Weighted set enrichment of gene expression data
نویسندگان
چکیده
منابع مشابه
Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles
Gene set enrichment analysis (GSEA) is a statistical method to determine if predefined sets of genes are differentially expressed in different phenotypes. Predefined gene sets may be genes in a known metabolic pathway, located in the same cytogenetic band, sharing the same Gene Ontology category, or any user-defined set. In microarray experiments where no single gene shows statistically signifi...
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Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closel...
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Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and b...
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MOTIVATION Gene Set Enrichment Analysis (GSEA) has been developed recently to capture changes in the expression of pre-defined sets of genes. We propose number of extensions to GSEA, including the use of different statistics to describe the association between genes and phenotypes of interest. We make use of dimension reduction procedures, such as principle component analysis, to identify gene ...
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We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...
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ژورنال
عنوان ژورنال: BMC Systems Biology
سال: 2013
ISSN: 1752-0509
DOI: 10.1186/1752-0509-7-s4-s10