A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data
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 t...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/2571-905X/7/3/59 |
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| author | Víctor Leiva Jimmy Corzo Myrian E. Vergara Raydonal Ospina Cecilia Castro |
| author_facet | Víctor Leiva Jimmy Corzo Myrian E. Vergara Raydonal Ospina Cecilia Castro |
| author_sort | Víctor Leiva |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-88f5e3418acf44cdaaf22b7af2fc02cb |
| institution | OA Journals |
| issn | 2571-905X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Stats |
| spelling | doaj-art-88f5e3418acf44cdaaf22b7af2fc02cb2025-08-20T01:55:52ZengMDPI AGStats2571-905X2024-09-017396798310.3390/stats7030059A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic DataVíctor Leiva0Jimmy Corzo1Myrian E. Vergara2Raydonal Ospina3Cecilia Castro4Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileDepartamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, ColombiaEscuela de Ciencias Básicas y Aplicadas, Universidad de La Salle, Bogotá 110231, ColombiaDepartamento de Estatística, LInCa, Universidade Federal da Bahia, Salvador 40170-110, BrazilCentre of Mathematics, Universidade do Minho, 4710-057 Braga, PortugalThis 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.https://www.mdpi.com/2571-905X/7/3/59differential gene expressiongenomic data normalizationRNA sequencingRp teststatistical testssymmetry assessment |
| spellingShingle | Víctor Leiva Jimmy Corzo Myrian E. Vergara Raydonal Ospina Cecilia Castro A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data Stats differential gene expression genomic data normalization RNA sequencing Rp test statistical tests symmetry assessment |
| title | A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data |
| title_full | A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data |
| title_fullStr | A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data |
| title_full_unstemmed | A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data |
| title_short | A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data |
| title_sort | statistical methodology for evaluating asymmetry after normalization with application to genomic data |
| topic | differential gene expression genomic data normalization RNA sequencing Rp test statistical tests symmetry assessment |
| url | https://www.mdpi.com/2571-905X/7/3/59 |
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