Longitudinal linear combination test for gene set analysis
نویسندگان
چکیده
منابع مشابه
Time-Course Gene Set Analysis for Longitudinal Gene Expression Data
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estima...
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As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. It has been proposed to model RNA-seq counts as continuous variables using nonparametric regression to account for their inherent heteroscedasticity. In this vein, we propose tcgsaseq, a principled, mo...
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MOTIVATION Gene class testing (GCT) or gene set analysis (GSA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Shortcomings of the Fisher's exact test for the overrepresentation analysis are illustrated by an example. Most alternative GSA methods are developed for data collected from two exper...
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distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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MOTIVATION Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion. RESULTS We demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of ...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2019
ISSN: 1471-2105
DOI: 10.1186/s12859-019-3221-7