DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
References
[1]
Liu, X., Yang, Q., Liu, X., Tao, R. and Rao, W. (2025) A Visco-Hyperelastic Constitutive Model of Hydrogel Considering the Coupling Effect between Segment Motion and Interchain Slippage. JournaloftheMechanicsandPhysicsofSolids, 196, Article ID: 105996. https://doi.org/10.1016/j.jmps.2024.105996
[2]
Ma, H., Geng, Y., Di Luzio, G., Li, G. and Wang, Y. (2025) Autogenous Shrinkage Model for Concrete Accounting for Compounding Effects of Mineral Admixtures. JournalofBuildingEngineering, 99, Article ID: 111503. https://doi.org/10.1016/j.jobe.2024.111503
[3]
Recoules, C., Touvier, M., Pierre, F. and Audebert, M. (2025) Evaluation of the Toxic Effects of Food Additives, Alone or in Mixture, in Four Human Cell Models. FoodandChemicalToxicology, 196, Article ID: 115198. https://doi.org/10.1016/j.fct.2024.115198
[4]
Xie, S., Gan, C. and Lawniczak, A.T. (2024) Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models. Mathematics, 12, Article No. 3768. https://doi.org/10.3390/math12233768
[5]
Zhou, K., Zhang, S., Shang, J. and Lan, X. (2025) Exploring Immune Gene Expression and Potential Regulatory Mechanisms in Anaplastic Thyroid Carcinoma Using a Combination of Single-Cell and Bulk RNA Sequencing Data. ComputationalBiologyandChemistry, 115, Article ID: 108311. https://doi.org/10.1016/j.compbiolchem.2024.108311
[6]
Duan, J., Jiang, R., Shen, H., Xu, X. and Sun, D. (2024) Analysis of Nitrogen Metabolism-Related Gene Expression in Hepatocellular Carcinoma to Establish Relevant Indicators for Prediction of Prognosis and Guidance of Immunotherapy. ComputerMethodsinBiomechanicsandBiomedicalEngineering. https://doi.org/10.1080/10255842.2024.2438922
[7]
Padala, S.K., Swamy, A.K. and Bhattacharjee, B. (2025) Air Temperature Prediction Models for Pavements Based on the Gene Expression Programming Approach. JournalofTransportationEngineering, PartB: Pavements, 151, No. 3. https://doi.org/10.1061/jpeodx.pveng-1496
[8]
Schunck, F., Kodritsch, B., Krauss, M., Busch, W. and Focks, A. (2024) Integrating Time-Resolved nrf2 Gene-Expression Data into a Full GUTS Model as a Proxy for Toxicodynamic Damage in Zebrafish Embryo. EnvironmentalScience&Technology, 58, 21942-21953. https://doi.org/10.1021/acs.est.4c06267
[9]
Qin, X., Zhang, S., Dong, X., Luo, T., Shi, H. and Yuan, L. (2024) An Improved Conditional Relevance and Weighted Redundancy Feature Selection Method for Gene Expression Data. TheJournalofSupercomputing, 81, Article No. 238. https://doi.org/10.1007/s11227-024-06714-5
[10]
Zhang, Y., Zhang, W., Cao, W.J., et al. (2015) Mixed Effects Model Analysis of Tumor Expression Profile Gene Chip Data. Advancesin Modern Biomedical Science, No. 3, 5.
[11]
Zhu, B., Wu, Y., Qi, X., et al. (2014) Genome Wide Association Analysis of QTL-MAS2011 Public Dataset Using Mixed Linear Model and BayesCPi. JournalofAnimalHusbandryandVeterinaryMedicine, 45, 692-698.
[12]
Wang, Y.T. (2016) Tissue Specificity Analysis of APA Loci Based on Information Entropy and Generalized Linear Mixed Effects Model.
[13]
Zhang, Y. and Zhai, S.D. (2004) Research Progress of Nonlinear Mixed Effects Model Method in Population Pharmacokinetics and Pharmacodynamics. InternationalJournalofPharmaceuticalResearch, 31, 236-240.
[14]
Fu, L.Y. (2013) Analysis of Parameter Estimation Methods for Nonlinear Mixed Effects Models. Scientia Silvae Sinica, 49, 78-86.
[15]
Qian, W.M. and Wang, J. (2003) Non Parametric Estimation of Random Effect Density in Linear Mixed Effects Models. Proceedingsofthe 11thAnnualConferenceoftheChineseSocietyforFieldStatistics, Part 1.
