Dataset Information

Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.

ABSTRACT: Early detection (localized stage) of colon cancer is associated with a five-year survival rate of 91%. Only 39% of colon cancers, however, are diagnosed at that early stage. Early and accurate diagnosis, therefore, constitutes a critical need and a decisive factor in the clinical treatment of colon cancer and its success. In this study, using supervised linear discriminant analysis, we have developed three diagnostic biomarker models that-based on global micro-RNA expression analysis of colonic tissue collected during surgery-can discriminate with a perfect accuracy between subjects with colon cancer (stages II-IV) and normal healthy subjects. We developed our three diagnostic biomarker models with 57 subjects [40 with colon cancer (stages II-IV) and 17 normal], and we validated them with 39 unknown (new and different) subjects [28 with colon cancer (stages II-IV) and 11 normal]. For all three diagnostic models, both the overall sensitivity and specificity were 100%. The nine most significant micro-RNAs identified, which comprise the input variables to the three linear discriminant functions, are associated with genes that regulate oncogenesis, and they play a paramount role in the development of colon cancer, as evidenced in the tumor tissue itself. This could have a significant impact in the fight against this disease, in that it may lead to the development of an early serum or blood diagnostic test based on the detection of those nine key micro-RNAs.

SUBMITTER: Nikas JB

PROVIDER: S-EPMC3256938 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.

Nikas Jason B JB Low Walter C WC

Cancer informatics 20111220

Early detection (localized stage) of colon cancer is associated with a five-year survival rate of 91%. Only 39% of colon cancers, however, are diagnosed at that early stage. Early and accurate diagnosis, therefore, constitutes a critical need and a decisive factor in the clinical treatment of colon cancer and its success. In this study, using supervised linear discriminant analysis, we have developed three diagnostic biomarker models that-based on global micro-RNA expression analysis of colonic ...[more]

PMID: 22259227

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Project description:BackgroundRNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated.ResultsIn this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications.ConclusionsWe have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data. R code is available at http://www.comp.hkbu.edu.hk/~xwan/NBLDA.R or https://github.com/yangchadam/NBLDA.

Dataset Information

Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.

Publications

Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.

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