Testing hypotheses about the microbiome using the linear decomposition model (LDM) is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 117 times.
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National Institutes of Health awards
Grant: R01GM116065
National Institutes of Health
Grant: 1R01GM116065-01A1
Association Tests of Rare Variants Using Sequence Reads without Calling Genotypes
NIH
FWCI
6.11
Citation Percentile
1.0%
Citation Trend
Fields of Study
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Keywords
Sustainable Development Goals
Additional file 1 of Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data
Additional file 1 of Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data
Additional file 1 of Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ)
Additional file 1 of Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ)
Additional file 2 of A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions
Additional file 2 of A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions
Additional file 1 of A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset
Additional file 1 of A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset
Additional file 1 of MIDASim: a fast and simple simulator for realistic microbiome data
Additional file 1 of MIDASim: a fast and simple simulator for realistic microbiome data
Additional file 2 of A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset
Additional file 2 of A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset