Gene Set Analysis Methods Research Articles

Measuring consistency among gene set analysis methods: A systematic study.

Gene set analysis is a quantitative approach for generating biological insight from gene expression datasets. The abundance of gene set analysis methods speaks to their popularity, but raises the question of the extent to which results are affected by the choice of method. Our systematic analysis of 13 popular methods using 6 different datasets, from both DNA microarray and RNA-Seq origin, shows that this choice matters a great deal. We observed that the overall number of gene sets reported by each method differed by up to 2 orders of magnitude, and there was a bias toward reporting large gene sets with some methods. Furthermore, there was substantial disagreement between the 20 most statistically significant gene sets reported by the methods. This was also observed when expanding to the 100 most statistically significant reported gene sets. For different datasets of the same phenotype/condition, the top 20 and top 100 most significant results also showed little to no agreement even when using the same method. GAGE, PAGE, and ORA were the only methods able to achieve relatively high reproducibility when comparing the 20 and 100 most statistically significant gene sets. Biological validation on a juvenile idiopathic arthritis (JIA) dataset showed wide variation in terms of the relevance of the top 20 and top 100 most significant gene sets to known biology of the disease, where GAGE predicted the most relevant gene sets, followed by GSEA, ORA, and PAGE.

MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data

Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package "mogsa."

Enhancing gene set enrichment using networks.

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.

SCIA: A Novel Gene Set Analysis Applicable to Data With Different Characteristics.

Gene set analysis is commonly used in functional enrichment and molecular pathway analyses. Most of the present methods are based on the competitive testing methods which assume each gene is independent of the others. However, the false discovery rates of competitive methods are amplified when they are applied to datasets with high inter-gene correlations. The self-contained testing methods could solve this problem, but there are other restrictions on data characteristics. Therefore, a statistically rigorous testing method applicable to different datasets with various complex characteristics is needed to obtain unbiased and comparable results. We propose a self-contained and competitive incorporated analysis (SCIA) to alleviate the bias caused by the limited application scope of existing gene set analysis methods. This is accomplished through a novel permutation strategy using a priori biological networks to selectively permute gene labels with different probabilities. In simulation studies, SCIA was compared with four representative analysis methods (GSEA, CAMERA, ROAST, and NES), and produced the best performance in both false discovery rate and sensitivity under most conditions with different parameter settings. Further, the KEGG pathway analysis on two real datasets of lung cancer showed that the results found by SCIA in both of the two datasets are much more than that of GSEA and most of them could be supported by literature. Overall, SCIA promisingly offers researchers more reliable and comparable results with different datasets.

Enhancing gene set enrichment using networks.

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.

Analysis of clinical significance of expression of AEBP1 in bladder cancer tissue

Objective To clarify its relationship with clinical pathological characteristics of bladder cancer, so as to evaluate the function of AEBP1 as a prognosis marker in bladder cancer, AEBP1 is predicted to promote the development of bladder cancer. Methods Retrospective and prognostic analysis of the public data set of bladder cancer collected from NCBI was performed. Gene set analysis method (GSEA) was used to analyze the related gene set regulated by AEBP1. Results The results showed that the expression of AEBP1 was significantly different in bladder cancer patients with different invasive (P<0.001), grade (P<0.001), T stage (P<0.001) and N stage (P=0.002). The expression of AEBP1 was correlated with the overall survival and specific survival of bladder cancer patients. The specific survival time of patients with bladder cancer with high expression of AEBP1 was significantly lower than that of patients with bladder cancer (P=0.0003, HR=0.2723, 95%CI: 0.1353-0.5481), and the overall survival of patients with bladder cancer with high expression of AEBP1 was significantly lower than that of AEBP1 low expression group (P=0.0159, HR=0.5479, 95%CI: 0.3360-0.8934). The expression of AEBP1 was enriched with myosin production, inflammatory response, UV response, MYC signaling pathway, endothelial-stromal transformation, Notch signaling pathway, ILS-JAK-STAT pathway, IL2-STAT5 signaling pathway, Hedgehog pathway, TNF-α pathway, complement, interferon gamma, top node, coagulation, mucus, hypoxia, angiogenesis, top, allograft rejection related gene. Conclusions AEBP1 is associated with multiple pathological indicators in bladder cancer. The prognosis of patients with bladder cancer in high expression group is worse, and prediction can be used as a potential marker for prognosis of patients with bladder cancer and the target of bladder tumor. Key words: Urinary Bladder Neoplasms; Prognosis

Using Gene-Set Analysis To Gain Biological Knowledge Based On GWAS Results

BackgroundGenome-wide association studies (GWAS) have successfully identified many novel loci for psychiatric traits. At the same time the results of GWAS showed that these traits are highly polygenic, mostly influenced by large numbers of weakly associated variants. To make sense of this complex genetic structure, a range of secondary analysis tools have been developed that place GWAS results in a broader biological context. One such approach is gene-set analysis (GSA), also referred to as pathway analysis. In GSA, genetic variants are aggregated to genes, which are in turn aggregated to sets of genes on the basis of shared biological or functional properties. These gene sets are then analyzed as a whole to determine their relevance to the trait of interest. The main aim of this session is two-fold: 1) To provide an overview of statistical properties of existing GSA approaches and which factors influence type-I and type-II errors, and 2) To introduce conditional gene-set analysis that allows to test for (in)dependence of genetic effects of gene-sets. MethodsWe use simulations to investigate statistical properties of several GSA approaches. By varying factors such as linkage-disequilibrium, gene-length and gene-set size we investigate the effects of these on type I and II errors. We developed a novel test for conditional gene-set analysis, which is validated using simulations and an empirical example. ResultsWe show that several existing GSA methods have undesirable statistical properties, while at least two have desirable properties. We also show how conditional gene-set analyses can provide further biological insight beyond the regular gene-set approach. ConclusionIt is important to be aware of the conditions under which GSA approaches can be used and how they can be interpreted. Applying conditional gene-set analyses is a useful novel way of using results of GWAS to gain further biological insight.

Association of breast cancer risk with polymorphisms in genes involved in the metabolism of xenobiotics and interaction with tobacco smoking: A gene-set analysis.

Single nucleotide polymorphisms (SNPs) in genes involved in xenobiotics metabolism (XM) are suspected to play a role in breast cancer risk. However, previous findings based on a SNP by SNP approach need to be replicated taking into account the combined effects of multiple SNPs. We used a gene-set analysis method to study the association between breast cancer risk and genetic variation in XM genes (seen as a set of SNPs) and in the XM pathway (seen as a set of genes). We also studied the interaction between variants in XM genes and tobacco smoking. The analysis was conducted in a case-control study of 1,125 cases and 1,172 controls. Using a dedicated chip, genotyping data of 585 SNPs in 68 XM genes were available. Genetic variation in the whole XM pathway was significantly associated with premenopausal breast cancer risk (p = 0.008). This association was mainly driven by genetic variation in NAT2, CYP2C18, CYP2C19, AKR1C2 and ALDH1A3. The association between the XM gene pathway and breast cancer was observed among current and previous smokers, but not among never smokers (p = 0.013 for interaction between XM genes and tobacco smoking status). The association with breast cancer risk indicates that XM genes variants may play a role in breast carcinogenesis through their detoxification function of environmental pollutants, such as those contained in tobacco smoke.

A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time

BackgroundFeature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection.MethodsWe adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data.ResultsUsing a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene’s expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time.ConclusionsWe believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.

Probabilistic prioritization of candidate pathway association with pathway score

BackgroundCurrent methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes.ResultsHere we propose a two-stage procedure to prioritize the pathways/gene-sets. In the first stage we develop a pathway-level measure with three properties. First, it contains all genes (differentially expressed or not) in the same set, and summarizes the collective effect of all genes per sample. Second, this pathway score accounts for the correlation between genes by synchronizing their correlation directions. Third, the score includes a rank transformation to enhance the variation among samples as well as to avoid the influence of extreme heterogeneity among genes. In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods.ConclusionsThe proposed summary pathway score provides for each sample an overall evaluation of gene expression in a gene-set. It demonstrates the advantages of including all genes in the set and the synchronization of correlation direction. The simultaneous utilization of all pathway-level scores in a Bayesian model not only offers a probabilistic evaluation and ranking of the pathway association but also presents good accuracy in identifying the top-ranking pathways. The resulting recommendation list of ranked pathways can be a reference for potential target therapy or for future allocation of research resources.

To select relevant features for longitudinal gene expression data by extending a pathway analysis method.

The emerging field of pathway-based feature selection that incorporates biological information conveyed by gene sets/pathways to guide the selection of relevant genes has become increasingly popular and widespread. In this study, we adapt a gene set analysis method - the significance analysis of microarray gene set reduction (SAMGSR) algorithm to carry out feature selection for longitudinal microarray data, and propose a pathway-based feature selection algorithm - the two-level SAMGSR method. By using simulated data and a real-world application, we demonstrate that a gene's expression profiles over time can be considered as a gene set. Thus a suitable gene set analysis method can be utilized or modified to execute the selection of relevant genes for longitudinal omics data. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel pathway-based feature selection algorithms.

Gene set analysis methods: a systematic comparison

BackgroundGene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope.MethodsIn this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods.ResultsA software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method’s sensitivity towards various effect sizes.ConclusionsThe results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.

ARD-PRED: an in silico tool for predicting age-related-disorder-associated proteins

Interactions among various proteins largely govern cellular processes, and this leads to numerous efforts toward extraction of information related to the proteins, their interactions and the function which is determined by these interactions. The main concern of the study is to present interface analysis of age-related-disorder (ARD)-related proteins to shed light on details of the interactions. It also emphasizes on the importance of using structures in network studies. A major goal in the post-genomic era is to identify and characterize disease susceptibility of genes and to apply this knowledge to disease prevention and treatment. Attempts have been made to integrate biological knowledge of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways into the genomics field. Many gene set analysis methods have been used to detect disease-related risk pathways. The present study combines the network-centered approach with three-dimensional structures to comprehend the biology behind ARDs. Interface properties of the interacting complexes have been used as descriptors to classify age-related associated proteins and non-age-related associated proteins. Machine learning has been used to generate a classifier which is used to predict potential age-related proteins. The ARD-PRED tool achieved an overall accuracy in terms of precision score 81.5, recall score 81.2, accuracy value 79 and ROC Area score 89.6, F-measure 81.1. The tool has been made online at http://genomeinformatics.dtu.ac.in/ARD-PRED/ . The present work would comprehend ongoing research in the field of ARDs and would also significantly improvise the understanding of the molecular mechanism of age-related diseases.

Distance-correlation based gene set analysis in longitudinal studies.

Longitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

Identification of satellite cells from anole lizard skeletal muscle and demonstration of expanded musculoskeletal potential

The lizards are evolutionarily the closest vertebrates to humans that demonstrate the ability to regenerate entire appendages containing cartilage, muscle, skin, and nervous tissue. We previously isolated PAX7-positive cells from muscle of the green anole lizard, Anolis carolinensis, that can differentiate into multinucleated myotubes and express the muscle structural protein, myosin heavy chain. Studying gene expression in these satellite/progenitor cell populations from A. carolinensis can provide insight into the mechanisms regulating tissue regeneration. We generated a transcriptome from proliferating lizard myoprogenitor cells and compared them to transcriptomes from the mouse and human tissues from the ENCODE project using XGSA, a statistical method for cross-species gene set analysis. These analyses determined that the lizard progenitor cell transcriptome was most similar to mammalian satellite cells. Further examination of specific GO categories of genes demonstrated that among genes with the highest level of expression in lizard satellite cells were an increased number of genetic regulators of chondrogenesis, as compared to mouse satellite cells. In micromass culture, lizard PAX7-positive cells formed Alcian blue and collagen 2a1 positive nodules, without the addition of exogenous morphogens, unlike their mouse counterparts. Subsequent quantitative RT-PCR confirmed up-regulation of expression of chondrogenic regulatory genes in lizard cells, including bmp2, sox9, runx2, and cartilage specific structural genes, aggrecan and collagen 2a1. Taken together, these data suggest that tail regeneration in lizards involves significant alterations in gene regulation with expanded musculoskeletal potency.

Linking metabolic network features to phenotypes using sparse group lasso.

Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. samal@combine.rwth-aachen.de or frohlich@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BackgroundDespite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.ResultsWe developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward’s method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.ConclusionWe validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive ‘Fuzzy Inference of Gene-sets (FIGS)’ package (GitHub: https://github.com/Thakar-Lab/FIGS) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

Background: Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies.Objective: In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology.Method: We used ‘R’, an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D.Result: Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology.Conclusion: Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling

Gene set analysis controlling for length bias in RNA-seq experiments

BackgroundIn gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become an important alternative to traditional expression arrays in gene expression studies. Challenges exist in adopting the existent algorithms to RNA-seq data given the intrinsic difference of the technologies and data. In RNA-seq experiments, the measure of gene expression is correlated with gene length. This inherent correlation may cause bias in gene set analysis.ResultsWe develop SeqGSA, a new method for gene set analysis with length bias adjustment for RNA-seq data. It extends from the R package GSA designed for microarrays. Our method compares the gene set maxmean statistic against permutations, while also taking into account of the statistics of the other gene sets. To adjust for the gene length bias, we implement a flexible weighted sampling scheme in the restandardization step of our algorithm. We show our method improves the power of identifying significant gene sets that are affected by the length bias. We also show that our method maintains the type I error comparing with another representative method for gene set enrichment test.ConclusionsSeqGSA is a promising tool for testing significant gene pathways with RNA-seq data while adjusting for inherent gene length effect. It enhances the power to detect gene sets affected by the bias and maintains type I error under various situations.

The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis.

Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis. This is a major handicap to the interpretation of results obtained from a gene set analysis. This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures underlying GSEA and the estimators used for it is crucial. bdebrabant@health.sdu.dk. Supplementary material is available at Bioinformatics online.