Showing 8 of total 8 results (show query)
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metagenomeSeq:Statistical analysis for sparse high-throughput sequencing
metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
Maintained by Joseph N. Paulson. Last updated 4 months ago.
immunooncologyclassificationclusteringgeneticvariabilitydifferentialexpressionmicrobiomemetagenomicsnormalizationvisualizationmultiplecomparisonsequencingsoftware
69 stars 11.90 score 494 scripts 7 dependentsbioc
Maaslin2:"Multivariable Association Discovery in Population-scale Meta-omics Studies"
MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin.
Maintained by Lauren McIver. Last updated 5 months ago.
metagenomicssoftwaremicrobiomenormalizationbiobakerybioconductordifferential-abundance-analysisfalse-discovery-ratemultiple-covariatespublicrepeated-measurestools
133 stars 11.03 score 532 scripts 3 dependentsbioc
benchdamic:Benchmark of differential abundance methods on microbiome data
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Maintained by Matteo Calgaro. Last updated 4 months ago.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scriptsbioc
microbiomeDASim:Microbiome Differential Abundance Simulation
A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.
Maintained by Justin Williams. Last updated 5 months ago.
microbiomevisualizationsoftware
3 stars 4.48 score 1 scriptsbioc
MMUPHin:Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies
MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery.
Maintained by Siyuan MA. Last updated 5 months ago.
metagenomicsmicrobiomebatcheffect
4.44 score 46 scriptsbioc
Macarron:Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets
Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition.
Maintained by Sagun Maharjan. Last updated 5 months ago.
sequencingmetabolomicscoveragefunctionalpredictionclustering
4.41 score 13 scriptsbioc
mbQTL:mbQTL: A package for SNP-Taxa mGWAS analysis
mbQTL is a statistical R package for simultaneous 16srRNA,16srDNA (microbial) and variant, SNP, SNV (host) relationship, correlation, regression studies. We apply linear, logistic and correlation based statistics to identify the relationships of taxa, genus, species and variant, SNP, SNV in the infected host. We produce various statistical significance measures such as P values, FDR, BC and probability estimation to show significance of these relationships. Further we provide various visualization function for ease and clarification of the results of these analysis. The package is compatible with dataframe, MRexperiment and text formats.
Maintained by Mercedeh Movassagh. Last updated 5 months ago.
snpmicrobiomewholegenomemetagenomicsstatisticalmethodregression
1 stars 4.00 score 3 scriptsbioc
microbiomeExplorer:Microbiome Exploration App
The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.
Maintained by Janina Reeder. Last updated 5 months ago.
classificationclusteringgeneticvariabilitydifferentialexpressionmicrobiomemetagenomicsnormalizationvisualizationmultiplecomparisonsequencingsoftwareimmunooncology
4.00 score 8 scripts