Identifying differentially expressed genes in microarray experiments with model-based variance estimation

2006 ◽  
Vol 54 (6) ◽  
pp. 2418-2426 ◽  
Author(s):  
Xiaodong Cai ◽  
G.B. Giannakis
Keyword(s):  
Differentially Expressed Genes ◽  
Variance Estimation ◽  
Differentially Expressed ◽  
Microarray Experiments ◽  
Model Based

An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data

2017 ◽  
Vol 15 (05) ◽  
pp. 1750020 ◽  
Author(s):  
Na You ◽  
Xueqin Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Multiple Testing ◽  
Empirical Bayes ◽  
Variance Estimation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Link Function ◽  
Multiple Testing Procedure

The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.

scholarly journals A New Test Statistic Based on Shrunken Sample Variance for Identifying Differentially Expressed Genes in Small Microarray Experiments

2008 ◽  
Vol 2 ◽  
pp. BBI.S473 ◽  
Author(s):  
Akihiro Hirakawa ◽  
Yasunori Sato ◽  
Chikuma Hamada ◽  
Isao Yoshimura
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Simulation Studies ◽  
Test Statistic ◽  
Microarray Experiments ◽  
Cancer Data ◽  
False Discovery ◽  
The Mean ◽  
Sample Variances ◽  
Better Than

Choosing an appropriate statistic and precisely evaluating the false discovery rate (FDR) are both essential for devising an effective method for identifying differentially expressed genes in microarray data. The t-type score proposed by Pan et al. (2003) succeeded in suppressing false positives by controlling the underestimation of variance but left the overestimation uncontrolled. For controlling the overestimation, we devised a new test statistic (variance stabilized t-type score) by placing shrunken sample variances of the James-Stein type in the denominator of the t-type score. Since the relative superiority of the mean and median FDRs was unclear in the widely adopted Significance Analysis of Microarrays (SAM), we conducted simulation studies to examine the performance of the variance stabilized t-type score and the characteristics of the two FDRs. The variance stabilized t-type score was generally better than or at least as good as the t-type score, irrespective of the sample size and proportion of differentially expressed genes. In terms of accuracy, the median FDR was superior to the mean FDR when the proportion of differentially expressed genes was large. The variance stabilized t-type score with the median FDR was applied to actual colorectal cancer data and yielded a reasonable result.

scholarly journals A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0149086 ◽  
Author(s):  
Samuel Sunghwan Cho ◽  
Yongkang Kim ◽  
Joon Yoon ◽  
Minseok Seo ◽  
Su-kyung Shin ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Model Based ◽  
Joint Identification

Grouping Rank Product Meta-Analysis Method for Identifying Differentially Expressed Genes in Microarray Experiments

2010 ◽  
Vol 44-47 ◽  
pp. 905-909
Author(s):  
Yuan Tian ◽  
Gui Xia Liu ◽  
Chun Guang Zhou
Keyword(s):  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Analysis Approach ◽  
Analysis Method ◽  
Experimental Conditions ◽  
Microarray Experiments ◽  
Rank Product ◽  
Product Approach ◽  
Improved Model

One of the main purposes in analysis of microarray experiments is to identify differentially expressed genes under two experimental conditions. The Meta-analysis method, rank product meta-analysis approach, considered a powerful tool for identification of differentially expressed genes. However, rank product meta-analysis approach used the each dataset in the computation of the fold changes, which leaded to less computational efficiency. Here we modified the rank product meta-analysis approach to obtain an improved model for identifying different gene expression. The new model, grouping rank product approach, adds competitive classification of samples to group datasets before the computation of the fold changes. We used the grouping rank product approach on two simulated datasets and two breast datasets and showed that the grouping rank product approach is not only as accurate as the rank product meta-analysis approach, but also more computational efficient in identifying differentially expressed genes.

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