ArticleMicrobiome2018
MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies.
Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- MicrobTiSDA: A flexible R package for inferring interspecies interactions and abundance dynamics in microbiome time-series data.iMetaOmics · 2026Article
- Sparse time-varying log-ratios for longitudinal high-throughput sequencing data.Frontiers in bioinformatics · 2026Article
- LorDist: a novel method for calculating the distance based on functional data analysis with application to longitudinal microbial data.Microbiology spectrum · 2025Article
- Postbiotics from Saccharomyces cerevisiae fermentation stabilize microbiota in rumen liquid digesta during grain-based subacute ruminal acidosis (SARA) in lactating dairy cows.Journal of animal science and biotechnology · 2024Article
- Postbiotics fromFrontiers in microbiology · 2024Article
- Dynamic lipidome alterations associated with human health, disease and ageing.Nature metabolism · 2023Article
- Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data.Genes · 2023Article
- Experimental bacterial dysbiosis with consequent immune alterations increase intrarectal SIV acquisition susceptibility.Cell reports · 2023Article
- LongDat: an R package for covariate-sensitive longitudinal analysis of high-dimensional data.Bioinformatics advances · 2023Article
- The abundance of Ruminococcus bromii is associated with faecal butyrate levels and atopic dermatitis in infancy.Allergy · 2022Article
- A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset.BMC genomics · 2022Observational
- Robust identification of temporal biomarkers in longitudinal omics studies.Bioinformatics (Oxford, England) · 2022Article
- Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations.PLoS computational biology · 2022Article
- Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19.JCI insight · 2022Article
- A Survey of Statistical Methods for Microbiome Data Analysis.Frontiers in applied mathematics and statistics · 2022Article
- Validation of a Classification Model Using Complete Blood Count to Predict Severe Human Adenovirus Lower Respiratory Tract Infections in Pediatric Cases.Frontiers in pediatrics · 2022Article
- Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study.BMC genomics · 2021Article
- Disruption of innate defense responses by endoglycosidase HPSE promotes cell survival.JCI insight · 2021Article
- Microbiota-associated risk factors for asymptomatic gut colonisation with multi-drug-resistant organisms in a Dutch nursing home.Genome medicine · 2021Article
- MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.BMC bioinformatics · 2021Article
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6 authors.
Funding
Abstract
backgroundMicrobial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects.
resultsWe present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort ( https://pubs.broadinstitute.org/diabimmune ) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease.
conclusionMetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN ( https://CRAN.R-project.org/package=MetaLonDA ).
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