Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
نویسندگان
چکیده
منابع مشابه
Robust 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 کاملRobust QTL analysis by minimum β - divergence method
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 usin...
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 کاملSignificance Analysis of Microarrays using Rank Scores
The Significance Analysis of Microarrays (SAM) software is a very practical tool for detecting significantly expressed genes and controlling the proportion of falsely detected genes, the False Discovery Rate (FDR). However, SAM tends to find biased estimates of the FDR. We show that the same method with the data replaced by rank scores does not have this tendency. We discuss the choice of the r...
متن کاملA Novel Minimum Divergence Approach to Robust Speaker Identification
In this work, a novel solution to the speaker identification problem is proposed through minimization of statistical divergences between the probability distribution (g) of feature vectors from the test utterance and the probability distributions of the feature vector corresponding to the speaker classes. This approach is made more robust to the presence of outliers, through the use of suitably...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BioMed Research International
سال: 2017
ISSN: 2314-6133,2314-6141
DOI: 10.1155/2017/5310198