Two-sample tests of high-dimensional means for compositional data
| Publication Date: |
2018-03-06
|
|---|---|
| Publisher: |
Oxford University Press
|
| Print ISSN: |
0006-3444
|
| Electronic ISSN: |
1464-3510
|
| Topics: |
Biology
Mathematics
Medicine
|
| Published by: |
| _version_ | 1836398817155481601 |
|---|---|
| autor | Cao Y, Lin W, Li H. |
| beschreibung | Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease. |
| citation_standardnr | 6180690 |
| datenlieferant | ipn_articles |
| feed_id | 3549 |
| feed_publisher | Oxford University Press |
| feed_publisher_url | http://global.oup.com/ |
| insertion_date | 2018-03-06 |
| journaleissn | 1464-3510 |
| journalissn | 0006-3444 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Oxford University Press |
| quelle | Biometrika |
| relation | https://academic.oup.com/biomet/article/105/1/115/4591648?rss=1 |
| search_space | articles |
| shingle_author_1 | Cao Y, Lin W, Li H. |
| shingle_author_2 | Cao Y, Lin W, Li H. |
| shingle_author_3 | Cao Y, Lin W, Li H. |
| shingle_author_4 | Cao Y, Lin W, Li H. |
| shingle_catch_all_1 | Two-sample tests of high-dimensional means for compositional data Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease. Cao Y, Lin W, Li H. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_2 | Two-sample tests of high-dimensional means for compositional data Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease. Cao Y, Lin W, Li H. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_3 | Two-sample tests of high-dimensional means for compositional data Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease. Cao Y, Lin W, Li H. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_4 | Two-sample tests of high-dimensional means for compositional data Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternatives is investigated. A modified test for paired samples is also considered. Simulations show that the proposed tests can be significantly more powerful than tests that are applied to the raw and log-transformed compositions. The usefulness of our tests is illustrated by applications to gut microbiome composition in obesity and Crohn’s disease. Cao Y, Lin W, Li H. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_title_1 | Two-sample tests of high-dimensional means for compositional data |
| shingle_title_2 | Two-sample tests of high-dimensional means for compositional data |
| shingle_title_3 | Two-sample tests of high-dimensional means for compositional data |
| shingle_title_4 | Two-sample tests of high-dimensional means for compositional data |
| timestamp | 2025-06-30T23:33:06.326Z |
| titel | Two-sample tests of high-dimensional means for compositional data |
| titel_suche | Two-sample tests of high-dimensional means for compositional data |
| topic | W SA-SP WW-YZ |
| uid | ipn_articles_6180690 |