Sample Size for Identifying Differentially Expressed Genes in Microarray Experiments

2004 ◽  
Vol 11 (4) ◽  
pp. 714-726 ◽  
Author(s):  
Sue-Jane Wang ◽  
James J. Chen
Keyword(s):  
Sample Size ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Microarray Experiments

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.

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.

scholarly journals Statistical tests for identifying differentially expressed genes in time-course microarray experiments

2003 ◽  
Vol 19 (6) ◽  
pp. 694-703 ◽  
Author(s):  
T. Park ◽  
S.-G. Yi ◽  
S. Lee ◽  
S. Y. Lee ◽  
D.-H. Yoo ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Statistical Tests ◽  
Differentially Expressed ◽  
Microarray Experiments
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