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Full-Text Articles in Microarrays
Statistical Approaches Of Gene Set Analysis With Quantitative Trait Loci For High-Throughput Genomic Studies., Samarendra Das
Statistical Approaches Of Gene Set Analysis With Quantitative Trait Loci For High-Throughput Genomic Studies., Samarendra Das
Electronic Theses and Dissertations
Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on …
The Clustering Of Regression Models Method With Applications In Gene Expression Data, Li-Xuan Qin, Steven G. Self
The Clustering Of Regression Models Method With Applications In Gene Expression Data, Li-Xuan Qin, Steven G. Self
UW Biostatistics Working Paper Series
Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we propose a new model-based clustering method -- the clustering of regression models method, which groups genes that …
Significance Analysis Of Time Course Microarray Experiments, John D. Storey, Wenzhong Xiao, Jeffrey T. Leek, Ronald G. Tompkins, Ron W. Davis
Significance Analysis Of Time Course Microarray Experiments, John D. Storey, Wenzhong Xiao, Jeffrey T. Leek, Ronald G. Tompkins, Ron W. Davis
UW Biostatistics Working Paper Series
Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. Using our method 7409 genes are called significant at a 1% …
Covariate Adjustment In The Analysis Of Microarray Data From Clinical Studies, Debashis Ghosh, Arul Chinnaiyan
Covariate Adjustment In The Analysis Of Microarray Data From Clinical Studies, Debashis Ghosh, Arul Chinnaiyan
The University of Michigan Department of Biostatistics Working Paper Series
There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for followup validation studies. We develop two approaches to the analysis of microarray data in nonrandomized clinical settings. The first is an extension of the current significance analysis of microarray procedures, where other covariates are taken into account. The second is a novel covariate-adjusted regression modelling based on the receiver operating characteristic curve for the analysis of gene expression …
Error Models For Microarray Intensities, Wolfgang Huber, Anja Von Heydebreck, Martin Vingron
Error Models For Microarray Intensities, Wolfgang Huber, Anja Von Heydebreck, Martin Vingron
Bioconductor Project Working Papers
We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation.