Evaluation of Methods for Gene Selection in Melanoma Cell Lines

A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome (G2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis

Evaluation of methods for gene selection in melanoma cell lines

A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome (G2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis

A Copula-based approach to differential gene expression analysis

Thesis submitted in total fulfillment of the requirements for the degree of Doctor of Philosophy in Biostatistics at Strathmore UniversityMicroarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. The main objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. In this thesis, we develop and evaluate a semi-parametric copula-based algorithm for gene selection that does not depend on the distributions of the covariates, except that their marginal distributions are continuous. A comparison of the developed method with the existing methods is done based on power to identify differentially expressed genes (DEGs) and control of Type I error rate via a simulation study. Simulations indicate that the copula-based model has a reasonable power in selecting differentially expressed gene and has a good control of Type I error rate. These results are validated in a publicly available melanoma dataset. The copula-based approach turns out to be useful in finding genes that are clinically important. Relaxing parametric assumptions on microarray data may yield procedures that have good power for differential gene expression analysis

A copula-based approach to differential gene expression analysis

Conference paper presented in International Biometric Conference 2014Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant.Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant

Water Filter Provision and Home-Based Filter Reinforcement Reduce Diarrhea in Kenyan HIV-Infected Adults and Their Household Members

Among human immunodeficiency virus (HIV) -infected adults and children in Africa, diarrheal disease remains a major cause of morbidity and mortality. We evaluated the effectiveness of provision and home-based reinforcement of a point-of-use water filtration device to reduce diarrhea among 361 HIV-infected adults in western Kenya by comparing prevalence of self-reported diarrhea before and after these interventions. After provision of the filter, 8.7% of participants reported diarrhea compared with 17.2% in the 3 months before filter provision (odds ratio [OR] = 0.39, 95% confidence interval [95% CI] = 0.23–0.66, P < 0.001). The association was similar among 231 participants who were already taking daily cotrimoxazole prophylaxis before being given a filter (OR = 0.47, 95% CI = 0.25–0.88, P = 0.019). Educational reinforcement was also associated with a modest reduction in self-reported diarrhea (OR = 0.50, 95% CI = 0.20–0.99, P = 0.047). Provision and reinforcement of water filters may confer significant benefit in reducing diarrhea among HIV-infected persons, even when cotrimoxazole prophylaxis is already being used

Field evaluation of BD FACSPrest for haemoglobin and CD4 measurement

Background:Measurements of CD4 and haemoglobin are used to determine the immunological state and information about disease progression for HIV-infected patients. Use of BD FACS Presto™ point of care (POC) device for CD4 and haemoglobin (Hb) determination can significantly improve access, uptake and coverage of laboratory services and hence management of HIV-infected patients in resource-limited settings. This study evaluated the relative bias in CD4 and Hb measurements using BD FACSPresto™ system compared to BD FACSCalibur™ CD4 analyser and Mindray BC-5380 haematology analyser respectively based on venous and capillary blood samples in a clinical hospital setting. Methods:Venous and capillary blood samples were used to determine CD4 counts and Hb levels among HIV-1 infected patients. The samples were analysed on the BD FACSPresto™ and results compared against BD FACSCalibur™ and Mindray BC-5380 for CD4 and haematology analyser respectively. Results: Results for absolute CD4 counts in both venous and capillary blood showed a high correlation (R2 = 0.922, P< 0.001) when they were analysed on BD FACSPresto™ and BD FACSCalibur™ machines. Overall, the mean difference in absolute CD4 count was 77.16 cells/mL (95%CI: 49.89, 104.42, p<0.01) when analysed on two platforms. The BD FACSCalibur™ gave a higher mean of absolute CD4 count (834.38 cells/ml) compared to BD FACSPresto™ (757.23 cells/ml) when venous sample type is used. There was a significant mean difference of Hb levels at 0.31 (P <0.001) between the two sample types when analysed on BD FACSPresto™ and Mindray BC-5380 haematology analyser. In addition, there was a high correlation (R2 = 0.920, P< 0.001) of Hb level measurements between the BD FACSPresto™ and Mindray BC-5380 haematology analyser.Conclusion: The BD FACSPresto performed satisfactorily in comparison to the conventional reference standard technologies. Venous and capillary blood sample types showed a high correlation when analysed for absolute CD4 count and Hb using BD FACSPresto™, BD FACSCalibur™ and Mindray BC-5380 haematology analyser. BD FACSPresto capillary platform can be used interchangeably with BD FACSCalibur™ venous platform for CD4 and Mindray BC-5380 for Hb measurement in resource limited settings to increase access and uptake of laboratory services

A Comparison of Parametric and Semi-Parametric Models for Microarray Data Analysis

Microarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. Parametric, nonparametric and semi-parametric statistical methods have been proposed for gene selection within the last sixteen years. In an effort to find the “gold standard", the performance of some common parametric and nonparametric methods have been compared in terms of power to select differentially expressed genes and other desirable properties. However, no such comparisons have been conducted between parametric and semi-parametric models. In this study, we compared a semi-parametric model based on copulas with a parametric model (the quantitative trait analysis or QTA model) in terms of power and the ability to control the Type I error rate. In addition, we proposed a simple algorithm for choosing an optimal copula. The two approaches were applied to a publicly available melanoma cell lines dataset for validation. Both methods performed well in terms of power but the copula approach was notably the better. In terms of the Type I error rate control, the two methods were comparable. More methods for selecting an optimal copula for gene expression data need to be developed, as the proposed procedure is limited to copulas that permit both negative and positive dependence only

Using copulas to select prognostic genes in melanoma patients

Melanoma of the skin is the fifth and seventh most commonly diagnosed carcinoma in men and women, respectively, in the USA. So far, gene signatures prognostic for overall and distant metastasis-free survival, for example, have been promising in the identification of therapeutic targets for primary and metastatic melanoma. But most of these gene signatures have been selected using statistics that depend entirely on the parametric distributions of the data (e.g. t-statistics). In this study, we assessed the impact of relaxing the parametric assumptions on the power of the models used for gene selection. We developed a semi-parametric model for feature selection that does not depend on the distributions of the covariates. This copula-based model only assumed that the marginal distributions of the covariates are continuous. Simulations indicated that the copula-based model had reasonable power at various levels of the false discovery rate (FDR). These results were validated in a publicly-available melanoma dataset. Relaxing parametric assumptions on microarray data may yield procedures that have good power for differential gene expression analysis

A Comparison of Parametric and Semi-Parametric Models for Microarray Data Analysis

Microarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. Parametric, nonparametric and semi-parametric statistical methods have been proposed for gene selection within the last sixteen years. In an effort to find the “gold standard", the performance of some common parametric and nonparametric methods have been compared in terms of power to select differentially expressed genes and other desirable properties. However, no such comparisons have been conducted between parametric and semi-parametric models. In this study, we compared a semi-parametric model based on copulas with a parametric model (the quantitative trait analysis or QTA model) in terms of power and the ability to control the Type I error rate. In addition, we proposed a simple algorithm for choosing an optimal copula. The two approaches were applied to a publicly available melanoma cell lines dataset for validation. Both methods performed well in terms of power but the copula approach was notably the better. In terms of the Type I error rate control, the two methods were comparable. More methods for selecting an optimal copula for gene expression data need to be developed, as the proposed procedure is limited to copulas that permit both negative and positive dependence only

Analysis of Recurrent Events with Associated Informative Censoring: Application to HIV Data

In this study, we adapt a Cox-based model for recurrent events; the Prentice, Williams and Peterson Total -Time (PWP-TT) that has largely, been used under the assumption of non-informative censoring and evaluate it under an informative censoring setting. Empirical evaluation was undertaken with the aid of the semi-parametric framework for recurrent events suggested by Huang [1] and implemented in R Studio software. For validation we used data from a typical HIV care setting in Kenya. Of the three models under consideration; the standard Cox Model had gender hazard ratio (HR) of 0.66 (p-value=0.165), Andersen-Gill had HR 0.46 (with borderline p-value=0.054) and extended PWP TT had HR 0.22 (p-value=0.006). The PWP-TT model performed better as compared to other models under informative setting. In terms of risk factors under informative setting, LTFU due to stigma; gender [base=Male] had HR 0.544 (p-value =0.002), age [base is &lt; 37] had HR 0.772 (p-value=0.008), ART regimen [base= First line] had HR 0.518 (p-value= 0.233) and differentiated care model (Base=not on DCM) had HR 0.77(p-value=0.036). In conclusion, in spite of the multiple interventions designed to address incidences of LTFU among HIV patients, within-person cases of LTFU are usually common and recurrent in nature, with the present likelihood of a person getting LTFU influenced by previous occurrences and therefore informative censoring should be checked