Tutorial on Microarray Gene Expression Experiments

A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.

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 .

An Evaluation of Gene Set Analysis for Biomarker Discovery with Applications to Myeloma Research

In this paper, we evaluate 15 methods for gene set analysis in microarray classification problems. We employ four datasets from myeloma research and three types of biological gene sets, encompassing a total of 12 scenarios. Taking a two-step approach, we first identify important genes within gene sets to create summary gene set scores, we then construct predictive models using the gene set scores as predictors. We propose two powerful linear methods in addition to the well-known SuperPC method for calculating scores. By comparing the 15 gene set methods with methods used in individual-gene analysis, we conclude that, overall, the gene set analysis approach provided more accurate predictions than the individual-gene analysis.

A decision analysis model for KEGG pathway analysis

Background

The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways.

Results

A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient—a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis.

Conclusions

A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1285-1) contains supplementary material, which is available to authorized users.

Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis

The analysis of most high-throughput proteomics experiments involves the selection of differentially expressed proteins or peptides between two different sets of samples, e.g., from two experimental groups. As a result, a large list of selected features is reported, typically sorted by a measure for the expression fold change and a p-value from a statistical test. The biological interpretation of such a list is usually difficult since the features can typically be assigned to a large variety of biological classes. To facilitate the biological interpretation, set-based procedures focus on the analysis of feature subsets that all belong to the same biological class (e.g., same cellular component, biological process, molecular function, or pathway). Set-based procedures can roughly be divided into "enrichment methods" and "global test procedures," where the first involve all features of an experiment and the second only those features of a particular set. In this chapter we detail the working principle of these kind of statistical methods and describe how features can be classified into molecular subsets. We illustrate the use of the methods on a data example from a proteomics Parkinson study.

Gene set analysis methods: statistical models and methodological differences

Many methods of gene set analysis developed in recent years have been compared empirically in a number of comprehensive review articles. Although it is recognized that different methods tend to identify different gene sets as significant, no consensus has been worked out as to which method is preferable, as the recommendations are often contradictory. In this article, we want to group and compare different methods in terms of the methodological assumptions pertaining to definition of a sample and formulation of the actual null hypothesis. We discuss four models of statistical experiment explicitly or implicitly assumed by most if not all currently available methods of gene set analysis. We analyse validity of the models in the context of the actual biological experiment. Based on this, we recommend a group of methods that provide biologically interpretable results in statistically sound way. Finally, we demonstrate how correlated or low signal-to-noise data affects performance of different methods, observed in terms of the false-positive rate and power.

GSAASeqSP: a toolset for gene set association analysis of RNA-Seq data

RNA-Seq is quickly becoming the preferred method for comprehensively characterizing whole transcriptome activity, and the analysis of count data from RNA-Seq requires new computational tools. We developed GSAASeqSP, a novel toolset for genome-wide gene set association analysis of sequence count data. This toolset offers a variety of statistical procedures via combinations of multiple gene-level and gene set-level statistics, each having their own strengths under different sample and experimental conditions. These methods can be employed independently, or results generated from multiple or all methods can be integrated to determine more robust profiles of significantly altered biological pathways. Using simulations, we demonstrate the ability of these methods to identify association signals and to measure the strength of the association. We show that GSAASeqSP analyses of RNA-Seq data from diverse tissue samples provide meaningful insights into the biological mechanisms that differentiate these samples. GSAASeqSP is a powerful platform for investigating molecular underpinnings of complex traits and diseases arising from differential activity within the biological pathways. GSAASeqSP is available at http://gsaa.unc.edu.

timeClip: pathway analysis for time course data without replicates

Background

Time-course gene expression experiments are useful tools for exploring biological processes. In this type of experiments, gene expression changes are monitored along time. Unfortunately, replication of time series is still costly and usually long time course do not have replicates. Many approaches have been proposed to deal with this data structure, but none of them in the field of pathway analysis. Pathway analyses have acquired great relevance for helping the interpretation of gene expression data. Several methods have been proposed to this aim: from the classical enrichment to the more complex topological analysis that gains power from the topology of the pathway. None of them were devised to identify temporal variations in time course data.

Results

Here we present timeClip, a topology based pathway analysis specifically tailored to long time series without replicates. timeClip combines dimension reduction techniques and graph decomposition theory to explore and identify the portion of pathways that is most time-dependent. In the first step, timeClip selects the time-dependent pathways; in the second step, the most time dependent portions of these pathways are highlighted. We used timeClip on simulated data and on a benchmark dataset regarding mouse muscle regeneration model. Our approach shows good performance on different simulated settings. On the real dataset, we identify 76 time-dependent pathways, most of which known to be involved in the regeneration process. Focusing on the 'mTOR signaling pathway' we highlight the timing of key processes of the muscle regeneration: from the early pathway activation through growth factor signals to the late burst of protein production needed for the fiber regeneration.

Conclusions

timeClip represents a new improvement in the field of time-dependent pathway analysis. It allows to isolate and dissect pathways characterized by time-dependent components. Furthermore, using timeClip on a mouse muscle regeneration dataset we were able to characterize the process of muscle fiber regeneration with its correct timing.

Adaption of the global test idea to proteomics data with missing values

MOTIVATION

Global test procedures are frequently used in gene expression analysis to study the relationship between a functional subset of RNA transcripts and an experimental group factor. However, these procedures have been rarely used for the analysis of high-throughput data from other sources, such as proteome expression data. The main difficulties in transferring global test procedures from genomics to proteomics data are the more complicated way of obtaining functional annotations and the handling of missing values in some types of proteomics data.

RESULTS

We propose a simple mixed linear model in combination with a permutation procedure and missing values imputation to conduct global tests in proteomics experiments. This new approach is motivated by protein expression data obtained by means of 2-D gel electrophoresis within a mouse experiment of our current research. A simulation study yielded that power and testing level of the mixed model alone can be affected by missing values in the dataset. Imputation of missing values was able to correct for a bias in some simulation settings. Our new approach provides the possibility to rank Gene Ontology (GO) terms associated with protein sets. It is also helpful in the case in which a specific protein is represented by multiple spots on a 2-D gel by considering these spots also as a protein set. Analysis of our data points at correlations between the deficiency of the protein 'calreticulin' and protein sets related to biological processes in the heart muscle.

AVAILABILITY AND IMPLEMENTATION

Our proposed approach is included in the R-package 'RepeatedHighDim', which already contains a global test procedure for gene expression data. The package can be retrieved from http://cran.r-project.org/.

CONTACT

klaus.jung@ams.med.uni-goettingen.de.

Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

BACKGROUND

We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these "differentially expressed GO terms" and have named the algorithm "matrix-assisted identification method of differentially expressed GO terms" (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms.

FINDINGS

We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson's correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated.

CONCLUSIONS

MIMGO is a reliable method to identify differentially expressed GO terms comprehensively.

Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing

Burkitt lymphoma is a mature aggressive B-cell lymphoma derived from germinal center B cells. Its cytogenetic hallmark is the Burkitt translocation t(8;14)(q24;q32) and its variants, which juxtapose the MYC oncogene with one of the three immunoglobulin loci. Consequently, MYC is deregulated, resulting in massive perturbation of gene expression. Nevertheless, MYC deregulation alone seems not to be sufficient to drive Burkitt lymphomagenesis. By whole-genome, whole-exome and transcriptome sequencing of four prototypical Burkitt lymphomas with immunoglobulin gene (IG)-MYC translocation, we identified seven recurrently mutated genes. One of these genes, ID3, mapped to a region of focal homozygous loss in Burkitt lymphoma. In an extended cohort, 36 of 53 molecularly defined Burkitt lymphomas (68%) carried potentially damaging mutations of ID3. These were strongly enriched at somatic hypermutation motifs. Only 6 of 47 other B-cell lymphomas with the IG-MYC translocation (13%) carried ID3 mutations. These findings suggest that cooperation between ID3 inactivation and IG-MYC translocation is a hallmark of Burkitt lymphomagenesis.

Borrowing strength: a likelihood ratio test for related sparse signals

MOTIVATION

Cancer biology is a field where the complexity of the phenomena battles against the availability of data. Often only a few observations per signal source, i.e. genes, are available. Such scenarios are becoming increasingly more relevant as modern sensing technologies generally have no trouble in measuring lots of channels, but where the number of subjects, such as patients or samples, is limited. In statistics, this problem falls under the heading 'large p, small n'. Moreover, in such situations the use of asymptotic analytical results should generally be mistrusted.

RESULTS

We consider two cancer datasets, with the aim to mine the activity of functional groups of genes. We propose a hierarchical model with two layers in which the individual signals share a common variance component. A likelihood ratio test is defined for the difference between two collections of corresponding signals. The small number of observations requires a careful consideration of the bias of the statistic, which is corrected through an explicit Bartlett correction. The test is validated on Monte Carlo simulations, which show improved detection of differences compared with other methods. In a leukaemia study and a cancerous fibroblast cell line, we find that the method also works better in practice, i.e. it gives a richer picture of the underlying biology.

AVAILABILITY

The MATLAB code is available from the authors or on http://www.math.rug.nl/stat/Software.

CONTACT

e.c.wit@rug.nl d.bakewell@liv.ac.uk.

Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data

Background

Based on available biological information, genomic data can often be partitioned into pre-defined sets (e.g. pathways) and subsets within sets. Biologists are often interested in determining whether some pre-defined sets of variables (e.g. genes) are differentially expressed under varying experimental conditions. Several procedures are available in the literature for making such determinations, however, they do not take into account information regarding the subsets within each set. Secondly, variables (e.g. genes) belonging to a set or a subset are potentially correlated, yet such information is often ignored and univariate methods are used. This may result in loss of power and/or inflated false positive rate.

Results

We introduce a multiple testing-based methodology which makes use of available information regarding biologically relevant subsets within each pre-defined set of variables while exploiting the underlying dependence structure among the variables. Using this methodology, a biologist may not only determine whether a set of variables are differentially expressed between two experimental conditions, but may also test whether specific subsets within a significant set are also significant.

Conclusions

The proposed methodology; (a) is easy to implement, (b) does not require inverting potentially singular covariance matrices, and (c) controls the family wise error rate (FWER) at the desired nominal level, (d) is robust to the underlying distribution and covariance structures. Although for simplicity of exposition, the methodology is described for microarray gene expression data, it is also applicable to any high dimensional data, such as the mRNA seq data, CpG methylation data etc.

From hybridization theory to microarray data analysis: performance evaluation

Background

Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most popular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a novel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics and uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are then employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods both in the old and in the newest generation of GeneChips.

Results

Tissue mixture and Latin Square datasets (provided by Affymetrix) were used to assess the performances of the differential expression analysis depending on the preprocessing strategy. A correlation analysis conducted on the tissue mixture data reveals that the median-polish algorithm allows to best summarize AffyILM concentrations computed at the probe-level. Those correlation results are equivalent to the best correlations observed using popular preprocessing methods relying on intensity values. The performances of each tested preprocessing algorithm were quantified using the Latin Square HG-U133A dataset, thanks to the comparison of differential analysis results with the list of spiked genes. The figures of merit generated illustrates that the performances associated to AffyILM(medianpolish), inferred from the present statistical analysis, are comparable to the best performing strategies previously reported.

Conclusions

Converting probe intensities to estimates of target concentrations prior to the statistical analysis, AffyILM(medianpolish) is one of the best performing strategy currently available. Using hybridization theory, probe-level estimates of target concentrations should be identically distributed. In the future, a probe-level multivariate analysis of the concentrations should be compared to the univariate analysis of probe-set summarized expression data.

A network-based gene-weighting approach for pathway analysis

Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.

Multiple testing for gene sets from microarray experiments

Background

A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome.

Results

In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.

Conclusions

Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.

Connectedness of PPI network neighborhoods identifies regulatory hub proteins

MOTIVATION

With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function.

RESULTS

We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease.

AVAILABILITY

Code is available at http://bcb.cs.tufts.edu/hubcomps.

CONTACT

fox.andrew.d@gmail.com; slonim@cs.tufts.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Investigating the effect of paralogs on microarray gene-set analysis

Background

In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research.

Results

We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http://www.cbio.uct.ac.za/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs.

Conclusions

The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets

Background

Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms.

Results

As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets.

Conclusions

The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Gene set analysis exploiting the topology of a pathway

Background

Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.

Results

We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression.

Conclusions

The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation.

Informatics for Infectious Disease Research and Control

The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.

Alterations of gene expression in blood cells associated with chronic fatigue in breast cancer survivors

Fatigue is one of the most frequent complaints among breast cancer survivors. However, mechanisms underlying persisting fatigue after end of treatment are poorly understood. To explore whether biological processes underlying persistent fatigue can affect gene expression of blood cells, genome-wide expression analyses were performed on whole blood samples from breast cancer survivors classified as chronic fatigued 2-6 years after diagnosis. Non-fatigued survivors served as controls. Several gene sets involved in plasma- and B-cell pathways differed between the chronic fatigued and the non-fatigued, suggesting that a dysregulation in these pathways is associated with chronic fatigue and that a B-cell-mediated inflammatory process might underlie fatigue. The chronic fatigued also had a higher level of leucocytes, lymphocytes and neutrophiles compared with the non-fatigued, thus further indicating that an activation of the immune system plays a role in the biology of chronic fatigue in breast cancer survivors.

A general modular framework for gene set enrichment analysis

Background

Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear.

Results

We conduct an extensive survey of statistical approaches for gene set analysis and identify a common modular structure underlying most published methods. Based on this finding we propose a general framework for detecting gene set enrichment. This framework provides a meta-theory of gene set analysis that not only helps to gain a better understanding of the relative merits of each embedded approach but also facilitates a principled comparison and offers insights into the relative interplay of the methods.

Conclusion

We use this framework to conduct a computer simulation comparing 261 different variants of gene set enrichment procedures and to analyze two experimental data sets. Based on the results we offer recommendations for best practices regarding the choice of effective procedures for gene set enrichment analysis.

Microarray-based gene set analysis: a comparison of current methods

Background

The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.

Results

Each of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.

Conclusion

Based on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics.

Gene set analyses for interpreting microarray experiments on prokaryotic organisms

BACKGROUND

Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes.

RESULTS

We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR.

CONCLUSION

MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

Gene-set analysis and reduction

Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between 'self-contained' versus 'competitive' methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of 'core members' that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.

Gene expression in human peripheral blood mononuclear cells upon acute ischemic stroke

BACKGROUND AND PURPOSE

Ischemic stroke provokes a systemic inflammatory response. The purpose of this study was to characterize this response on the gene expression level in circulating mononuclear leukocytes from acute ischemic stroke (AIS) patients.

METHODS

RNA from peripheral blood mononuclear cells (PBMCs) of AIS patients (24 + 2 hours after onset of symptoms) was analyzed with Affymetrix U133A GeneChips using a pooled design. We compared the gene expression signature from AIS patients (n = 20), stroke survivors (n = 15), patients with acute traumatic brain injury (ATBI, n = 15) and healthy control subjects without vascular risk factors (n = 15).

RESULTS

Expression levels of 9682 probe sets with present calls on each GeneChip were compared. Between AIS patients and stroke survivors or between AIS patients and ATBI patients there were no significant differences in expression values of single genes after correction for multiple testing. However, comparison of the PBMC expression profiles from AIS patients and healthy subjects revealed significantly different expression (p = 0.012) of a single probe set, specific for phosphodiesterase 4 D (PDE4D). In order to detect modest expression differences in multiple genes with a presumed cumulative effect we studied the gene expression of functional groups of genes by global statistical tests. Analysis of 11 gene groups revealed differential expression between AIS patients and healthy subjects for genes involved in the inflammatory response (GeneOntology GO:0006954). Genes encoding the N-formyl peptide receptor-like 1 (FPRL1), interleukin-1 receptor antagonist (IL1RN) and complement component 3a receptor 1 (C3AR1) contributed most to the observed difference.

CONCLUSIONS

This transcriptome analysis did not identify significant changes between circulating mononuclear cells from AIS patients 24 hours after stroke and closely matched stroke survivors. However, comparing AIS patients with healthy control subjects revealed measurable differences in PDE4D and in inflammatory response genes when considered as a set.

Comparison of globin RNA processing methods for genome-wide transcriptome analysis from whole blood

Aims: Whole blood is likely to become the prime tissue to detect biomarkers using gene expression analyses. In this study, we assessed whether whole-blood or globin-reduced RNA provides the most robust and sensitive results to detect small gene expression changes, for example, in response to hormone therapy (HT) exposure. Material & methods: Each sample (n = 12) was processed according to three different protocols: no globin reduction, globin reduction using peptide nucleic acids (PNAs) and globin reduction using magnetic beads in the GlobinClear™ kit from Ambion. Results: Both globin reduction approaches using Ambion kit and PNAs were efficient at reducing globin RNA. However, globin reduction using Ambion kit also lowered the remaining cRNA. Samples processed with PNAs gave an intermediary profile closest to the no globin reduction group, with a slight increase in sensitivity of transcript detection and decrease in variability, but a loss of reproducibility. Conclusions: Globin RNA processing method in blood transcriptome analyses should be optimized for each microarray platform to be used. In our study, globin reduction was not found to be beneficial using the Applied Biosystems microarray system (AB1700).

GlobalANCOVA: exploration and assessment of gene group effects

MOTIVATION

Several authors have studied expression in gene sets with specific goals: overrepresentation of interesting genes in functional groups, predictive power for class membership and searches for groups where the constituent genes show coordinated changes in expression under the experimental conditions. The purpose of this article is to follow the third direction. One important aspect is that the gene sets under analysis are known a priori and are not determined from the experimental data at hand. Our goal is to provide a methodology that helps to identify the relevant structural constituents (phenotypical, experimental design, biological component) that determine gene expression in a group.

RESULTS

Gene-wise linear models are used to formalize the structural aspects of a study. The full model is contrasted with a reduced model that lacks the relevant design component. A comparison with respect to goodness of fit is made and quantified. An asymptotic test and a permutation test are derived to test the null hypothesis that the reduced model sufficiently explains the observed expression within the gene group of interest. Graphical tools are available to illustrate and interpret the results of the analysis. Examples demonstrate the wide range of application.

AVAILABILITY

The R-package GlobalAncova (http://www.bioconductor.org) offers data and functions as well as a vignette to guide the user through specific analysis steps.

Comparative evaluation of gene-set analysis methods

Background

Multiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated with a binary phenotype. Following Goeman and Bühlmann's recent review, we compared statistical performance of three methods, namely Global Test, ANCOVA Global Test, and SAM-GS, that test "self-contained null hypotheses" Via. subject sampling. The three methods were compared based on a simulation experiment and analyses of three real-world microarray datasets.

Results

In the simulation experiment, we found that the use of the asymptotic distribution in the two Global Tests leads to a statistical test with an incorrect size. Specifically, p-values calculated by the scaled χ² distribution of Global Test and the asymptotic distribution of ANCOVA Global Test are too liberal, while the asymptotic distribution with a quadratic form of the Global Test results in p-values that are too conservative. The two Global Tests with permutation-based inference, however, gave a correct size. While the three methods showed similar power using permutation inference after a proper standardization of gene expression data, SAM-GS showed slightly higher power than the Global Tests. In the analysis of a real-world microarray dataset, the two Global Tests gave markedly different results, compared to SAM-GS, in identifying pathways whose gene expressions are associated with p53 mutation in cancer cell lines. A proper standardization of gene expression variances is necessary for the two Global Tests in order to produce biologically sensible results. After the standardization, the three methods gave very similar biologically-sensible results, with slightly higher statistical significance given by SAM-GS. The three methods gave similar patterns of results in the analysis of the other two microarray datasets.

Conclusion

An appropriate standardization makes the performance of all three methods similar, given the use of permutation-based inference. SAM-GS tends to have slightly higher power in the lower α-level region (i.e. gene sets that are of the greatest interest). Global Test and ANCOVA Global Test have the important advantage of being able to analyze continuous and survival phenotypes and to adjust for covariates. A free Microsoft Excel Add-In to perform SAM-GS is available from .

Transcriptional profiling of transurethral resection samples provides insight into molecular mechanisms of hormone refractory prostate cancer

The molecular mechanisms for hormone-resistant prostate cancer progression still remain elusive, mainly due to the limited availability of corresponding tissue. As transurethral resection (TUR) is a common palliative therapy for patients with hormone refractory prostate cancer (HRPC) who have subvesical obstruction, we aimed to demonstrate that TUR samples can be used to identify significantly affected biological pathways during the switch to HRPC using oligonucleotide microarray analysis. Among the most significantly deregulated pathways in HRPC, we observed an induction of oxidative phosphorylation and a repression of cytoskeletal components.

Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees

Clustering of microarray gene expression data is performed routinely, for genes as well as for samples. Clustering of genes can exhibit functional relationships between genes; clustering of samples on the other hand is important for finding e.g. disease subtypes, relevant patient groups for stratification or related treatments. Usually this is done by first filtering the genes for high-variance under the assumption that they carry most of the information needed for separating different sample groups. If this assumption is violated, important groupings in the data might be lost. Furthermore, classical clustering methods do not facilitate the biological interpretation of the results. Therefore, we propose to methodologically integrate the clustering algorithm with prior biological information. This is different from other approaches as knowledge about classes of genes can be directly used to ease the interpretation of the results and possibly boost clustering performance. Our approach computes dendrograms that resemble decision trees with gene classes used to split the data at each node which can help to find biologically meaningful differences between the sample groups. We have tested the proposed method both on simulated and real data and conclude its usefulness as a complementary method, especially when assumptions of few differentially expressed genes along with an informative mapping of genes to different classes are met.

Pathogen profiling for disease management and surveillance

The usefulness of rapid pathogen genotyping is widely recognized, but its effective interpretation and application requires integration into clinical and public health decision-making. How can pathogen genotyping data best be translated to inform disease management and surveillance? Pathogen profiling integrates microbial genomics data into communicable disease control by consolidating phenotypic identity-based methods with DNA microarrays, proteomics, metabolomics and sequence-based typing. Sharing data on pathogen profiles should facilitate our understanding of transmission patterns and the dynamics of epidemics.

Modeling in biomedical informatics—An exploratory analysis

OBJECTIVE

Modeling is a significant part of research, education and practice in biomedical and health informatics. Our objective was to explore which types of models of processes are used in current biomedical/health informatics research, as reflected in publications of scientific journals in this field. Also, the implications for medical informatics curricula were investigated.

METHODS

Retrospective, prolective observational study on recent publications of the two official journals of the International Medical Informatics Association (IMIA), the International Journal of Medical Informatics (IJMI) and Methods of Information in Medicine (MIM). All publications of the years 2004 and 2005 from these journals were indexed according to a given list of model types. Random samples out of these publications were analysed in more depth.

RESULTS

Three hundred and eighty-four publications have been analysed, 190 of IJMI and 194 of MIM. For publications in special issues (121 in IJMI) and special topics (132 in MIM) we found differences between theme-centered and conference-centered special issues/special topics (SIT) publications. In particular, we could observe a high variation between modeling in publications of theme-centered SITs. It became obvious that often sound formal knowledge as well as a strong engineering background is needed for carrying out this type of research. Usually, this knowledge and the related skills can be best provided in consecutive B.Sc. and M.Sc. programs in medical informatics (respectively, health informatics, biomedical informatics). If the focus should be primarily on health information systems and evaluation this can be offered in a M.Sc. program in medical informatics.

CONCLUSIONS

In analysing the 384 publications it became obvious that modeling continues to be a major task in research, education and practice in biomedical and health informatics. Knowledge and skills on a broad range of model types are needed in biomedical/health informatics.

A multivariate approach for integrating genome-wide expression data and biological knowledge

MOTIVATION

Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions.

RESULTS

We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.