quantitatively assesses when the findings are no longer consistent across
replicates. Our curve is fitted by a copula mixture model, from which we derive
a quantitative reproducibility score, which we call the "irreproducible
discovery rate" (IDR) analogous to the FDR. This score can be computed at each
set of paired replicate ranks and permits the principled setting of thresholds
both for assessing reproducibility and combining replicates. Since our approach
permits an arbitrary scale for each replicate, it provides useful descriptive
measures in a wide variety of situations to be explored. We study the
performance of the algorithm using simulations and give a heuristic analysis of
its theoretical properties. We demonstrate the effectiveness of our method in a
ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Stability and aggregation of ranked gene lists
Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector
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