Using simple statistical model to identify differentially expressed genes in microarray experiments

作者：Wu Jia Nan; Zhou Chun Guang; Xia Xue Fei; Liu Gui Xia^*; Shen Wei; Zhou You

来源：Journal of Jilin University (Engineering and Technology Edition), 2013, 43(4): 1059-1063.

DOI：10.7964/jdxbgxb201304034

• Data preprocessing
• Differential expressions
• Differentially expressed gene
• Experiment condition
• Identification of differentially expressed genes
• Microarray data
• Microarray experiments
• Rank

One of the main objectives in the analysis of microarray data is the identification of Differentially Expressed Genes (DEGs) under different experiment conditions. A main approach for such analysis is to calculate a statistical value for each gene, and then rank the genes in accordance with their statistical values. A large ranking value is evidence of a differential expression. Inevitably, different methods generally produce different gene rankings, and the performance of each method depends on its evaluation metric, the dataset and data preprocessing method. A disadvantage shared by existing methods is that some top ranked genes, which are falsely detected as DEGs, tend to exhibit lower expression levels. Here, we present a novel technique named Matrix Rank Product (MRP) for identifying differentially expressed genes that originate from a simple statistical rank model. The algorithm can directly deal with the raw data of the microarray. As a result it can eliminate the interference of different data preprocessing methods. Meanwhile, the new technique is designed for accurate gene ranking by calculating the microarray data matrix of overall sorting.

• 出版日期2013