Modified Significance Analysis of Microarrays in Heterogeneous Diseases
Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/11/2/62 |
| _version_ | 1797409291933581312 |
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| author | I-Shiang Tzeng |
| author_facet | I-Shiang Tzeng |
| author_sort | I-Shiang Tzeng |
| collection | DOAJ |
| description | Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the “half SAM score” can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity. |
| first_indexed | 2024-03-09T04:12:05Z |
| format | Article |
| id | doaj.art-e373117e4a844e28a6c45b2c0d12a49d |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T04:12:05Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-e373117e4a844e28a6c45b2c0d12a49d2023-12-03T13:59:26ZengMDPI AGJournal of Personalized Medicine2075-44262021-01-011126210.3390/jpm11020062Modified Significance Analysis of Microarrays in Heterogeneous DiseasesI-Shiang Tzeng0Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei 231, TaiwanSignificance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the “half SAM score” can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity.https://www.mdpi.com/2075-4426/11/2/62gene expressionheterogeneous datasignificance analysis of microarrays |
| spellingShingle | I-Shiang Tzeng Modified Significance Analysis of Microarrays in Heterogeneous Diseases Journal of Personalized Medicine gene expression heterogeneous data significance analysis of microarrays |
| title | Modified Significance Analysis of Microarrays in Heterogeneous Diseases |
| title_full | Modified Significance Analysis of Microarrays in Heterogeneous Diseases |
| title_fullStr | Modified Significance Analysis of Microarrays in Heterogeneous Diseases |
| title_full_unstemmed | Modified Significance Analysis of Microarrays in Heterogeneous Diseases |
| title_short | Modified Significance Analysis of Microarrays in Heterogeneous Diseases |
| title_sort | modified significance analysis of microarrays in heterogeneous diseases |
| topic | gene expression heterogeneous data significance analysis of microarrays |
| url | https://www.mdpi.com/2075-4426/11/2/62 |
| work_keys_str_mv | AT ishiangtzeng modifiedsignificanceanalysisofmicroarraysinheterogeneousdiseases |