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- 저자 : Pernemalm, Maria (검색결과 2 건)
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Identifying and Assessing Interesting Subgroups in a Heterogeneous Population
Lee, Woojoo,Alexeyenko, Andrey,Pernemalm, Maria,Guegan, Justine,Dessen, Philippe,Lazar, Vladimir,Lehtiö,, Janne,Pawitan, Yudi Hindawi Publishing Corporation 2015 BioMed research international Vol.2015 No.-
<P>Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as <I>k</I>-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided.</P>
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Network enrichment analysis: extension of gene-set enrichment analysis to gene networks
Alexeyenko, Andrey,Lee, Woojoo,Pernemalm, Maria,Guegan, Justin,Dessen, Philippe,Lazar, Vladimir,Lehtiö,, Janne,Pawitan, Yudi BioMed Central 2012 BMC bioinformatics Vol.13 No.-
<P><B>Background</B></P><P>Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.</P><P><B>Results</B></P><P>We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.</P><P><B>Conclusions</B></P><P>The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.</P>
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