Identifying and Assessing Interesting Subgroups in a Heterogeneous Population.
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 unrecogn...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 14 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/3/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109030987&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109030987 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/3/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109030987 109030987 109030987 10.1155/2015/462549 109030987 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Identifying and Assessing Interesting Subgroups in a Heterogeneous Population. aug: au: Lee, Woojoo Alexeyenko, Andrey Pernemalm, Maria Guegan, Justine Dessen, Philippe Lazar, Vladimir Lehtiö, Janne Pawitan, Yudi affil: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden sug: subj: Population Research Measurement Cluster Analysis Descriptive Statistics Data Analysis Software Mathematics Validity Data Analysis Lung Neoplasms Funding Source Human ab: 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 k-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. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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