Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization
Abstract Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome fea...
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-021-04232-2 |
| _version_ | 1818450434719744000 |
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| author | Emily Goren Chong Wang Zhulin He Amy M. Sheflin Dawn Chiniquy Jessica E. Prenni Susannah Tringe Daniel P. Schachtman Peng Liu |
| author_facet | Emily Goren Chong Wang Zhulin He Amy M. Sheflin Dawn Chiniquy Jessica E. Prenni Susannah Tringe Daniel P. Schachtman Peng Liu |
| author_sort | Emily Goren |
| collection | DOAJ |
| description | Abstract Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. Results In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. Conclusions Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable. |
| first_indexed | 2024-12-14T20:51:15Z |
| format | Article |
| id | doaj.art-e243c094e8064288a1e698ae9d2e4be3 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T20:51:15Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-e243c094e8064288a1e698ae9d2e4be32022-12-21T22:47:48ZengBMCBMC Bioinformatics1471-21052021-07-0122111910.1186/s12859-021-04232-2Feature selection and causal analysis for microbiome studies in the presence of confounding using standardizationEmily Goren0Chong Wang1Zhulin He2Amy M. Sheflin3Dawn Chiniquy4Jessica E. Prenni5Susannah Tringe6Daniel P. Schachtman7Peng Liu8Department of Statistics, Iowa State UniversityDepartment of Statistics, Iowa State UniversityDepartment of Statistics, Iowa State UniversityDepartment of Horticulture and Landscape Architecture, Colorado State UniversityDepartment of Energy, Joint Genome InstituteDepartment of Horticulture and Landscape Architecture, Colorado State UniversityDepartment of Energy, Joint Genome InstituteDepartment of Agronomy and Horticulture, University of NebraskaDepartment of Statistics, Iowa State UniversityAbstract Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. Results In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. Conclusions Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.https://doi.org/10.1186/s12859-021-04232-2High-dimensional feature selectionMicrobiome analysisNext-generation sequencingStandardizationCausal inference |
| spellingShingle | Emily Goren Chong Wang Zhulin He Amy M. Sheflin Dawn Chiniquy Jessica E. Prenni Susannah Tringe Daniel P. Schachtman Peng Liu Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization BMC Bioinformatics High-dimensional feature selection Microbiome analysis Next-generation sequencing Standardization Causal inference |
| title | Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| title_full | Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| title_fullStr | Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| title_full_unstemmed | Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| title_short | Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| title_sort | feature selection and causal analysis for microbiome studies in the presence of confounding using standardization |
| topic | High-dimensional feature selection Microbiome analysis Next-generation sequencing Standardization Causal inference |
| url | https://doi.org/10.1186/s12859-021-04232-2 |
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