Package: microbiomeMarker 1.9.0

Yang Cao

microbiomeMarker: microbiome biomarker analysis toolkit

To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers.

Authors:Yang Cao [aut, cre]

microbiomeMarker_1.9.0.tar.gz
microbiomeMarker_1.9.0.zip(r-4.3)
microbiomeMarker_1.9.0.tgz(r-4.3-any)
microbiomeMarker_1.9.0.tar.gz(r-4.5-noble)microbiomeMarker_1.9.0.tar.gz(r-4.4-noble)
microbiomeMarker_1.9.0.tgz(r-4.4-emscripten)microbiomeMarker_1.9.0.tgz(r-4.3-emscripten)
microbiomeMarker.pdf |microbiomeMarker.html
microbiomeMarker/json (API)
NEWS

# Install 'microbiomeMarker' in R:
install.packages('microbiomeMarker', repos = c('https://metagenlab.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/yiluheihei/microbiomemarker/issues

Datasets:
  • caporaso - 16S rRNA data from "Moving pictures of the human microbiome"
  • cid_ying - 16S rRNA data of 94 patients from CID 2012
  • ecam - Data from Early Childhood Antibiotics and the Microbiome (ECAM) study
  • enterotypes_arumugam - Enterotypes data of 39 samples
  • kostic_crc - Data from a study on colorectal cancer
  • oxygen - Oxygen availability 16S dataset, of which taxa table has been summarized for python lefse input
  • pediatric_ibd - IBD stool samples
  • spontaneous_colitis - This is a sample data from lefse python script, a 16S dataset for studying the characteristics of the fecal microbiota in a mouse model of spontaneous colitis.

On CRAN:

metagenomicsmicrobiomedifferentialexpressionbiomarker-discoverydifferential-abundance-analysislefse

6.16 score 171 stars 1 packages 188 scripts 61 exports 252 dependencies

Last updated 1 years agofrom:afe4745f3e. Checks:ERROR: 4. Indexed: no.

TargetResultDate
Doc / VignettesFAILNov 09 2024
R-4.5-linuxERRORNov 09 2024
R-4.3-winERRORNov 09 2024
R-4.3-macERRORNov 09 2024

Exports:%>%abundancesaggregate_taxacompare_DAconfounderextract_posthoc_resimport_biomimport_dada2import_mothurimport_picrust2import_qiimeimport_qiime2marker_tablemarker_table<-microbiomeMarkernmarkernorm_clrnorm_cpmnorm_cssnorm_rarefynorm_rlenorm_tmmnorm_tssnormalizensamplesntaxaotu_tableotu_table2metagenomeSeqphyloseq2DESeq2phyloseq2edgeRphyloseq2metagenomeSeqplot_abundanceplot_cladogramplot_ef_barplot_ef_dotplot_heatmapplot_postHocTestplot_sl_rocpostHocTestrun_aldexrun_ancomrun_ancombcrun_deseq2run_edgerrun_lefserun_limma_voomrun_markerrun_metagenomeseqrun_posthoc_testrun_simple_statrun_slrun_test_multiple_groupsrun_test_two_groupssample_datasample_namesshowsubset_markersummarize_taxatax_tabletaxa_namestransform_abundances

Dependencies:abindade4ALDEx2ANCOMBCapeaplotaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelbiomformatBiostringsbitbit64bitopsbootbslibcachemcaretcaToolscellrangercheckmatecirclizeclassclicliprclockclueclustercodetoolscoincolorspacecommonmarkComplexHeatmapcpp11crayoncurlCVXRdata.tableDelayedArraydeldirDescToolsDESeq2diagramdigestdirectlabelsdoParalleldoRNGdplyre1071ECOSolveRedgeRenergyevaluateExactexpmfansifarverfastmapfontawesomeforcatsforeachforeignformatRFormulafsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggfunggplot2ggplotifyggsignifggtreegldglmnetGlobalOptionsglobalsgluegmpgowergplotsgridExtragridGraphicsgridSVGgslgtablegtoolshardhathavenhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrigraphinterpipredIRangesisobanditeratorsjpegjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelatticeExtralavalazyevallibcoinlifecyclelimmalistenvlme4lmerTestlmomlocfitlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetagenomeSeqmgcvmimeminqaModelMetricsmodeltoolsmultcompmulttestmunsellmvtnormNADAnlmenloptrnnetnumDerivopensslosqpparallellypatchworkpermutephyloseqpillarpixmappkgconfigplotROCplyrpngprettyunitspROCprodlimprogressprogressrpromisesproxypurrrquadprogR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratRdpackreadrreadxlrecipesrematchreshape2Rfastrhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRmpfrrngtoolsrootSolverpartrstudioapiS4ArraysS4VectorssandwichsassscalesscsshapeshinysnowsourcetoolsspSparseArraySQUAREMstatmodstringistringrSummarizedExperimentsurvivalsysTH.datatibbletidyrtidyselecttidytreetimechangetimeDatetinytextreeiotruncnormtzdbUCSC.utilsutf8vctrsveganviridisviridisLitevroomwithrWrenchxfunXMLxtableXVectoryamlyulab.utilszCompositionszlibbioczoo

Readme and manuals

Help Manual

Help pageTopics
Extract 'marker_table' object[ [,marker_table,ANY,ANY,ANY-method
Extract taxa abundancesabundances abundances, abundances,microbiomeMarker-method abundances,otu_table-method abundances,phyloseq-method otu_table-method
Aggregate Taxaaggregate_taxa
Assign a new OTU tableassign-otu_table otu_table<-,microbiomeMarker,microbiomeMarker-method otu_table<-,microbiomeMarker,otu_table-method otu_table<-,microbiomeMarker,phyloseq-method
Comparing the results of differential analysis methods by Empirical power and False Discovery Ratecompare_DA
Confounder analysisconfounder
16S rRNA data from "Moving pictures of the human microbiome"caporaso data-caporaso
16S rRNA data of 94 patients from CID 2012cid_ying data-cid_ying
Data from Early Childhood Antibiotics and the Microbiome (ECAM) studydata-ecam ecam
Enterotypes data of 39 samplesdata-enterotypes_arumugam enterotypes_arumugam
Data from a study on colorectal cancer (kostic 2012)data-kostic_crc kostic_crc
Oxygen availability 16S dataset, of which taxa table has been summarized for python lefse inputdata-oxygen oxygen
IBD stool samplesdata-pediatric_ibd pediatric_ibd
This is a sample data from lefse python script, a 16S dataset for studying the characteristics of the fecal microbiota in a mouse model of spontaneous colitis.data-spontaneous_colitis spontaneous_colitis
Extract results from a posthoc testextract_posthoc_res
Import function to read the the output of dada2 as phyloseq objectimport_dada2
Import function to read the output of picrust2 as phyloseq objectimport_picrust2
Import function to read the the output of dada2 as phyloseq objectimport_qiime2
Build or access the marker_tablemarker_table marker_table,data.frame-method marker_table,microbiomeMarker-method
The S4 class for storing microbiome marker informationmarker_table-class
Assign marker_table to 'object'assign-marker_table marker_table<-
Build microbiomeMarker-class objectsmicrobiomeMarker
The main class for microbiomeMarker datamicrobiomeMarker-class show,microbiomeMarker-method
Get the number of microbiome markersnmarker nmarker,marker_table-method nmarker,microbiomeMarker-method
Normalize the microbial abundance datanormalize normalize,data.frame-method normalize,matrix-method normalize,otu_table-method normalize,phyloseq-method norm_clr norm_cpm norm_css norm_rarefy norm_rle norm_tmm norm_tss
Convert 'phyloseq-class' object to 'DESeqDataSet-class' objectphyloseq2DESeq2
Convert phyloseq data to edgeR 'DGEList' objectphyloseq2edgeR
Convert phyloseq data to MetagenomeSeq 'MRexperiment' objectotu_table2metagenomeSeq phyloseq2metagenomeSeq
plot the abundances of markersplot_abundance
plot cladogram of micobiomeMaker resultsplot_cladogram
bar and dot plot of effect size of microbiomeMarker dataef-barplot,ef-dotplot plot_ef_bar plot_ef_dot
Heatmap of microbiome markerplot_heatmap
'postHocTest' plotplot_postHocTest
ROC curve of microbiome marker from supervised learning methodsplot_sl_roc
Plotting DA comparing resultplot.compareDA
Build postHocTest objectpostHocTest
The postHocTest Class, represents the result of post-hoc test result among multiple groupspostHocTest-class postHocTest-method show, show,postHocTest-method
Perform differential analysis using ALDEx2run_aldex
Perform differential analysis using ANCOMrun_ancom
Differential analysis of compositions of microbiomes with bias correction (ANCOM-BC).run_ancombc
Perform DESeq differential analysisrun_deseq2
Perform differential analysis using edgeRrun_edger
Liner discriminant analysis (LDA) effect size (LEFSe) analysisrun_lefse
Differential analysis using limma-voomrun_limma_voom
Find makers (differentially expressed metagenomic features)run_marker
metagenomeSeq differential analysisrun_metagenomeseq
Post hoc pairwise comparisons for multiple groups test.run_posthoc_test
Simple statistical analysis of metagenomic profilesrun_simple_stat
Identify biomarkers using supervised leaning (SL) methodsrun_sl
Statistical test for multiple groupsrun_test_multiple_groups
Statistical test between two groupsrun_test_two_groups
Subset microbiome markerssubset_marker
Summarize taxa into a taxonomic level within each samplesummarize_taxa
Summary differential analysis methods comparison resultssummary.compareDA
Transform the taxa abundances in 'otu_table' sample by sampletransform_abundances