omicplotR: visualizing omic datasets as compositions
Abstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-11-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-3174-x |
| _version_ | 1818263258737410048 |
|---|---|
| author | Daniel J. Giguere Jean M. Macklaim Brandon Y. Lieng Gregory B. Gloor |
| author_facet | Daniel J. Giguere Jean M. Macklaim Brandon Y. Lieng Gregory B. Gloor |
| author_sort | Daniel J. Giguere |
| collection | DOAJ |
| description | Abstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. Results omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. Conclusions omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience. |
| first_indexed | 2024-12-12T19:16:10Z |
| format | Article |
| id | doaj.art-da59f7e49b8f4f67a92dbab29eddc18f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T19:16:10Z |
| publishDate | 2019-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-da59f7e49b8f4f67a92dbab29eddc18f2022-12-22T00:14:44ZengBMCBMC Bioinformatics1471-21052019-11-012011510.1186/s12859-019-3174-xomicplotR: visualizing omic datasets as compositionsDaniel J. Giguere0Jean M. Macklaim1Brandon Y. Lieng2Gregory B. Gloor3Department of Biochemistry, Schulich School of Medicine and Dentistry, Western UniversityDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western UniversityDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western UniversityDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western UniversityAbstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. Results omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. Conclusions omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.http://link.springer.com/article/10.1186/s12859-019-3174-xDifferential abundanceData visualizationCompositional dataEffect plotsExploratory data analysisDifferential expression |
| spellingShingle | Daniel J. Giguere Jean M. Macklaim Brandon Y. Lieng Gregory B. Gloor omicplotR: visualizing omic datasets as compositions BMC Bioinformatics Differential abundance Data visualization Compositional data Effect plots Exploratory data analysis Differential expression |
| title | omicplotR: visualizing omic datasets as compositions |
| title_full | omicplotR: visualizing omic datasets as compositions |
| title_fullStr | omicplotR: visualizing omic datasets as compositions |
| title_full_unstemmed | omicplotR: visualizing omic datasets as compositions |
| title_short | omicplotR: visualizing omic datasets as compositions |
| title_sort | omicplotr visualizing omic datasets as compositions |
| topic | Differential abundance Data visualization Compositional data Effect plots Exploratory data analysis Differential expression |
| url | http://link.springer.com/article/10.1186/s12859-019-3174-x |
| work_keys_str_mv | AT danieljgiguere omicplotrvisualizingomicdatasetsascompositions AT jeanmmacklaim omicplotrvisualizingomicdatasetsascompositions AT brandonylieng omicplotrvisualizingomicdatasetsascompositions AT gregorybgloor omicplotrvisualizingomicdatasetsascompositions |