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A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data

Author

Listed:
  • Víctor Leiva

    (Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Jimmy Corzo

    (Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Myrian E. Vergara

    (Escuela de Ciencias Básicas y Aplicadas, Universidad de La Salle, Bogotá 110231, Colombia)

  • Raydonal Ospina

    (Departamento de Estatística, LInCa, Universidade Federal da Bahia, Salvador 40170-110, Brazil
    Departamento de Estatística, CASTLab, Universidade Federal da Pernambuco, Recife 50670-901, Brazil)

  • Cecilia Castro

    (Centre of Mathematics, Universidade do Minho, 4710-057 Braga, Portugal)

Abstract

This study evaluates the symmetry of data distributions after normalization, focusing on various statistical tests, including a few explored test named Rp. We apply normalization techniques, such as variance stabilizing transformations, to ribonucleic acid sequencing data with varying sample sizes to assess their effectiveness in achieving symmetric data distributions. Our findings reveal that while normalization generally induces symmetry, some samples retain asymmetric distributions, challenging the conventional assumption of post-normalization symmetry. The Rp test, in particular, shows superior performance when there are variations in sample size and data distribution, making it a preferred tool for assessing symmetry when applied to genomic data. This finding underscores the importance of validating symmetry assumptions during data normalization, especially in genomic data, as overlooked asymmetries can lead to potential inaccuracies in downstream analyses. We analyze postmortem lateral temporal lobe samples to explore normal aging and Alzheimer’s disease, highlighting the critical role of symmetry testing in the accurate interpretation of genomic data.

Suggested Citation

  • Víctor Leiva & Jimmy Corzo & Myrian E. Vergara & Raydonal Ospina & Cecilia Castro, 2024. "A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data," Stats, MDPI, vol. 7(3), pages 1-17, September.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:59-983:d:1474107
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    References listed on IDEAS

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    1. Jiayu Huang & Jie Yang & Zhangrong Gu & Wei Zhu & Song Wu, 2021. "A Constrained Generalized Functional Linear Model for Multi-Loci Genetic Mapping," Stats, MDPI, vol. 4(3), pages 1-28, June.
    2. Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    3. Antonietta Mira, 1999. "Distribution-free test for symmetry based on Bonferroni's measure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 959-972.
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