Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes.
The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0219551 |
| _version_ | 1818824550267224064 |
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| author | Han-Ming Liu Dan Yang Zhao-Fa Liu Sheng-Zhou Hu Shen-Hai Yan Xian-Wen He |
| author_facet | Han-Ming Liu Dan Yang Zhao-Fa Liu Sheng-Zhou Hu Shen-Hai Yan Xian-Wen He |
| author_sort | Han-Ming Liu |
| collection | DOAJ |
| description | The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods. |
| first_indexed | 2024-12-18T23:57:39Z |
| format | Article |
| id | doaj.art-6529b37953a04dea93bdc9c0afeb3d8c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T23:57:39Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6529b37953a04dea93bdc9c0afeb3d8c2022-12-21T20:46:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01147e021955110.1371/journal.pone.0219551Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes.Han-Ming LiuDan YangZhao-Fa LiuSheng-Zhou HuShen-Hai YanXian-Wen HeThe hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.https://doi.org/10.1371/journal.pone.0219551 |
| spellingShingle | Han-Ming Liu Dan Yang Zhao-Fa Liu Sheng-Zhou Hu Shen-Hai Yan Xian-Wen He Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. PLoS ONE |
| title | Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. |
| title_full | Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. |
| title_fullStr | Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. |
| title_full_unstemmed | Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. |
| title_short | Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes. |
| title_sort | density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes |
| url | https://doi.org/10.1371/journal.pone.0219551 |
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