Open Access Te Herenga Waka-Victoria University of Wellington
Browse
stats-05-00036-v2.pdf (593.68 kB)

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

Download (593.68 kB)
journal contribution
posted on 2022-07-07, 14:00 authored by Dianliang Deng, Mashfiqul ChowdhuryMashfiqul Chowdhury
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.

History

Preferred citation

Deng, D. & Chowdhury, M. H. (2022). Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions. Stats, 5(3), 583-605. https://doi.org/10.3390/stats5030036

Journal title

Stats

Volume

5

Issue

3

Publication date

2022-07-02

Pagination

583-605

Publisher

MDPI AG

Publication status

Published online

Online publication date

2022-07-02

ISSN

2571-905X

eISSN

2571-905X

Language

en

Usage metrics

    Journal articles

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC