Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel

Yang, Jian, Zou, Yangyun and Zhu, Jun (2009) Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel. Functional & Integrative Genomics, 9 1: 59-66. doi:10.1007/s10142-008-0096-5


Author Yang, Jian
Zou, Yangyun
Zhu, Jun
Title Identifying differentially expressed genes in human acute leukemia and mouse brain microarray datasets utilizing QTModel
Journal name Functional & Integrative Genomics   Check publisher's open access policy
ISSN 1438-793X
1438-7948
Publication date 2009-02-01
Year available 2008
Sub-type Article (original research)
DOI 10.1007/s10142-008-0096-5
Volume 9
Issue 1
Start page 59
End page 66
Total pages 8
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Formatted abstract
One of the essential issues in microarray data analysis is to identify differentially expressed genes (DEGs) under different experimental treatments. In this article, a statistical procedure was proposed to identify the DEGs for gene expression data with or without missing observations from microarray experiment with one- or two-treatment factors. An F statistic based on Henderson method III was constructed to test the significance of differential expression for each gene under different treatment(s) levels. The cutoff P value was adjusted to control the experimental-wise false discovery rate. A human acute leukemia dataset corrected from 38 leukemia patients was reanalyzed by the proposed method. In comparison to the results from significant analysis of microarray (SAM) and microarray analysis of variance (MAANOVA), it was indicated that the proposed method has similar performance with MAANOVA for data with one-treatment factor, but MAANOVA cannot directly handle missing data. In addition, a mouse brain dataset collected from six brain regions of two inbred strains (two-treatment factors) was reanalyzed to identify genes with distinct regional-specific expression patterns. The results showed that the proposed method could identify more distinct regional-specific expression patterns than the previous analysis of the same dataset. Moreover, a computer program was developed and incorporated in the software QTModel, which is freely available at http://ibi.zju.edu.cn/software/qtmodel
Keyword Microarray
Two-treatment factors
Differentially expressed gene (DEG)
Cdna Microarray
Dna Microarrays
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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