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miaViz:Microbiome Analysis Plotting and Visualization
The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages.
Maintained by Tuomas Borman. Last updated 14 days ago.
microbiomesoftwarevisualizationbioconductormicrobiome-analysisplotting
10 stars 8.67 score 81 scripts 1 dependentsbioc
lefser:R implementation of the LEfSE method for microbiome biomarker discovery
lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes).
Maintained by Sehyun Oh. Last updated 1 months ago.
softwaresequencingdifferentialexpressionmicrobiomestatisticalmethodclassificationbioconductor-packager01ca230551
56 stars 8.44 score 56 scriptsbioc
MGnifyR:R interface to EBI MGnify metagenomics resource
Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework.
Maintained by Tuomas Borman. Last updated 6 days ago.
infrastructuredataimportmetagenomicsmicrobiomemicrobiomedata
21 stars 7.48 score 32 scriptsbioc
iSEEtree:Interactive visualisation for microbiome data
iSEEtree is an extension of iSEE for the TreeSummarizedExperiment data container. It provides interactive panel designs to explore hierarchical datasets, such as the microbiome and cell lines.
Maintained by Giulio Benedetti. Last updated 14 days ago.
softwarevisualizationmicrobiomeguishinyappsdataimportshiny-appsvisualisation
3 stars 6.28 score 5 scriptsbioc
dar:Differential Abundance Analysis by Consensus
Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.
Maintained by Francesc Catala-Moll. Last updated 18 days ago.
softwaresequencingmicrobiomemetagenomicsmultiplecomparisonnormalizationbioconductorbiomarker-discoverydifferential-abundance-analysisfeature-selectionmicrobiologyphyloseq
2 stars 5.98 score 8 scripts