Predicting differences in gene regulatory systems by state space models.
We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with(case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.
Yamaguchi
Rui
R
Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. ruiy@ims.u-tokyo.ac.jp
Imoto
Seiya
S
Yamauchi
Mai
M
Nagasaki
Masao
M
Yoshida
Ryo
R
Shimamura
Teppei
T
Hatanaka
Yosuke
Y
Ueno
Kazuko
K
Higuchi
Tomoyuki
T
Gotoh
Noriko
N
Miyano
Satoru
S
D000970:Antineoplastic Agents / Q000627:therapeutic use; D016022:Case-Control Studies; D004847:Epithelial Cells / Q000187:drug effects / Q000502:physiology; D066246:ErbB Receptors / Q000037:antagonists & inhibitors / Q000502:physiology; D000077156:Gefitinib; D005786:Gene Expression Regulation; D015972:Gene Expression Regulation, Neoplastic; D005809:Genes, Regulator / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics; D011237:Predictive Value of Tests; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use; D011799:Quinazolines / Q000627:therapeutic use; D012142:Respiratory Tract Neoplasms / Q000188:drug therapy
D000970:Antineoplastic Agents; D047428:Protein Kinase Inhibitors; D011799:Quinazolines; D066246:ErbB Receptors; D000077156:Gefitinib