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

• Published in: Functional & integrative genomics Vol. 9; no. 1; pp. 59 - 66
• Main Authors: Yang, Jian, Zou, Yangyun, Zhu, Jun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.02.2009; Springer-Verlag; Springer; Springer Nature B.V
• Subjects: Animal Genetics and Genomics; Animals; Biochemistry; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Brain; Brain - metabolism; Cell Biology; Cluster Analysis; Databases, Genetic; Differentially expressed gene (DEG); Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Leukemic; General aspects; Genomics; Humans; Leukemia; Leukemia - classification; Leukemia - genetics; Life Sciences; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Mice; Mice, Inbred Strains; Microarray; Microbial Genetics and Genomics; Oligonucleotide Array Sequence Analysis; Organ Specificity - genetics; Original Paper; Plant Genetics and Genomics; Rodents; Software; Statistical methods; Two-treatment factors; Microarray; Two-treatment factors; Differentially expressed gene (DEG); Human; Brain; Acute; Leukemia; Rodentia; Encephalon; Vertebrata; Mammalia; Treatment; Gene; Mouse
• ISSN: 1438-793X; 1438-7948
• DOI: 10.1007/s10142-008-0096-5

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.