Domain-enhanced analysis of microarray data using GO annotations

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

BACKGROUND

Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding.

RESULTS

We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions.

CONCLUSIONS

The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO .

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA provided new insights compared to standard approaches thanks to an increased sensitivity compared to other approaches. Those results highlight the benefits of the TcGSA method for analyzing gene expression dynamics.

GWAS-based pathway analysis differentiates between fluid and crystallized intelligence

Cognitive abilities vary among people. About 40-50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) - the ability to reason in novel situations - and general crystallized intelligence (gC) - the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome-wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene- and pathway-based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long-term depression (LTD) seemed to underlie gC. Thus, this study supports the gF-gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation.

Multivariate inference of pathway activity in host immunity and response to therapeutics

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method.

Comparison and evaluation of pathway-level aggregation methods of gene expression data

Background

Microarray experiments produce expression measurements in genomic scale. A way to derive functional understanding of the data is to focus on functional sets of genes, such as pathways, instead of individual genes. While a common practice for the pathway-level analysis has been functional enrichment analysis such as over-representation analysis and gene set enrichment analysis, an alternative approach has also been explored. In this approach, gene expression data are first aggregated at pathway level to transform the original data into a compact representation in which each row corresponds to a pathway instead of a gene. Thereafter the pathway expression data can be used for differential expression and classification analyses in pathway space, leveraging existing algorithms usually applied to gene expression data. While several studies have proposed the pathway-level aggregation methods, it remains unclear how they compare with one another, since the evaluations were done to a limited extent. Thus this study presents a comprehensive evaluation of six most prominent aggregation methods.

Results

The compared methods include five existing methods--mean of all member genes (Mean all), mean of condition-responsive genes (Mean CORGs), analysis of sample set enrichment scores (ASSESS), principal component analysis (PCA), and partial least squares (PLS)--and a variant of an existing method (Mean top 50%, averaging top half of member genes). Comprehensive and stringent benchmarking was performed by collecting seven pairs of related but independent datasets encompassing various phenotypes. Aggregation was done in the space of KEGG pathways. Performance of the methods was assessed by classification accuracy validated both internally and externally, and by examining the correlative extent of pathway signatures between the dataset pairs. The assessment revealed that (i) the best accuracy and correlation were obtained from ASSESS and Mean top 50%, (ii) Mean all showed the lowest accuracy, and (iii) Mean CORGs and PLS gave rise to the largest extent of discordance in the pathway signature correlation.

Conclusions

The two best performing method (ASSESS and Mean top 50%) are suggested to be preferred. The benchmarking analysis also suggests that there is both room and necessity for developing a novel method for pathway-level aggregation.

Comparative evaluation of set-level techniques in predictive classification of gene expression samples

Background

Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set-level classifiers have yielded rather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set-level framework within a large collection of machine learning experiments.

Results

Genuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene-set methods GSEA and SAM-GS as well as two generic feature selection methods. To aggregate expressions of genes into a feature value, the singular value decomposition (SVD) method as well as the SetSig technique improve on simple arithmetic averaging. Set-level classifiers learned with 10 features constituted by the Global test slightly outperform baseline gene-level classifiers learned with all original data features although they are slightly less accurate than gene-level classifiers learned with a prior feature-selection step.

Conclusion

Set-level classifiers do not boost predictive accuracy, however, they do achieve competitive accuracy if learned with the right combination of ingredients.

Availability

Open-source, publicly available software was used for classifier learning and testing. The gene expression datasets and the gene set database used are also publicly available. The full tabulation of experimental results is available at http://ida.felk.cvut.cz/CESLT.

Functional genomics and networks: new approaches in the extraction of complex gene modules

The engine that makes the cell work is made of an intricate network of molecular interactions. Nowadays, the elements and relationships of this complex network can be studied with several types of high-throughput techniques. The dream of having a global picture of the cell from different perspectives that can jointly explain cell behavior is, at least technically, feasible. However, this task can only be accomplished by filling the gap between data and information. The availability of methods capable of accurately managing, integrating and analyzing the results from these experiments is crucial for this purpose. Here, we review the new challenges raised by the availability of different genomic data, as well as the new proposals presented to cope with the increasing data complexity. Special emphasis is given to approaches that explore the transcriptome trying to describe the modules of genes that account for the traits studied.

A unified mixed effects model for gene set analysis of time course microarray experiments

Methods for gene set analysis test for coordinated changes of a group of genes involved in the same biological process or molecular pathway. Higher statistical power is gained for gene set analysis by combining weak signals from a number of individual genes in each group. Although many gene set analysis methods have been proposed for microarray experiments with two groups, few can be applied to time course experiments. We propose a unified statistical model for analyzing time course experiments at the gene set level using random coefficient models, which fall into the more general class of mixed effects models. These models include a systematic component that models the mean trajectory for the group of genes, and a random component (the random coefficients) that models how each gene's trajectory varies about the mean trajectory. We show that the proposed model (1) outperforms currently available methods at discriminating gene sets differentially changed over time from null gene sets; (2) provides more stable results that are less affected by sampling variations; (3) models dependency among genes adequately and preserves type I error rate; and (4) allows for gene ranking based on predicted values of the random effects. We describe simulation studies using gene expression data with "real life" correlations and we demonstrate the proposed random coefficient model using a mouse colon development time course dataset. The agreement between results of the proposed random coefficient model and the previous reports for this proof-of-concept trial further validates this methodology, which provides a unified statistical model for systems analysis of microarray experiments with complex experimental designs when re-sampling based methods are difficult to apply.

A flexible two-stage procedure for identifying gene sets that are differentially expressed

MOTIVATION

Microarray data analysis has expanded from testing individual genes for differential expression to testing gene sets for differential expression. The tests at the gene set level may focus on multivariate expression changes or on the differential expression of at least one gene in the gene set. These tests may be powerful at detecting subtle changes in expression, but findings at the gene set level need to be examined further to understand whether they are informative and if so how.

RESULTS

We propose to first test for differential expression at the gene set level but then proceed to test for differential expression of individual genes within discovered gene sets. We introduce the overall false discovery rate (OFDR) as an appropriate error rate to control when testing multiple gene sets and genes. We illustrate the advantage of this procedure over procedures that only test gene sets or individual genes.

Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies

Disease gene identification is still a challenge despite modern high-throughput methods. Many diseases are very rare or lethal and thus cannot be investigated with traditional methods. Several in silico methods have been developed but they have some limitations. We introduce a new method that combines information about protein-interaction network properties and Gene Ontology terms. Genes with high-calculated network scores and statistically significant gene ontology terms based on known diseases are prioritized as candidate genes. The method was applied to identify novel primary immunodeficiency-related genes, 26 of which were found. The investigation uses the protein-interaction network for all essential immunome human genes available in the Immunome Knowledge Base and an analysis of their enriched gene ontology annotations. The identified disease gene candidates are mainly involved in cellular signaling including receptors, protein kinases and adaptor and binding proteins as well as enzymes. The method can be generalized for any disease group with sufficient information.

Formulating and testing hypotheses in functional genomics

OBJECTIVE

The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment.

METHODS AND RESULTS

Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis

MOTIVATION

The field of microarray data analysis is shifting emphasis from methods for identifying differentially expressed genes to methods for identifying differentially expressed gene categories. The latter approaches utilize a priori information about genes to group genes into categories and enhance the interpretation of experiments aimed at identifying expression differences across treatments. While almost all of the existing approaches for identifying differentially expressed gene categories are practically useful, they suffer from a variety of drawbacks. Perhaps most notably, many popular tools are based exclusively on gene-specific statistics that cannot detect many types of multivariate expression change.

RESULTS

We have developed a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of a real data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and non-differentially expressed gene categories, and we utilize a resampling based strategy for controlling the false discovery rate when testing multiple categories.

AVAILABILITY

R code (www.r-project.org) for implementing our approach is available from the first author by request.