Application Of Microarray Technology Research Articles

Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification.

Perspectives on the Application of Cytogenomic Approaches in Chronic Lymphocytic Leukaemia

Chronic lymphocytic leukaemia (CLL) is a haematological malignancy characterised by the accumulation of monoclonal mature B lymphocytes (positive for CD5+ and CD23+) in peripheral blood, bone marrow, and lymph nodes. Although CLL is reported to be rare in Asian countries compared to Western countries, the disease course is more aggressive in Asian countries than in their Western counterparts. It has been postulated that this is due to genetic variants between populations. Various cytogenomic methods, either of the traditional type (conventional cytogenetics or fluorescence in situ hybridisation (FISH)) or using more advanced technology such as DNA microarrays, next generation sequencing (NGS), or genome wide association studies (GWAS), were used to detect chromosomal aberrations in CLL. Up until now, conventional cytogenetic analysis remained the gold standard in diagnosing chromosomal abnormality in haematological malignancy including CLL, even though it is tedious and time-consuming. In concordance with technological advancement, DNA microarrays are gaining popularity among clinicians as they are faster and better able to accurately diagnose the presence of chromosomal abnormalities. However, every technology has challenges to overcome. In this review, CLL and its genetic abnormalities will be discussed, as well as the application of microarray technology as a diagnostic platform.

New mechanistic insights of clear cell renal cell carcinoma from integrated miRNA and mRNA expression profiling studies

Differentially expressed (DE) microRNAs (miRNAs) in clear cell renal cell carcinoma (ccRCC) tissues from pooled samples were reported to affect the tumorigenesis and progression of ccRCC. However, systematic studies on the miRNA-mRNA regulatory networks involved in various pathways in all four stages of the disease are lacking. In this study, we applied microarray technology to perform an integrated analysis of the miRNome and transcriptome in ccRCC tissues from patients at different stages of ccRCC. A total of 604 DEmiRNAs and 6892 DEgenes (DEGs) were identified by comparison with corresponding adjacent normal tissues. The pairing of miRNAs with DEGs were searched using validated miRWalk module, and the pairs were confirmed by comparing with DEmiRNAs in our study. Our results demonstrated that different stages of ccRCC had distinct miRNA/mRNA profiles. However, four common pathways (the complement and coagulation cascades, the pathway for the metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and the pathway for aldosterone-regulated sodium reabsorption) were enriched by targets of DEmiRNAs at all stages of ccRCC. We carried out an extensive analysis of data on miR-16, which had the most target genes, and found that its differential expression was validated in The Cancer Genome Atlas dataset. We also verified the correlation between miR-16 expression and target pathways by gene set enrichment analysis and in vitro experiments. High miR-16 level was also associated with shorter survival time in ccRCC. Our work presents a systematic profiling of miRNA, mRNA and pathways regulated by miRNAs in different stages of ccRCC. Our cross-omics results also identify four common pathways that function aberrantly in all stages of the disease. These pathways are likely to be critical in occurrence and progression of ccRCC. These common dysfunctional pathways have the potential to serve as therapeutic targets and diagnostic biomarkers, whereas miRNAs (miR-20, 484, 497) differentially expressed in only stage I tissues and in blood could be used to diagnose early-stage ccRCC patients.

Characterization of rubella-specific humoral immunity following two doses of MMR vaccine using proteome microarray technology.

Introduction//BackgroundThe lack of standardization of the currently used commercial anti-rubella IgG antibody assays leads to frequent misinterpretation of results for samples with low/equivocal antibody concentration. The use of alternative approaches in rubella serology could add new information leading to a fuller understanding of rubella protective immunity and neutralizing antibody response after vaccination.MethodsWe applied microarray technology to measure antibodies to all rubella virus proteins in 75 high and 75 low rubella virus-specific antibody responders after two MMR vaccine doses. These data were used in multivariate penalized logistic regression modeling of rubella-specific neutralizing antibody response after vaccination.ResultsWe measured antibodies to all rubella virus structural proteins (i.e., the glycoproteins E1 and E2 and the capsid C protein) and to the non-structural protein P150. Antibody levels to each of these proteins were: correlated with the neutralizing antibody titer (p<0.006); demonstrated differences between the high and the low antibody responder groups (p<0.008); and were components of the model associated with/predictive of vaccine-induced rubella virus-specific neutralizing antibody titers (misclassification error = 0.2).ConclusionOur study supports the use of this new technology, as well as the use of antibody profiles/patterns (rather than single antibody measures) as biomarkers of neutralizing antibody response and correlates of protective immunity in rubella virus serology.

Applications of MicroArray Technology

Applications of MicroArray Technology

Effect of follicular dendritic cell secreted protein on gene expression of human periodontal ligament cells

ObjectiveThe objective of this study was to investigate the specific roles of follicular dendritic cell secreted protein (FDC-SP), a protein exists in saliva, in the inhibition of calcium precipitation during periodontal regeneration, as well as affect phenotype expression of human periodontal ligament cells (hPDLCs) during the differentiation process. DesignTo investigate this, we applied microarray technology to identify gene expression changes in hPDLCs transfected with FDC-SP and then clustered them according to their biological functions. ResultsOne hundred seventy-one genes were found differentially expressed by at least two-fold between FDC-SP -transfected and empty vector-transfected cells. Besides, genes encoding cell-cycle proteins, blood-related and cell differentiation-related proteins tended to be up-regulated after FDC-SP transfection, whereas cytokine/growth factors, signal transduction and metabolism-related genes tended to be down-regulated in hPDLCs overexpression FDC-SP. ConclusionsThe present study investigated FDC-SP’s roles in hPDLCs’ phenotype expression, via comparing the gene expression profiles between FDC-SP -transfected hPDLCs and empty vector-transfected cells upon microarray analysis. hPDLCs overexpression FDC-SP appear to display different gene expression patterns. In all, these observations showed a potential of FDC-SP in the maintenance of PDL homeostasis and its ultimate contribution to periodontal would-healing processes.

Microarray expression profiles of long non-coding RNAs in germinal center-like diffuse large B-cell lymphoma.

Long non-coding RNAs (lncRNAs) are continuously transcribed and are involved in various cellular activities. However, their contributions to the occurrence and development of germinal center B-cell (GCB)-like diffuse large B-cell lymphoma (DLBCL) remain largely unknown. We applied microarray technology to profile the expression of lncRNAs in two different GCB-DLBCL cell lines (OCI-ly1 and OCI-ly19) and normal B lymphocytes. We demonstrated that 21,539 lncRNAs were expressed in all of the samples analyzed. This included 1,648 lncRNAs that showed a ≥2-fold upregulation and 2,671 lncRNAs that displayed a ≥2-fold downregulation in tumor cell lines (P<0.05). The expression levels of 8 lncRNAs were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Bioinformatic analyses (Gene Ontology, pathway and network analysis) were performed to predict how the differentially expressed lncRNAs may function in GCB-DLBCL. Results from the pathway analysis suggested that totals of 64 and 62 biological pathways corresponded to upregulated and downregulated transcripts, respectively (P<0.05). Additionally, we constructed a lncRNA-mRNA network for the purpose of identifying specific coding genes which were co-expressed with 5 selected lncRNAs. Conclusively, our results may contribute to a better understanding of GCB-DLBCL pathogenesis.

Distinctive gene expression profile in women with history of postpartum depression

Postpartum depression (PPD) is a disease which incorporates a variety of depressive states differing in nature and severity. To assist in the understanding of the pathogenesis of the disease, we aimed to ascertain a molecular mechanism underlying PPD development. We applied microarray technology to characterize gene expression of euthymic women with a history of PPD and compared the results with healthy controls. Our study demonstrated that women who considered euthymic on a clinical level, in fact, had an altered molecular profile when compared to participants with no PPD history. We identified nine genes significantly distinguished expression in post- depressive women; they may serve as a diagnostic tool for the detection of a predisposition to PPD. Our findings contribute significantly to the understanding of PPD etiology and its pathogenesis, offer a plausible explanation for the risk of the PPD recurrence, and may also contribute to clinical treatment.

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

P084 : THE HLA PROTEIN CHIP – A HIGH PERFORMANCE MICROARRAY PLATFORM FOR ANTI-HLA ANTIBODY DETECTION UTILIZING SOLUBLE HLA

Aim Successful pairing of a transplant recipient with a donor organ depends on accurate prediction of risk factors for immunologic rejection. As such, there is much interest in accurate clinical measurement of HLA Ab specificities in recipient sera. However, it has become apparent that current methodologies are still surrounded by shortcomings. Here we offer a new solution that is based on recent advancements made in the application of microarray technology in clinical diagnostics, to increase the precision and sensitivity of HLA Ab serology. The aim of this study was to demonstrate the coupling of recombinant soluble HLA proteins to a novel microarray technology as a new, simplified, inexpensive and accurate approach to transplant serology. Methods In order to validate and optimize sHLA microarray performance, multiple 12 × 12 microarrays were fabricated by microfluidic spotting of highly purified soluble HLA class I and II molecules under a range of conditions chosen to optimize protein stabilization on the microarray surface. Of particular interest were the evaluation of maximal signal and background intensities, signal uniformity between different HLA alleles and the structural integrity of the HLA proteins subsequent to microfluidic attachment to the microarray surface. Results HLA protein printed in this array system yielded >65,000 MFI with essentially no background signal. Signal uniformity among alleles tested was consistently below a 10% spread. As assessed by structure-specific antibodies HC-10 and W6/32, it was determined that the native dimer configuration predominated subsequent to microarray printing. Accuracy and reproducibility required for a low cost HLA antibody-screening array was validated with over 200 samples, resulting in a high degree of concordance with known monoclonal Ab patterns and established clinical reference sera. Conclusion We have successfully demonstrated that the combination of microarray and soluble HLA technology has resulted in a simple, accurate, and low cost method for high-performance multiplexed HLA Ab detection, which with additional development in progress will be ready to accommodate very high resolution HLA Ab screening, at time of transplantation and as a routine follow-up test to monitor the earliest stages of graft rejection.

Gene Expression Profiling of PC12 Cells Exposed to Manganese

Chronic exposure to Manganese (Mn) causes manganism with symptoms similar to Parkinson's disease. The objective of this study was to determine the molecular targets of Mn in PC12 cells. We applied microarray technology to identify molecular targets of Mn and RT‐PCR/Western to validate results. Our results showed that Mn altered the expression of 223 genes; 146 were up regulated and 77 were down regulated. The sub‐lethal dose range (38–300 μM MnCl2) initiated metabolic stress, an induction of enolase/aldolase and hemeoxygenase 1 (HO‐1) (7.7 fold). Concentrations greater than 300 μM caused production of H2O2 concomitant to increase of gene expression of antioxidant enzymes; biliverdin reductase, arsenite inducible RNA associated protein, dithiolethione‐inducible gene‐1 (DIG‐1) and thioredoxin reductase 1 with reduced expression of mitochondrial glutaryl‐coenzyme A dehydrogenase (GCDH). In conclusion the results suggest that Mn toxicity is primarily associated with dose dependent induction of HO‐1 and elevated production of H2O2, along with a minor loss of mitochondrial function. (Supported by NIH grant NCRR G12 RR03020 and G12 MD007582).

Clinical Applicability of Microarray Technology in the Diagnosis, Prognostic Stratification, Treatment and Clinical Surveillance of Cervical Adenocarcinoma

Invasive cervical cancer is a common problem worldwide and while rates of squamous cell carcinoma of the cervix (SCC) have been declining with the implementation of community screening programs, adenocarcinoma of the cervix (ADC) has not shown a similar response to screening. At this time, the two entities are tested for and treated with the same clinical algorithms, namely gynecologic cytology and molecular testing for HPV DNA. However, ADC arises more proximally within the cervical canal and frequently occurs below the superficial mucosal lining, precluding it from being easily diagnosed with cytologic examination. Furthermore, ADC has a more aggressive course than SCC and is significantly less responsive to radiotherapy. These factors combine to produce a lesion that is diagnosed at a higher stage and has an overall poorer prognosis than SCC. While both lesions have pathogenic origins with the high-risk species of Human papilloma viruses (HPV), the high association of ADC with HPV 18, a virus with a higher genome integration rate, suggests that the transformation pathway of ADC differs from SCC. Modern methods of molecular analysis can produce gene expression profiles of various cell types and preliminary studies have shown that SCC and ADC do, indeed, produce a distinct genetic signature and can be reproducibly segregated. This technology has promise for clinical applicability in the diagnosis, the prognostic stratification, formation of a treatment care plan and post-therapeutic monitoring of ADC.

Safety, Accuracy and Reproductive Outcome of Preimplantation Genetic Diagnosis

PGD is now performed for 357 different conditions, with over 99.5% accuracy in the leading PGD centers. There is also no restriction in provision of PGD, which may presently be performed for any genetic condition, even in cases that the genetic condition is first identified in one of the parents or only in the affected child. Indications for PGD are steadily expanding, with current wider application of PGD to diseases with genetic predisposition, such as different cancers and cardiovascular disorders, so PGD centers will face a forthcoming increase of PGD requests from this highly sensitive group of at risk couples. Despite recent controversy in PGD for chromosomal disorders, the present progress in improving the accuracy of the procedure through adequate choice of biopsy material and microarray analysis for 24 chromosomes demonstrated the obvious clinical impact of avoiding the transfer of aneuploid embryos. A major breakthrough has been in the development and application of microarray technology for PGD of chromosomal disorders, allowing a highly improved detection of chromosomally abnormal oocytes and embryos, resulting in a higher accuracy and improved clinical outcome following the application of 24-chromosome aneuploidy testing coupled with blastocyst biopsy.

Clinical review: Sepsis and septic shock - the potential of gene arrays

Over the past decade several investigators have applied microarray technology and related bioinformatic approaches to clinical sepsis and septic shock, thus allowing for an assessment of how, or if, this branch of genomic medicine has meaningfully impacted the field of sepsis research. The ability to simultaneously and efficiently measure the steady-state mRNA abundance of thousands of transcripts from a given tissue source (that is, 'transcriptomics') has provided an unprecedented opportunity to gain a broader, genome-level 'picture' of complex and heterogeneous clinical syndromes such as sepsis. A trancriptomic approach to sepsis and septic shock is technically challenging on multiple levels, but nonetheless modest, tangible advances are being realized. These include a genome-level understanding of the complexity of sepsis and septic shock, identification of novel candidate pathways and targets for potential intervention, discovery of novel, candidate diagnostic and stratification biomarkers, and the ability to stratify patients into clinically relevant, expression-based subclasses. The challenges moving forward include robust validation studies, standardization of technical approaches, standardization and further development of analytical algorithms, and large-scale collaborations.

The application of microarray technology to the identification of Tc1-like element sequences in fish genomes

The phylogenetic relationships between Tc1 transposable elements have previously been reported for the genomes of some fish species. However, research in this field has been hindered by the low number of fish genome sequences available in databases. The application of a DNA microarray as a universal tool for Tc1 transposon sequence analysis in fish genomes is described here. A prototype oligonucleotide microarray was constructed and used to compare samples of genomic DNA isolated from selected fish species. These results, combined with earlier reported molecular analysis of Tc1 showed the usefulness of DNA microarray in the screening of transposon sequences.

Polar body-based preimplantation genetic diagnosis for Mendelian disorders

Introduced >20 years ago, the use of polar bodies (PBs), involving sequential removal and genetic analysis of the first (PB1) and second (PB2) PB, provides the option for pre-embryonic diagnosis, when the objection to the embryo biopsy procedures makes preimplantation genetic diagnosis (PGD) non-applicable. PB-based approach has presently been utilized in PGD for genetic and chromosomal disorders, applied either separately, or together with embryo biopsy approaches, especially if there are two or more PGD indications. We present here the world's largest experience of 938 PGD cycles for single-gene disorders performed by PB testing for 146 different monogenic conditions, which resulted in the birth of 345 healthy children (eight pregnancies are still ongoing), providing strong evidence that PB-based PGD is a reliable and safe procedure, with an extremely high accuracy rate of over 99%. With application of microarray technology, PB-based approach will be utilized for increasing number of indications, involving simultaneous testing for 24 chromosomes and single-gene disorders.

Mycobacterium bovis-BCG Vaccination Induces Specific Pulmonary Transcriptome Biosignatures in Mice

BackgroundIn the present study, we applied microarray technology to define biosignatures by microarray transcriptome analysis in lung and spleen samples after BCG vaccination and M. bovis infection of BALB/c mice. The aims were two-fold, namely to define biosignatures that could predict vaccine success before challenge, and biomarker patterns that correlated with anamnestic protective responses following exposure to virulent M. bovis. Further, these biosignatures should be detectable without in vitro antigenic challenge.Principal FindingsAfter BCG vaccination, we defined a specific pulmonary gene expression signature related to the connective tissue development and function network that predicted vaccine success before M. bovis challenge. In addition, a Th17-related cytokine profile was found that correlated with vaccine-induced protective immunity following infection with virulent M. bovis in the lung as well as additional genes that were up-regulated in the spleens of vaccinated animals post-infection related to neutrophil biology and inflammation.ConclusionsThis study has therefore prioritized both biomarkers predicting vaccination success before challenge and bio-signatures that are potentially associated with protective immune responses that will be useful to evaluate future vaccine candidates.

Toxicogenomic profiling in maternal and fetal rodent brains following gestational exposure to chlorpyrifos

Considering the wide variety of effects that have been reported to occur in the developmental neurotoxicity of chlorpyrifos (CP) and the lack of consensus on their dependence of brain acetylcholinesterase (AChE) activity inhibition, we applied microarray technology to explore dose-dependent alterations in transcriptional response in the fetal and maternal C57BL/6 mouse brain after daily gestational exposure (days 6 to 17) to CP (2, 4, 10, 12 or 15 mg/kg, sc). We identified significantly altered genes across doses and assessed for overrepresentation of Gene Ontology (GO) biological processes and KEGG pathways. We further clustered genes based on their expression profiles across doses and repeated the GO/pathways analysis for each cluster. The dose–effect relationship of CP on gene expression, both at the gene and pathway levels was non-monotonic and not necessarily related to brain AChE inhibition. The largest impact was observed in the 10 mg/kg dose group which was also the LOAEL for brain AChE inhibition. In the maternal brain, lower doses (4 mg/kg) influenced GO categories and pathways such as cell adhesion, behavior, lipid metabolism, long-term potentiation, nervous system development, neurogenesis, synaptic transmission. In the fetal brain, lower doses (2 and/or 4 mg/kg) significantly altered cell division, translation, transmission of nerve impulse, chromatin modification, long-term potentiation. In addition, some genes involved in nervous system development and signaling were shown to be specifically influenced by these lower CP doses. Our approach was sensitive and reflected the diversity of responses known to be disrupted by CP and highlighted possible additional consequences of CP neurotoxicity, such as disturbance of the ubiquitin proteasome system.

Microarray Data Analysis Using Peripheral Blood Samples Suggests Differential Enrichment of Signaling Pathways between Breast Cancer Patients and Normal Subjects.

Abstract Background: Breast cancer is the second most common cancer among women in the United States and the second leading cause of cancer death in women. Early and accurate diagnosis is crucial for better treatment and reduction of mortality. Diagnostics based on blood samples are being developed for many diseases including a variety of cancers.Gene expression profiling has been widely used to investigate the mechanisms of tumorigenesis with the goal of developing novel treatment strategies. We have applied microarray technology to study the gene expression signatures in blood samples from patients with invasive breast cancer, with benign breast disease and from disease free (normal) control groups to find potential diagnostic patterns. We have identified differentially expressed genes in each group and investigated the pathways these genes are involved in and how these are regulated.Material and Methods: Human blood samples from 102 invasive breast cancer patients, 57 patients with benign breast disease and 102 normal controls were collected for this study. Patients are enrolled in the Clinical Breast Care Project (CBCP) following HIPAA-compliant IRB approved protocols, with proven breast pathology diagnosis categories. Affymetrix HG-U133 Plus 2 GeneChips were used to investigate the gene expression profile. Microarray experiments were performed following Affymetrix protocols. CEL files were then analyzed with RMA algorithm for the calculation of gene expression matrix. Differentially expressed genes were identified between different groups using Wilcoxon rank sum test. We also applied Gene Set Enrichment Analysis (GSEA) to the entire gene expression profiles to investigate the gene sets or pathways enriched in different groups.Results: We identified about 2,051 differentially expressed genes between normal and invasive groups (p &amp;lt; 0.001 and Fold Change &amp;gt; 1.2). Furthermore, about 445 and 51 genes were identified in normal vs. benign and benign vs. invasive groups respectively. We performed GSEA using 395 gene sets from pathway databases, initially focusing on normal vs. invasive groups. 123 gene sets are highly expressed in the normal group and 272 gene sets are highly expressed in the invasive group. For the normal group, 10 gene sets are significantly enriched and 22 gene sets are significantly enriched in the invasive group (p &amp;lt; 0.05). These results show that some important pathways are down regulated in the invasive group, such as, the B-cell antigen receptor pathway, the BCR signaling pathway, the T-cell signal transduction pathway, the IL4 receptor pathway, the PIP3 pathway, the ERK pathway, etc. The pathways up regulated in the invasive group are oxidative phosphorylation pathway, ATP synthesis, etc.Discussion: Our results suggest that a list of genes differentially expressed in different groups and they may be used to compose cancer marker panels which can be integrated with currently clinical procedures for cancer diagnosis. We also find some crucial pathways enriched in each group. Most of the pathways downregulated in the blood of patients in the invasive group are related to the immune response. In contrast, most of pathways upregulated in the blood of patients in the invasive group are associated with metabolism. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3024.

Gene set enrichment analysis made simple

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, choose an appropriate cut-off, and create a list of candidate genes. This approach has been criticised for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, the most popular method, gene set enrichment analysis (GSEA), seems overly complicated. Furthermore, GSEA is based on a statistical test known for its lack of sensitivity. In this article we compare the performance of a simple alternative to GSEA. We find that this simple solution clearly outperforms GSEA. We demonstrate this with eight different microarray datasets.