Comparative evaluation of gene-set analysis methods

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>χ</it>²distribution 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 <it>p53 </it>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.</p> <p>Conclusion</p> <p>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 <it>α</it>-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 <url>http://www.ualberta.ca/~yyasui/homepage.html</url>.</p

Neural and behavioural changes in male periadolescent mice after prolonged nicotine-MDMA treatment

The interaction between MDMA and Nicotine affects multiple brain centres and neurotransmitter systems (serotonin, dopamine and glutamate) involved in motor coordination and cognition. In this study, we have elucidated the effect of prolonged (10 days) MDMA, Nicotine and a combined Nicotine-MDMA treatment on motor-cognitive neural functions. In addition, we have shown the correlation between the observed behavioural change and neural structural changes induced by these treatments in BALB/c mice.We observed that MDMA (2 mg/Kg body weight; subcutaneous) induced a decline in motor function, while Nicotine (2 mg/Kg body weight; subcutaneous) improved motor function in male periadolescent mice. In combined treatment, Nicotine reduced the motor function decline observed in MDMA treatment, thus no significant change in motor function for the combined treatment versus the control. Nicotine or MDMA treatment reduced memory function and altered hippocampal structure. Similarly, a combined Nicotine-MDMA treatment reduced memory function when compared with the control. Ultimately, the metabolic and structural changes in these neural systems were seen to vary for the various forms of treatment. It is noteworthy to mention that a combined treatment increased the rate of lipid peroxidation in brain tissue

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

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 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

Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance

Human Resources’ purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees’ perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. Human Resource employees no longer need to manually handle vast amounts of data with the advent of data mining. Data mining’s primary goal is to uncover information hidden in data patterns and trends in order to produce results that are close to ideal. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using Principal Component Analysis. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best in the prediction of employee performance

Predicting swimming performance using state anxiety

Competitive state anxiety is a common response to stressful competitive sports situations that could affect athletic performance. The effects of state anxiety on swimming performance need further inquiry. The aim of the study was to determine the component of state anxiety that best predicts swimming performance. A quantitative, cross-sectional study design that made use of the Competitive State Anxiety Inventory-2 to measure precompetitive state anxiety was used. A total of 61 male high school swimmers whose age ranged between 14 and 19 years (M = 16.16, standard deviation = 1.66 years) completed the Competitive State Anxiety Inventory-2 1 hr before competing in a 50-m individual swimming event. Performance was evaluated using finishing position. Due to the relatively short duration of the 50-m event, the available literature would suggest that Somatic Anxiety would have a greater effect on Performance - there is not enough time to allow cognitive anxiety to have a detrimental impact on performance. Thus, it was hypothesized that somatic rather than cognitive anxiety will best predict swimming performance. It emerged that both cognitive (b =.787; p <.001) and somatic anxieties (b =.840; p <.001) can independently predict swimming performance. However, when both cognitive and somatic anxieties were regressed onto swimming performance, somatic anxiety partially dominated cognitive anxiety (b =.626; p <.001) and became the significant predictor of swimming performance. It is recommended that swimmers and swimming coaches make use of specific intervention strategies that eradicate the detrimental effects of somatic anxiety immediately before competition.IS

Association between multimorbidity and mortality in a cohort of patients admitted to hospital with COVID-19 in Scotland

Funding: BREATHE - The Health Data Research Hub for Respiratory Health, which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK (MC_PC_19004); CSO Rapid Research in Covid-19 Programme (COV/SAN/20/06); HDR UK Measuring and Understanding Multi-morbidity using Routine Data in the UK (MurMuRUK) (HDR-9006-9006; CFC0110); Medical Research Council (MR/R008345/1).Objectives We investigated the association between multimorbidity among patients hospitalised with COVID-19 and their subsequent risk of mortality. We also explored the interaction between the presence of multimorbidity and the requirement for an individual to shield due to the presence of specific conditions and its association with mortality. Design We created a cohort of patients hospitalised in Scotland due to COVID-19 during the first wave (between 28 February 2020 and 22 September 2020) of the pandemic. We identified the level of multimorbidity for the patient on admission and used logistic regression to analyse the association between multimorbidity and risk of mortality among patients hospitalised with COVID-19. Setting Scotland, UK. Participants Patients hospitalised due to COVID-19. Main outcome measures Mortality as recorded on National Records of Scotland death certificate and being coded for COVID-19 on the death certificate or death within 28 days of a positive COVID-19 test. Results Almost 58% of patients admitted to the hospital due to COVID-19 had multimorbidity. Adjusting for confounding factors of age, sex, social class and presence in the shielding group, multimorbidity was significantly associated with mortality (adjusted odds ratio 1.48, 95%CI 1.26–1.75). The presence of multimorbidity and presence in the shielding patients list were independently associated with mortality but there was no multiplicative effect of having both (adjusted odds ratio 0.91, 95%CI 0.64–1.29). Conclusions Multimorbidity is an independent risk factor of mortality among individuals who were hospitalised due to COVID-19. Individuals with multimorbidity could be prioritised when making preventive policies, for example, by expanding shielding advice to this group and prioritising them for vaccination.Publisher PDFPeer reviewe

Perceived barriers and enablers of physical activity in postpartum women: A qualitative approach

© 2016 Saligheh et al.Background: Postpartum women's recovery from birth can be assisted through increased physical activity (PA). However, women face substantial barriers to participating in exercise and require support to enable them to benefit from increased PA. Methods: This study sought to explore women's beliefs about and experiences of PA and exercise during the 6 weeks to 12 months postpartum period. A cohort of 14 postpartum women from a survey study of the barriers and enablers to exercise participation agreed to take part in interview sessions to provide an in-depth understanding of the women's perceptions of the postpartum period and their physical activity during this time. Results: Findings are presented with reference to the social ecological framework and indicate postpartum women face substantial personal and environmental barriers to PA and exercise participation: fatigue, a lack of motivation and confidence, substantial time constraints, lack of access to affordable and appropriate activities and poor access to public transport. In contrast, enablers such as possessing greater social support, in particular partner support, improved PA and exercise participation. Conclusions: The findings encourage facilitation of exercise through mothers' groups, mothers' exercise clubs or postnatal classes suggesting behavioral and social change is needed. Interaction between individuals, community, organizations and policy makers is required. In addition, the provision of specifically tailored and appropriate exercise programs could potentially enable increased PA in postpartum women, thereby improving their health

Depression and physical activity in a sample of nigerian adolescents: levels, relationships and predictors

<p>Abstract</p> <p>Background</p> <p>Physical inactivity is related to many morbidities but the evidence of its link with depression in adolescents needs further investigation in view of the existing conflicting reports.</p> <p>Methods</p> <p>The data for this cross-sectional study were collected from 1,100 Nigerian adolescents aged 12-17 years. Depressive symptomatology and physical activity were assessed using the Children's Depression Inventory (CDI) and the Physical Activity Questionnaire-Adolescent version (PAQ-A) respectively. Independent t tests, Pearson's Moment Correlation and Multi-level logistic regression analyses for individual and school area influences were carried out on the data at p < 0.05.</p> <p>Results</p> <p>The mean age of the participants was 15.20 ± 1.435 years. The prevalence of mild to moderate depression was 23.8%, definite depression was 5.7% and low physical activity was 53.8%. More severe depressive symptoms were linked with lower levels of physical activity (r = -0.82, p < 0.001) and moderate physical activity was linked with reduced risk of depressive symptoms (OR = 0.42, 95% CI = 0.29-0.71). The odds of having depressive symptoms were higher in older adolescents (OR = 2.16, 95% CI = 1.81-3.44) and in females (OR = 2.92, 95% CI = 1.82-3.54). Females had a higher risk of low physical activity than male adolescents (OR = 2.91, 95% CI = 1.51-4.26). Being in Senior Secondary class three was a significant predictor of depressive symptoms (OR = 3.4, 95% CI = 2.55-4.37) and low physical activity.</p> <p>Conclusions</p> <p>A sizable burden of depression and low physical activity existed among the studied adolescents and these were linked to both individual and school factors. Future studies should examine the effects of physical activity among clinical samples of adolescents with depression.</p