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Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions

Author

Listed:
  • Dianliang Deng

    (Department of Mathematics and Statistics, University of Regina, Regina, SK S4S 0A2, Canada
    These authors contributed equally to this work.)

  • Mashfiqul Huq Chowdhury

    (Department of Statistics, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
    These authors contributed equally to this work.)

Abstract

Temporal gene expression data contain ample information to characterize gene function and are now widely used in bio-medical research. A dense temporal gene expression usually shows various patterns in expression levels under different biological conditions. The existing literature investigates the gene trajectory using the mean function. However, temporal gene expression curves usually show a strong degree of heterogeneity under multiple conditions. As a result, rates of change for gene expressions may be different in non-central locations and a mean function model may not capture the non-central location of the gene expression distribution. Further, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical when analyzing gene expression data. In this research, a linear quantile mixed model is used to find the trajectory of gene expression data. This method enables the changes in gene expression over time to be studied by estimating a family of quantile functions. A statistical test is proposed to test the similarity between two different gene expressions based on estimated parameters using a quantile model. Then, the performance of the proposed test statistic is examined using extensive simulation studies. Simulation studies demonstrate the good statistical performance of this proposed test statistic and show that this method is robust against normal error assumptions. As an illustration, the proposed method is applied to analyze a dataset of 18 genes in P. aeruginosa , expressed in 24 biological conditions. Furthermore, a minimum Mahalanobis distance is used to find the clustering tree for gene expressions.

Suggested Citation

  • Dianliang Deng & Mashfiqul Huq Chowdhury, 2022. "Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions," Stats, MDPI, vol. 5(3), pages 1-23, July.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:3:p:36-605:d:854374
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    References listed on IDEAS

    as
    1. Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, vol. 1(1), pages 1-14, June.
    2. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.
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