Identifying Differentially Expressed Genes for Time-course Microarray Data through Functional Data Analysis
نویسندگان
چکیده
منابع مشابه
Identifying Differentially Expressed Genes for Time-course Microarray Data through Functional Data Analysis
Identification of differentially expressed (DE) genes across two conditions is a common task with microarray. Most existing approaches accomplish this goal by examining each gene separately based on a model and then control the false discovery rate over all genes. We took a different approach that employs a uniform platform to simultaneously depict the dynamics of the gene trajectories for all ...
متن کاملIdentifying Differentially Expressed Genes in Time Course Microarray Data
Identifying differentially expressed (DE) genes across conditions or treatments is a typical problem in microarray experiments. In time course microarray experiments (under two or more conditions/treatments), it is sometimes of interest to identify two classes of DE genes: those with no time-condition interactions (called parallel DE genes, or PDE), and those with time-condition interactions (n...
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MOTIVATION Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray exp...
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Microarray is a recently developed functional genomic technology that has powerful applications in a wide array of biological research areas, including the medical sciences, agriculture, biotechnology and environmental studies. One of the important problems in the analysis of microarray data is the identification of differentially expressed genes. Commonly used approaches for identifying differ...
متن کاملIdentifying temporally differentially expressed genes through functional principal components analysis.
Time course gene microarray is an important tool to identify genes with differential expressions over time. Traditional analysis of variance (ANOVA) type of longitudinal investigation may not be applicable because of irregular time intervals and possible missingness due to contamination in microarray experiments. Functional principal components analysis is proposed to test hypotheses in the cha...
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ژورنال
عنوان ژورنال: Statistics in Biosciences
سال: 2010
ISSN: 1867-1764,1867-1772
DOI: 10.1007/s12561-010-9024-z