A Biological Evaluation of Six Gene Set Analysis Methods for Identification of Differentially Expressed Pathways in Microarray Data

Gene-set analysis of microarray data evaluates biological pathways, or gene sets, for their differential expression by a phenotype of interest. In contrast to the analysis of individual genes, gene-set analysis utilizes existing biological knowledge of genes and their pathways in assessing differential expression. This paper evaluates the biological performance of five gene-set analysis methods testing “self-contained null hypotheses” via subject sampling, along with the most popular gene-set analysis method, Gene Set Enrichment Analysis (GSEA). We use three real microarray analyses in which differentially expressed gene sets are predictable biologically from the phenotype. Two types of gene sets are considered for this empirical evaluation: one type contains “truly positive” sets that should be identified as differentially expressed; and the other type contains “truly negative” sets that should not be identified as differentially expressed. Our evaluation suggests advantages of SAM-GS, Global, and ANCOVA Global methods over GSEA and the other two methods

Improving GSEA for Analysis of Biologic Pathways for Differential Gene Expression across a Binary Phenotype

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of single genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the single-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Specifically, we illustrate, in a simulation study, that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are highly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs perfectly in the simulation study: none of the null gene sets is identified with statistical significance, while all of the truly-associated gene sets are. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show the advantages of SAM-GS over GSEA, both statistically and biologically

Improving gene set analysis of microarray data by SAM-GS

<p>Abstract</p> <p>Background</p> <p><it>Gene-set </it>analysis evaluates the expression of biological pathways, or <it>a priori </it>defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).</p> <p>Results</p> <p>Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with <it>p53 </it>mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of <it>p53</it>. Of the 31 gene sets, 11 actually involve <it>p53 </it>directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of <it>p53 </it>signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with <it>p53</it>.</p> <p>Conclusion</p> <p>We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.</p

Fatal Pneumococcus Sepsis after Treatment of Late Antibody-Mediated Kidney Graft Rejection

Antibody-mediated rejection (ABMR) is a major cause of late renal allograft dysfunction and graft loss. Risks and benefits of treatment of late ABMR have not been evaluated in randomized clinical trials. We report on a 35-year-old patient with deterioration in renal function and progressive proteinuria 15 years after transplantation. Recurrent infections after a splenectomy following traumatic splenic rupture 3 years earlier had led to reduction of immunosuppression. Renal transplant biopsy showed glomerular double contours, 40% fibrosis/tubular atrophy, peritubular capillaritis, and positive C4d staining indicating chronic-active ABMR. ABMR treatment was initiated with steroids, plasmapheresis, and rituximab. Fourteen days later, she presented to the emergency department with fever, diarrhea, vomiting, and hypotension. Despite antibiotic treatment she deteriorated with progressive hypotension, capillary leak with pleural effusion, peripheral edema, and progressive respiratory insufficiency. She died due to septic shock five days after admission. Blood cultures showed Streptococcus pneumoniae, consistent with a diagnosis of overwhelming postsplenectomy infection syndrome, despite protective pneumococcus vaccination titers. We assume that the infection was caused by one of the strains not covered by the Pneumovax 23 vaccination. The increased immunosuppression with B cell depletion may have contributed to the overwhelming course of this infection

Therapeutic plasma exchange in a tertiary care center: 185 patients undergoing 912 treatments - a one-year retrospective analysis

Abstract Background Therapeutic plasma exchange (TPE) is increasingly used throughout the world. Although the procedure itself is fairly standardized, it is yet unknown how the underlying disease entities influence the key coordinates of the treatment. Methods Retrospective chart review. The treatment indications were clustered into four categories. Data are presented as median and interquartile (25–75%) range [IQR]. Results Within 1 year, 912 TPE treatments were performed in 185 patients (90 female, 48.6%). The distribution of the treatment numbers to the pre-specified disease categories were as follows: transplantation (35.7%), neurology (31.9%), vasculitis and immunological disease (17.3%), and others including thrombotic microangiopathy (8.1%), critical care related diseases (5.4%), hematology [multiple myeloma] (1.1%), and endocrine disorders (0.5%). The calculated plasma volume was significantly higher in patients with vasculitis and immunological diseases (3984 [3433–4439] ml) as compared to patients treated for transplant related indications (3194 [2545–3658] ml; p = 0.0003) and neurological diseases (3058 [2533–3359] ml; p < 0.0001). This was mainly due to the differences in the hematocrit which was 30.5 [27.0–33.6] % in the vasculitis/immunological disease patients and 40.2 [37.5–42.9] % in the neurological patients; p < 0.0001. Interestingly, treatment time using a membrane based technology was significantly longer than TPE using a centrifugal device 135.0 [125.0–140.0] min vs. 120.0 [112.5–135.0] min. Furthermore, the relative exchanged plasma volume was significantly lower in the treatment of vasculitis and immunological diseases as compared to treatments of transplant related indications and neurological diseases. Conclusion Patients with low hematocrit and high body weight do not receive the minimum recommended dose of exchange volume. Centrifugal TPE allowed faster plasma exchange than membrane TPE

Molecular Correlates of Renal Function in Kidney Transplant Biopsies

The molecular changes in the parenchyma that reflect disturbances in the function of kidney transplants are unknown. We studied the relationships among histopathology, gene expression, and renal function in 146 human kidney transplant biopsies performed for clinical indications. Impaired function (estimated GFR) correlated with tubular atrophy and fibrosis but not with inflammation or rejection. Functional deterioration before biopsy correlated with inflammation and tubulitis and was greater in cases of rejection. Microarray analysis revealed a correlation between impaired renal function and altered expression of sets of transcripts consistent with tissue injury but not with those consistent with cytotoxic T cell infiltration or IFN-γ effects. Multivariate analysis of clinical variables, histologic lesions, and transcript sets confirmed that expression of injury-related transcript sets independently correlated with renal function. Analysis of individual genes confirmed that the transcripts with the greatest positive or negative correlations with renal function were those suggestive of response to injury and parenchymal dedifferentiation not inflammation. We defined new sets of genes based on individual transcripts that correlated with renal function, and these highly correlated with the previously developed injury sets and with atrophy and fibrosis. Thus, in biopsies performed for clinical reasons, functional disturbances are reflected in transcriptome changes representing tissue injury and dedifferentiation but not the inflammatory burden

A molecular classifier for predicting future graft loss in late kidney transplant biopsies

Kidney transplant recipients that develop signs of renal dysfunction or proteinuria one or more years after transplantation are at considerable risk for progression to renal failure. To assess the kidney at this time, a “for-cause” biopsy is performed, but this provides little indication as to which recipients will go on to organ failure. In an attempt to identify molecules that could provide this information, we used micorarrays to analyze gene expression in 105 for-cause biopsies taken between 1 and 31 years after transplantation. Using supervised principal components analysis, we derived a molecular classifier to predict graft loss. The genes associated with graft failure were related to tissue injury, epithelial dedifferentiation, matrix remodeling, and TGF-β effects and showed little overlap with rejection-associated genes. We assigned a prognostic molecular risk score to each patient, identifying those at high or low risk for graft loss. The molecular risk score was correlated with interstitial fibrosis, tubular atrophy, tubulitis, interstitial inflammation, proteinuria, and glomerular filtration rate. In multivariate analysis, molecular risk score, peritubular capillary basement membrane multilayering, arteriolar hyalinosis, and proteinuria were independent predictors of graft loss. In an independent validation set, the molecular risk score was the only predictor of graft loss. Thus, the molecular risk score reflects active injury and is superior to either scarring or function in predicting graft failure