Browsing by Author "Omolo, Bernard"
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- ItemA Bayesian hierarchical model for correlation in microarray studiesOmolo, BernardMicroarrays are miniaturised biological devices consisting of molecules (e.g. DNA or protein), called \probes", that are orderly arranged at a microscopic scale onto a solid support such as a nylon membrane or a glass slide.The array elements (probes) bind speci cally to labeled molecules, called "targets", into complex molecular mixtures,thereby generating signals that reveal the identity and the concentration of the interacting labeled cells.Microarray analysis has a broad range of applications that involve di erent types of probes and/or targets (cDNA or oligos)
- ItemA copula-based approach to differential gene expression analysisChaba, Linda Akoth; Odhiambo, John W.; Omolo, BernardMelanoma 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.
- ItemA smooth test of goodness-of-fit for the baseline hazard function in recurrent event modelsOdhiambo, John W.; Odhiambo, Collins; Omolo, BernardIn this paper, we formulate a smooth test of goodness-of-fit for a simple hypothesis about the baseline hazard function in recurrent-event models. The formulation is an extension of Neyman' s goodness-of-fit approach, whose score tests are obtained by embedding the null hypothesis in a larger class of hazard rate functions. Since the application is in recurrent event models , the data is dynamic.A useful feature about this test is the parametric approach that makes inference about the hazard function more efficient. To examine the finite-sample properties of this test, we used simulated data . For validation, we applied the test to a real-life recurrent event data. Results show that the test possesses better power over wide range of alternatives, when compared with similar tests of the chi-square type in the literature.
- ItemAdaptation of a RAS pathway activation signature from FF to FFPE tissues in colorectal cancer(Strathmore University, 2017) Omolo, Bernard; Yang, Mingli; Lo, Fang Yin; Schell, Michael J.; Austin, Sharon; Howard, Kellie; Madan, Anup; Yeatman, Timothy J.Background: The KRAS gene is mutated in about 40 % of colorectal cancer (CRC) cases, which has been clinically validated as a predictive mutational marker of intrinsic resistance to anti-EGFR inhibitor (EGFRi) therapy. Since nearly 60 % of patients with a wild type KRAS fail to respond to EGFRicombination therapies, there is a need to develop more reliable molecular signatures to better predict response. Here we address the challenge of adapting a gene expression signature predictive of RAS pathway activation, created using fresh frozen (FF) tissues, for use with more widely available formalinfixed paraffin-embedded (FFPE) tissues. Methods: In this study, we evaluated the translation of an 18-gene RAS pathway signature score from FF to FFPE in 54 CRC cases, using a head-to- head comparison of five technology platforms. FFPE-based technologies included the Affymetrix Gene Chip (Affy), NanoString nCounter™ (NanoS),Illumina whole genome RNASeq (RNA-Acc), Illumina targeted RNASeq (t-RNA), and Illuminastranded Total RNA-rRNA- depletion (rRNA). Results: Using Affy_FF as the “gold” standard, initial analysis of the 18-gene RAS scores on all 54samples shows varying pairwise Spearman correlations, with (1) Affy_FFPE (r= 0.233, p = 0.090); (2)NanoS_FFPE (r= 0.608, p < 0.0001); (3) RNA-Acc_FFPE (r= 0.175, p = 0.21); (4) t-RNA_FFPE (r=−0.237, p = 0.085); (5) and t-RNA (r= −0.012, p = 0.93). These results suggest that only NanoString has successful FF to FFPE translation. The subsequentremoval of identified “problematic” samples (n= 15) and genes (n= 2) further improves the correlations of Affy_FF with three of the five technologies: Affy_FFPE (r= 0.672, p < 0.0001); NanoS_FFPE (r= 0.738, p < 0.0001); and RNA-Acc_FFPE (r= 0.483, p = 0.002). Conclusions: Of the five technology platforms tested, Nano String technology provides a more faithful translation of the RAS pathway gene expression signature from FF to FFPE than the Affymetrix GeneChip and multiple RNASeq technologies. Moreover, Nano String was the most forgiving technology in the analysis of samples with presumably poor RNA quality. Using this approach, the RAS signature score may now be reasonably applied to FFPE clinical samples.
- ItemAn Integrated RNA and DNA Molecular Signature for Colorectal Cancer Classification(Strathmore University, 2019-08) Mohammed, Mohanad; Mwambi, Henry; Omolo, BernardColorectal cancer (CRC) is the third most common cancer among women and men in the USA. The KRAS gene is mutated in 40% of the CRC cases and hence the RAS pathway activation has become a major focus of drug targeting efforts. However, nearly 60% of patients with wild-type KRAS fail to respond to RAS targeted therapies, for example the anti-epithelial growth factor receptor inhibitor (EGFRi) combination therapies. Thus, there is a need to develop more reliable molecular signatures to better predict mutation status. In this study, we develop a hybrid (DNA mutation and RNA expression) signature and assess its predictive properties for the mutation status of CRC patients. Publicly-available microarray and RNA-Seq data from 54 matched formalin-fixed paraffin embedded (FFPE) samples from the Affymetrix GeneChip and RNA-Seq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. For classification, the support-vector machines, artificial neural networks, random forests, k-nearest neighbors and the nave Bayes algorithms were employed. Compared to the genelist from each of the platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity and AUC for mutation status and could therefore be useful in clinical practice, especially for colorectal cancer diagnosis and therapeutics.
- ItemA Model-based approach to genetic association testing in Malaria studies(Strathmore University, 2019) Akoth, Morine; Odhiambo, John; Omolo, BernardIn human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes, but not in genome-wide association studies (GWAS). For example, heterosis is believed to confer resistance to certain strains of malaria in patients heterozygous for the sickle-cell gene HbS. Yet the power of allele-based tests can be substantially diminished by heterosis. Since GWAS (and haplotype-associations) also utilize allele-based tests, it is unclear to what degree GWAS could underachieve because heterosis is ignored. In this study, we propose a two-step approach to genetic association testing in malaria studies in a GWAS setting that may enhance the power of the tests, by identifying the underlying genetic model first before applying the association tests. We fit generalized linear models for the dominant, recessive, additive and heterotic effects and perform tests of significance using the MAX and the allelic tests, noting the minimum p-values across all the models and the proportion of tests that a given genetic model was deemed the best, using simulated data. Case-control genotype data on malaria from Kenya and the Gambia are used for validation. Results show that the allelic test returned a number of false negatives under the heterosis model, suggesting reduced power in testing genetic association. Thus, GWAS and haplotype associations should be treated with caution, unless the underlying genetic model had been determined.
- ItemSpatiotemporal Bayesian Technique to gap-fill and downscale long-term vegetation index records(Strathmore University, 2017) Okuto, Erick; Omolo, Bernard; Marshall, MichaelLong-term Earth observation based vegetation index records are used extensively to characterize the environmental and ecosystem response to climate variability and change. However, the presence of clouds, and other spectral and radiometric inconsistencies limit their application in related studies. Compositing is typically used to minimize the effect, but inconsistencies persist, necessitating further processing. In this study, we show that a spatiotemporal Bayesian technique is more robust in gap-filling and downscaling the Vegetation Index & Phenology (VIP) lab Normalized Difference Vegetation Index (NDVI) record. VIP-NDVI is available 5 km spatial resolution at 15-day intervals. The technique was developed for an area covering the Central African Republic (CAR) which displays a strong climate-ecological gradient. Its ability to gap-fill was compared against an optimized Savitzky-Golay (SG) filter commonly used to gap-fill Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index records and downscale was compared to the US Geological Survey’s Earth Resources Observation and Science Center expedited MODISbased NDVI product (eMODIS) at 250m resolution available for CAR from 2000-2015. The inter-comparison was via descriptive, correlation and regression analysis. Overall, reconstructed records based on the spatiotemporal Bayesian approach showed a higher level of correlation with good-quality VIP-NDVI data than SG-NDVI. In addition, the downscaled Bayesian-NDVI to 250 m resolution was more comparable to eMODIS NDVI.
- ItemUsing copulas to select prognostic genes in melanoma patients(Strathmore University, 2017) Chaba, Linda; Odhiambo, John; Omolo, BernardMelanoma of the skin is the 5th 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.
- ItemUsing copulas to select prognostic genes in melanoma patients(Life science Global, 2017) Chaba, Linda; Odhiambo, John; Omolo, BernardMelanoma 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.
- ItemValidation of the smooth test of goodness-of-fit for proportional hazards in Cancer survival studies(Strathmore University, 2017) Odhiambo, Collins; Odhiambo, John; Omolo, BernardIn this study, we validate the smooth test of goodness-of-fit for the proportionality of the hazard function in the two-sample problem in cancer survival studies. The smooth test considered here is an extension of Neyman’s smooth test for proportional hazard functions. Simulations are conducted to compare the performance of the smooth test, the data-driven smooth test, the Kolmogorov-Smirnov proportional hazards test and the global test, in terms of power. Eight real cancer datasets from different settings are assessed for the proportional hazard assumption in the Cox proportional hazard models, for validation. The smooth test performed best and is independent of the number of covariates in the Cox proportional hazard models.