Robust QTL analysis by minimum β - divergence method
نویسندگان
چکیده
Robustness has received too little attention in Quantitative Trait Loci (QTL) analysis in experimental crosses. This paper discusses a robust QTL mapping algorithm based on Composite Interval Mapping (CIM) model by minimising β-divergence using the EM like algorithm. We investigate the robustness performance of the proposed method in a comparison of Interval Mapping (IM) and CIM algorithms using both synthetic and real datasets. Experimental results show that the proposed method significantly improves the performance over the traditional IM and CIM methods for QTL analysis in presence of outliers; otherwise, it keeps equal performance.
منابع مشابه
Robust Estimation in Linear Regression Model: the Density Power Divergence Approach
The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data. In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this est...
متن کاملRobust Extraction of Local Structures by the Minimum β-Divergence Method
This paper discusses a new highly robust learning algorithm for exploring local principal component analysis (PCA) structures in which an observed data follow one of several heterogeneous PCA models. The proposed method is formulated by minimizing β-divergence. It searches a local PCA structure based on an initial location of the shifting parameter and a value of the tuning parameter β. If the ...
متن کاملRobustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis
The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression data...
متن کاملRobust Significance Analysis of Microarrays by Minimum β-Divergence Method
Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Theref...
متن کاملExploring Latent Structure of Mixture ICA Models by the Minimum ß-Divergence Method
Independent component analysis (ICA) aims to extract original independent signals (source components) that are linearly mixed in a basic framework. This paper discusses a sequential procedure for hidden class separation in which the observed data follow a mixture of several ICA models. Each class is described by linear combination of independent and non-Gaussian sources. Our proposing method is...
متن کامل