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"build_tree,DNAbin-method", "build_tree,DNAStringSet", "build_tree,DNAStringSet-method" ] }, { "page": "convert_to_treedata", "title": "convert dataframe contained hierarchical relationship or other classes to treedata class", "topics": [ "convert_to_treedata" ] }, { "page": "data-hmp_aerobiosis_small", "title": "(Data) Small subset of the HMP 16S dataset", "topics": [ "data-hmp_aerobiosis_small", "hmp_aerobiosis_small" ] }, { "page": "data-kostic2012crc", "title": "(Data) Genomic analysis identifies association of Fusobacterium with colorectal carcinoma (2012)", "topics": [ "data-kostic2012crc", "kostic2012crc" ] }, { "page": "data-test_otu_data", "title": "(Data) simulated dataset.", "topics": [ "data-test_otu_data", "test_otu_data" ] }, { "page": "diff_analysis", "title": "Differential expression analysis", "topics": [ "diff_analysis", "diff_analysis.data.frame", "diff_analysis.phyloseq" ] }, { "page": "diffAnalysisClass-class", "title": "diffAnalysisClass class", "topics": [ "diffAnalysisClass-class" ] }, { "page": "dr_extract", "title": "Extracting the internal tbl_df attribute of tibble.", "topics": [ "dr_extract" ] }, { "page": "drop_taxa", "title": "Dropping Species with Few abundance and Few Occurrences", "topics": [ "drop_taxa", "drop_taxa,data.frame", "drop_taxa,data.frame-method", "drop_taxa,phyloseq", "drop_taxa,phyloseq-method" ] }, { "page": "extract_binary_offspring", "title": "extract the binary offspring of the specified internal nodes", "topics": [ "extract_binary_offspring" ] }, { "page": "generalizedFC", "title": "generalized fold change", "topics": [ "generalizedFC", "generalizedFC.default", "generalizedFC.formula" ] }, { "page": "get_alphaindex", "title": "alpha index", "topics": [ "get_alphaindex", "get_alphaindex,data.frame", "get_alphaindex,data.frame-method", "get_alphaindex,integer", "get_alphaindex,integer-method", "get_alphaindex,matrix", "get_alphaindex,matrix-method", "get_alphaindex,numeric", "get_alphaindex,numeric-method", "get_alphaindex,phyloseq", "get_alphaindex,phyloseq-method" ] }, { "page": "get_clust", "title": "Hierarchical cluster analysis for the samples", "topics": [ "get_clust", "get_clust.data.frame", "get_clust.dist", "get_clust.phyloseq" ] }, { "page": "get_coord", "title": "get ordination coordinates.", "topics": [ "get_coord", "get_coord.pcoa", "get_coord.prcomp" ] }, { "page": "get_count", "title": "calculate the count or relative abundance of replicate element with a speficify column", "topics": [ "get_count", "get_ratio" ] }, { "page": "get_dist", "title": "calculate distance", "topics": [ "get_dist", "get_dist.data.frame", "get_dist.phyloseq" ] }, { "page": "get_mean_median", "title": "get the mean and median of specific feature.", "topics": [ "get_mean_median" ] }, { "page": "get_NRI_NTI-methods", "title": "calculating related phylogenetic alpha metric", "topics": [ "get_NRI_NTI", "get_NRI_NTI,data.frame", "get_NRI_NTI,data.frame-method", "get_NRI_NTI,matrix", "get_NRI_NTI,matrix-method", "get_NRI_NTI,phyloseq", "get_NRI_NTI,phyloseq-method" ] }, { "page": "get_pca", "title": "Performs a principal components analysis", "topics": [ "get_pca", "get_pca.data.frame", "get_pca.phyloseq" ] }, { "page": "get_pcoa", "title": "performs principal coordinate analysis (PCoA)", "topics": [ "get_pcoa", "get_pcoa.data.frame", "get_pcoa.dist", "get_pcoa.phyloseq" ] }, { "page": "get_pvalue", "title": "Methods for computation of the p-value", "topics": [ "get_pvalue", "get_pvalue.glm", "get_pvalue.htest", "get_pvalue.lm", "get_pvalue.lme", "get_pvalue.negbin", "get_pvalue.QuadTypeIndependenceTest", "get_pvalue.ScalarIndependenceTest" ] }, { "page": "get_rarecurve", "title": "obtain the result of rare curve", "topics": [ "get_rarecurve", "get_rarecurve,data.frame", "get_rarecurve,data.frame-method", "get_rarecurve,phyloseq", "get_rarecurve,phyloseq-method" ] }, { "page": "get_sampledflist", "title": "Generate random data list from a original data.", "topics": [ "get_sampledflist" ] }, { "page": "get_taxadf", "title": "get the data of specified taxonomy", "topics": [ "get_taxadf", "get_taxadf,data.frame", "get_taxadf,data.frame-method", "get_taxadf,phyloseq", "get_taxadf,phyloseq-method" ] }, { "page": "get_upset", "title": "generate the dataset for upset of UpSetR", "topics": [ "get_upset", "get_upset,data.frame", "get_upset,data.frame-method", "get_upset,phyloseq", "get_upset,phyloseq-method" ] }, { "page": "get_varct", "title": "get the contribution of variables", "topics": [ "get_varct", "get_varct.pcasample", "get_varct.pcoa", "get_varct.prcomp" ] }, { "page": "get_vennlist", "title": "generate a vennlist for VennDiagram", "topics": [ "get_vennlist", "get_vennlist,data.frame-method", "get_vennlist,data.framet", "get_vennlist,phyloseq", "get_vennlist,phyloseq-method" ] }, { "page": "ggbartax", "title": "taxonomy barplot", "topics": [ "ggbartax", "ggbartax.data.frame", "ggbartax.phyloseq", "ggbartaxa" ] }, { "page": "ggbox", "title": "A box or violin plot with significance test", "topics": [ "ggbox", "ggbox,alphasample", "ggbox,alphasample-method", "ggbox,data.frame", "ggbox,data.frame-method" ] }, { "page": "ggclust", "title": "plot the result of hierarchical cluster analysis for the samples", "topics": [ "ggclust", "ggclust.treedata" ] }, { "page": "ggdiffbox", "title": "boxplot for the result of diff_analysis", "topics": [ "ggdiffbox", "ggdiffbox,diffAnalysisClass", "ggdiffbox,diffAnalysisClass-method" ] }, { "page": "ggdiffclade", "title": "plot the clade tree with highlight", "topics": [ "ggdiffclade", "ggdiffclade.data.frame", "ggdiffclade.diffAnalysisClass" ] }, { "page": "ggdifftaxbar", "title": "significantly discriminative feature barplot", "topics": [ "ggdiffbartaxa", "ggdifftaxbar", "ggdifftaxbar,diffAnalysisClass", "ggdifftaxbar,diffAnalysisClass-method", "ggdifftaxbar.featureMeanMedian" ] }, { "page": "ggeffectsize", "title": "visualization of effect size by the Linear Discriminant Analysis or randomForest", "topics": [ "ggeffectsize", "ggeffectsize.data.frame", "ggeffectsize.diffAnalysisClass" ] }, { "page": "ggordpoint", "title": "ordination plotter based on ggplot2.", "topics": [ "ggordpoint", "ggordpoint.default", "ggordpoint.pcasample" ] }, { "page": "ggrarecurve", "title": "Rarefaction alpha index", "topics": [ "ggrarecurve", "ggrarecurve.data.frame", "ggrarecurve.phyloseq", "ggrarecurve.rarecurve" ] }, { "page": "ImportDada2", "title": "Import function to load the feature table and taxonomy table of dada2", "topics": [ "ImportDada2", "import_dada2", "mp_import_dada2" ] }, { "page": "ImportQiime2", "title": "Import function to load the output of qiime2.", "topics": [ "ImportQiime2", "import_qiime2", "mp_import_qiime2" ] }, { "page": "mouse.time.mpse", "title": "(Data) An example data", "topics": [ "mouse.time.mpse" ] }, { "page": "mp_adonis-methods", "title": "Permutational Multivariate Analysis of Variance Using Distance Matrices for MPSE or tbl_mpse object", "topics": [ "mp_adonis", "mp_adonis,grouped_df_mpse", "mp_adonis,grouped_df_mpse-method", "mp_adonis,MPSE", "mp_adonis,MPSE-method", "mp_adonis,tbl_mpse", "mp_adonis,tbl_mpse-method" ] }, { "page": "mp_aggregate-methods", "title": "aggregate the assays with the specific group of sample and fun.", "topics": [ "mp_aggregate", "mp_aggregate,MPSE", "mp_aggregate,MPSE-method" ] }, { "page": "mp_aggregate_clade-methods", "title": "calculate the mean/median (relative) abundance of internal nodes according to their children tips.", "topics": [ "mp_aggregate_clade", "mp_aggregate_clade,grouped_df_mpse", "mp_aggregate_clade,grouped_df_mpse-method", "mp_aggregate_clade,MPSE", "mp_aggregate_clade,MPSE-method", "mp_aggregate_clade,tbl_mpse", "mp_aggregate_clade,tbl_mpse-method" ] }, { "page": "mp_anosim-methods", "title": "Analysis of Similarities (ANOSIM) with MPSE or tbl_mpse object", "topics": [ "mp_anosim", "mp_anosim,grouped_df_mpse", "mp_anosim,grouped_df_mpse-method", "mp_anosim,MPSE", "mp_anosim,MPSE-method", "mp_anosim,tbl_mpse", "mp_anosim,tbl_mpse-method" ] }, { "page": "mp_balance_clade-methods", "title": "Calculating the balance score of internal nodes (clade) according to the geometric.mean/mean/median abundance of their binary children tips.", "topics": [ "mp_balance_clade", "mp_balance_clade,grouped_df_mpse", "mp_balance_clade,grouped_df_mpse-method", "mp_balance_clade,MPSE", "mp_balance_clade,MPSE-method", "mp_balance_clade,tbl_mpse", "mp_balance_clade,tbl_mpse-method" ] }, { "page": "mp_cal_abundance-methods", "title": "Calculate the (relative) abundance of each taxonomy class for each sample or group.", "topics": [ "mp_cal_abundance", "mp_cal_abundance,grouped_df_mpse", "mp_cal_abundance,grouped_df_mpse-method", "mp_cal_abundance,MPSE", "mp_cal_abundance,MPSE-method", "mp_cal_abundance,tbl_mpse", "mp_cal_abundance,tbl_mpse-method" ] }, { "page": "mp_cal_alpha-methods", "title": "calculate the alpha index with MPSE or tbl_mpse", "topics": [ "mp_cal_alpha", "mp_cal_alpha,grouped_df_mpse", "mp_cal_alpha,grouped_df_mpse-method", "mp_cal_alpha,MPSE", "mp_cal_alpha,MPSE-method", "mp_cal_alpha,tbl_mpse", "mp_cal_alpha,tbl_mpse-method" ] }, { "page": "mp_cal_cca-methods", "title": "[Partial] [Constrained] Correspondence Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_cca", "mp_cal_cca,grouped_df_mpse", "mp_cal_cca,grouped_df_mpse-method", "mp_cal_cca,MPSE", "mp_cal_cca,MPSE-method", "mp_cal_cca,tbl_mpse", "mp_cal_cca,tbl_mpse-method" ] }, { "page": "mp_cal_clust-methods", "title": "Hierarchical cluster analysis for the samples with MPSE or tbl_mpse object", "topics": [ "mp_cal_clust", "mp_cal_clust,grouped_df_mpse", "mp_cal_clust,grouped_df_mpse-method", "mp_cal_clust,MPSE", "mp_cal_clust,MPSE-method", "mp_cal_clust,tbl_mpse", "mp_cal_clust,tbl_mpse-method" ] }, { "page": "mp_cal_dca-methods", "title": "Detrended Correspondence Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_dca", "mp_cal_dca,grouped_df_mpse", "mp_cal_dca,grouped_df_mpse-method", "mp_cal_dca,MPSE", "mp_cal_dca,MPSE-method", "mp_cal_dca,tbl_mpse", "mp_cal_dca,tbl_mpse-method" ] }, { "page": "mp_cal_dist-methods", "title": "Calculate the distances between the samples or features with specified abundance.", "topics": [ "mp_cal_dist", "mp_cal_dist,grouped_df_mpse", "mp_cal_dist,grouped_df_mpse-method", "mp_cal_dist,MPSE", "mp_cal_dist,MPSE-method", "mp_cal_dist,tbl_mpse", "mp_cal_dist,tbl_mpse-method" ] }, { "page": "mp_cal_divergence-methods", "title": "calculate the divergence with MPSE or tbl_mpse", "topics": [ "mp_cal_divergence", "mp_cal_divergence,grouped_df_mpse", "mp_cal_divergence,grouped_df_mpse-method", "mp_cal_divergence,MPSE", "mp_cal_divergence,MPSE-method", "mp_cal_divergence,tbl_mpse", "mp_cal_divergence,tbl_mpse-method" ] }, { "page": "mp_cal_nmds-methods", "title": "Nonmetric Multidimensional Scaling Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_nmds", "mp_cal_nmds,grouped_df_mpse", "mp_cal_nmds,grouped_df_mpse-method", "mp_cal_nmds,MPSE", "mp_cal_nmds,MPSE-method", "mp_cal_nmds,tbl_mpse", "mp_cal_nmds,tbl_mpse-method" ] }, { "page": "mp_cal_pca-methods", "title": "Principal Components Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_pca", "mp_cal_pca,grouped_df_mpse", "mp_cal_pca,grouped_df_mpse-method", "mp_cal_pca,MPSE", "mp_cal_pca,MPSE-method", "mp_cal_pca,tbl_mpse", "mp_cal_pca,tbl_mpse-method" ] }, { "page": "mp_cal_pcoa-methods", "title": "Principal Coordinate Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_pcoa", "mp_cal_pcoa,grouped_df_mpse", "mp_cal_pcoa,grouped_df_mpse-method", "mp_cal_pcoa,MPSE", "mp_cal_pcoa,MPSE-method", "mp_cal_pcoa,tbl_mpse", "mp_cal_pcoa,tbl_mpse-method" ] }, { "page": "mp_cal_pd_metric-methods", "title": "Calculating related phylogenetic alpha metric with MPSE or tbl_mpse object", "topics": [ "mp_cal_pd_metric", "mp_cal_pd_metric,grouped_df_mpse", "mp_cal_pd_metric,grouped_df_mpse-method", "mp_cal_pd_metric,MPSE", "mp_cal_pd_metric,MPSE-method", "mp_cal_pd_metric,tbl_mpse", "mp_cal_pd_metric,tbl_mpse-method" ] }, { "page": "mp_cal_rarecurve-methods", "title": "Calculating the different alpha diversities index with different depth", "topics": [ "mp_cal_rarecurve", "mp_cal_rarecurve,grouped_df_mpse", "mp_cal_rarecurve,grouped_df_mpse-method", "mp_cal_rarecurve,MPSE", "mp_cal_rarecurve,MPSE-method", "mp_cal_rarecurve,tbl_mpse", "mp_cal_rarecurve,tbl_mpse-method" ] }, { "page": "mp_cal_rda-methods", "title": "[Partial] [Constrained] Redundancy Analysis with MPSE or tbl_mpse object", "topics": [ "mp_cal_rda", "mp_cal_rda,grouped_df_mpse", "mp_cal_rda,grouped_df_mpse-method", "mp_cal_rda,MPSE", "mp_cal_rda,MPSE-method", "mp_cal_rda,tbl_mpse", "mp_cal_rda,tbl_mpse-method" ] }, { "page": "mp_cal_upset-methods", "title": "Calculating the samples or groups for each OTU, the result can be visualized by 'ggupset'", "topics": [ "mp_cal_upset", "mp_cal_upset,grouped_df_mpse", "mp_cal_upset,grouped_df_mpse-method", "mp_cal_upset,MPSE", "mp_cal_upset,MPSE-method", "mp_cal_upset,tbl_mpse", "mp_cal_upset,tbl_mpse-method" ] }, { "page": "mp_cal_venn-methods", "title": "Calculating the OTU for each sample or group, the result can be visualized by 'ggVennDiagram'", "topics": [ "mp_cal_venn", "mp_cal_venn,grouped_df_mpse", "mp_cal_venn,grouped_df_mpse-method", "mp_cal_venn,MPSE", "mp_cal_venn,MPSE-method", "mp_cal_venn,tbl_mpse", "mp_cal_venn,tbl_mpse-method" ] }, { "page": "mp_decostand-methods", "title": "This Function Provideds Several Standardization Methods for Community Data", "topics": [ "mp_decostand", "mp_decostand,data.frame", "mp_decostand,data.frame-method", "mp_decostand,grouped_df_mpse", "mp_decostand,grouped_df_mpse-method", "mp_decostand,MPSE", "mp_decostand,MPSE-method", "mp_decostand,tbl_mpse", "mp_decostand,tbl_mpse-method" ] }, { "page": "mp_diff_analysis-methods", "title": "Differential expression analysis for MPSE or tbl_mpse object", "topics": [ "mp_diff_analysis", "mp_diff_analysis,grouped_df_mpse", "mp_diff_analysis,grouped_df_mpse-method", "mp_diff_analysis,MPSE", "mp_diff_analysis,MPSE-method", "mp_diff_analysis,tbl_mpse", "mp_diff_analysis,tbl_mpse-method" ] }, { "page": "mp_diff_clade-methods", "title": "Differential internal and tip nodes (clades) analysis for MPSE or tbl_mpse object", "topics": [ "mp_diff_clade", "mp_diff_clade,grouped_df_mpse", "mp_diff_clade,grouped_df_mpse-method", "mp_diff_clade,MPSE", "mp_diff_clade,MPSE-method", "mp_diff_clade,tbl_mpse", "mp_diff_clade,tbl_mpse-method" ] }, { "page": "mp_dmn-methods", "title": "Fit Dirichlet-Multinomial models to MPSE or tbl_mpse", "topics": [ "mp_dmn", "mp_dmn,grouped_df_mpse", "mp_dmn,grouped_df_mpse-method", "mp_dmn,MPSE", "mp_dmn,MPSE-method", "mp_dmn,tbl_mpse", "mp_dmn,tbl_mpse-method" ] }, { "page": "mp_dmngroup-methods", "title": "Dirichlet-Multinomial generative classifiers to MPSE or tbl_mpse", "topics": [ "mp_dmngroup", "mp_dmngroup,grouped_df_mpse", "mp_dmngroup,grouped_df_mpse-method", "mp_dmngroup,MPSE", "mp_dmngroup,MPSE-method", "mp_dmngroup,tbl_mpse", "mp_dmngroup,tbl_mpse-method" ] }, { "page": "mp_envfit-methods", "title": "Fits an Environmental Vector or Factor onto an Ordination With MPSE or tbl_mpse Object", "topics": [ "mp_envfit", "mp_envfit,grouped_df_mpse", "mp_envfit,grouped_df_mpse-method", "mp_envfit,MPSE", "mp_envfit,MPSE-method", "mp_envfit,tbl_mpse", "mp_envfit,tbl_mpse-method" ] }, { "page": "mp_extract_abundance-methods", "title": "Extracting the abundance metric from MPSE or tbl_mpse object", "topics": [ "mp_extract_abundance", "mp_extract_abundance,grouped_df_mpse", "mp_extract_abundance,grouped_df_mpse-method", "mp_extract_abundance,MPSE", "mp_extract_abundance,MPSE-method", "mp_extract_abundance,tbl_mpse", "mp_extract_abundance,tbl_mpse-method" ] }, { "page": "mp_extract_assays-methods", "title": "extract the abundance matrix from MPSE object or tbl_mpse object", "topics": [ "mp_extract_assays", "mp_extract_assays,grouped_df_mpse", "mp_extract_assays,grouped_df_mpse-method", "mp_extract_assays,MPSE", "mp_extract_assays,MPSE-method", "mp_extract_assays,tbl_mpse", "mp_extract_assays,tbl_mpse-method" ] }, { "page": "mp_extract_dist-methods", "title": "extract the dist object from MPSE or tbl_mpse object", "topics": [ "mp_extract_dist", "mp_extract_dist,grouped_df_mpse", "mp_extract_dist,grouped_df_mpse-method", "mp_extract_dist,MPSE", "mp_extract_dist,MPSE-method", "mp_extract_dist,tbl_mpse", "mp_extract_dist,tbl_mpse-method" ] }, { "page": "mp_extract_feature-methods", "title": "extract the feature (OTU) information in MPSE object", "topics": [ "mp_extract_feature", "mp_extract_feature,grouped_df_mpse", "mp_extract_feature,grouped_df_mpse-method", "mp_extract_feature,MPSE", "mp_extract_feature,MPSE-method", "mp_extract_feature,tbl_mpse", "mp_extract_feature,tbl_mpse-method" ] }, { "page": "mp_extract_internal_attr-methods", "title": "Extracting the PCA, PCoA, etc results from MPSE or tbl_mpse object", "topics": [ "mp_extract_internal_attr", "mp_extract_internal_attr,grouped_df_mpse", "mp_extract_internal_attr,grouped_df_mpse-method", "mp_extract_internal_attr,MPSE", "mp_extract_internal_attr,MPSE-method", "mp_extract_internal_attr,tbl_mpse", "mp_extract_internal_attr,tbl_mpse-method" ] }, { "page": "mp_extract_rarecurve-methods", "title": "Extract the result of mp_cal_rarecurve with action=\"add\" from MPSE or tbl_mpse object", "topics": [ "mp_extract_rarecurve", "mp_extract_rarecurve,grouped_df_mpse", "mp_extract_rarecurve,grouped_df_mpse-method", "mp_extract_rarecurve,MPSE", "mp_extract_rarecurve,MPSE-method", "mp_extract_rarecurve,tbl_mpse", "mp_extract_rarecurve,tbl_mpse-method" ] }, { "page": "mp_extract_refseq-methods", "title": "Extract the representative sequences from MPSE object", "topics": [ "mp_extract_refseq", "mp_extract_refseq,grouped_df_mpse", "mp_extract_refseq,grouped_df_mpse-method", "mp_extract_refseq,MPSE", "mp_extract_refseq,MPSE-method", "mp_extract_refseq,tbl_mpse", "mp_extract_refseq,tbl_mpse-method" ] }, { "page": "mp_extract_sample-methods", "title": "extract the sample information in MPSE object", "topics": [ "mp_extract_sample", "mp_extract_sample,grouped_df_mpse", "mp_extract_sample,grouped_df_mpse-method", "mp_extract_sample,MPSE", "mp_extract_sample,MPSE-method", "mp_extract_sample,tbl_mpse", "mp_extract_sample,tbl_mpse-method" ] }, { "page": "mp_extract_tree-methods", "title": "extract the taxonomy tree in MPSE object", "topics": [ "mp_extract_otutree", "mp_extract_taxatree", "mp_extract_tree", "mp_extract_tree,grouped_df_mpse", "mp_extract_tree,grouped_df_mpse-method", "mp_extract_tree,MPSE", "mp_extract_tree,MPSE-method", "mp_extract_tree,tbl_mpse", "mp_extract_tree,tbl_mpse-method" ] }, { "page": "mp_filter_taxa-methods", "title": "Filter OTU (Features) By Abundance Level", "topics": [ "mp_filter_taxa", "mp_filter_taxa,grouped_df_mpse", "mp_filter_taxa,grouped_df_mpse-method", "mp_filter_taxa,MPSE", "mp_filter_taxa,MPSE-method", "mp_filter_taxa,tbl_mpse", "mp_filter_taxa,tbl_mpse-method" ] }, { "page": "mp_fortify", "title": "mp_fortify", "topics": [ "mp_fortify" ] }, { "page": "mp_import_biom", "title": "building MPSE object from biom-format file.", "topics": [ "mp_import_biom" ] }, { "page": "mp_import_humann_regroup", "title": "Import function to load the output of human_regroup_table in HUMAnN.", "topics": [ "mp_import_humann_regroup" ] }, { "page": "mp_import_metaphlan", "title": "Import function to load the output of MetaPhlAn.", "topics": [ "mp_import_metaphlan" ] }, { "page": "mp_import_qiime", "title": "Import function to load the output of qiime.", "topics": [ "mp_import_qiime" ] }, { "page": "mp_mantel-methods", "title": "Mantel and Partial Mantel Tests for MPSE or tbl_mpse Object", "topics": [ "mp_mantel", "mp_mantel,grouped_df_mpse", "mp_mantel,grouped_df_mpse-method", "mp_mantel,MPSE", "mp_mantel,MPSE-method", "mp_mantel,tbl_mpse", "mp_mantel,tbl_mpse-method" ] }, { "page": "mp_mrpp-methods", "title": "Analysis of Multi Response Permutation Procedure (MRPP) with MPSE or tbl_mpse object", "topics": [ "mp_mrpp", "mp_mrpp,grouped_df_mpse", "mp_mrpp,grouped_df_mpse-method", "mp_mrpp,MPSE", "mp_mrpp,MPSE-method", "mp_mrpp,tbl_mpse", "mp_mrpp,tbl_mpse-method" ] }, { "page": "mp_plot_abundance-methods", "title": "plotting the abundance of taxa via specified taxonomy class", "topics": [ "mp_plot_abundance", "mp_plot_abundance,grouped_df_mpse", "mp_plot_abundance,grouped_df_mpse-method", "mp_plot_abundance,MPSE", "mp_plot_abundance,MPSE-method", "mp_plot_abundance,tbl_mpse", "mp_plot_abundance,tbl_mpse-method" ] }, { "page": "mp_plot_alpha-methods", "title": "Plotting the alpha diversity between samples or groups.", "topics": [ "mp_plot_alpha", "mp_plot_alpha,grouped_df_mpse", "mp_plot_alpha,grouped_df_mpse-method", "mp_plot_alpha,MPSE", "mp_plot_alpha,MPSE-method", "mp_plot_alpha,tbl_mpse", "mp_plot_alpha,tbl_mpse-method" ] }, { "page": "mp_plot_diff_boxplot-methods", "title": "displaying the differential result contained abundance and LDA with boxplot (abundance) and error bar (LDA).", "topics": [ "mp_plot_diff_boxplot", "mp_plot_diff_boxplot,grouped_df_mpse", "mp_plot_diff_boxplot,grouped_df_mpse-method", "mp_plot_diff_boxplot,MPSE", "mp_plot_diff_boxplot,MPSE-method", "mp_plot_diff_boxplot,tbl_mpse", "mp_plot_diff_boxplot,tbl_mpse-method" ] }, { "page": "mp_plot_diff_cladogram", "title": "Visualizing the result of mp_diff_analysis with cladogram.", "topics": [ "mp_plot_diff_cladogram" ] }, { "page": "mp_plot_diff_manhattan-methods", "title": "displaying the differential result contained abundance and LDA with manhattan plot.", "topics": [ "mp_plot_diff_manhattan", "mp_plot_diff_manhattan,grouped_df_mpse", "mp_plot_diff_manhattan,grouped_df_mpse-method", "mp_plot_diff_manhattan,MPSE", "mp_plot_diff_manhattan,MPSE-method", "mp_plot_diff_manhattan,tbl_mpse", "mp_plot_diff_manhattan,tbl_mpse-method" ] }, { "page": "mp_plot_diff_res-methods", "title": "The visualization of result of mp_diff_analysis", "topics": [ "mp_plot_diff_res", "mp_plot_diff_res,grouped_df_mpse", "mp_plot_diff_res,grouped_df_mpse-method", "mp_plot_diff_res,MPSE", "mp_plot_diff_res,MPSE-method", "mp_plot_diff_res,tbl_mpse", "mp_plot_diff_res,tbl_mpse-method" ] }, { "page": "mp_plot_dist-methods", "title": "Plotting the distance between the samples with heatmap or boxplot.", "topics": [ "mp_plot_dist", "mp_plot_dist,grouped_df_mpse", "mp_plot_dist,grouped_df_mpse-method", "mp_plot_dist,MPSE", "mp_plot_dist,MPSE-method", "mp_plot_dist,tbl_mpse", "mp_plot_dist,tbl_mpse-method" ] }, { "page": "mp_plot_ord-methods", "title": "Plotting the result of PCA, PCoA, CCA, RDA, NDMS or DCA", "topics": [ "mp_plot_ord", "mp_plot_ord,grouped_df_mpse", "mp_plot_ord,grouped_df_mpse-method", "mp_plot_ord,MPSE", "mp_plot_ord,MPSE-method", "mp_plot_ord,tbl_mpse", "mp_plot_ord,tbl_mpse-method" ] }, { "page": "mp_plot_rarecurve-methods", "title": "Rarefaction alpha index with MPSE", "topics": [ "mp_plot_rarecurve", "mp_plot_rarecurve,grouped_df_mpse-method", "mp_plot_rarecurve,grouped_tbl_mpse", "mp_plot_rarecurve,MPSE", "mp_plot_rarecurve,MPSE-method", "mp_plot_rarecurve,tbl_mpse", "mp_plot_rarecurve,tbl_mpse-method" ] }, { "page": "mp_plot_upset-methods", "title": "Plotting the different number of OTU between group via UpSet plot", "topics": [ "mp_plot_upset", "mp_plot_upset,grouped_df_mpse", "mp_plot_upset,grouped_df_mpse-method", "mp_plot_upset,MPSE", "mp_plot_upset,MPSE-method", "mp_plot_upset,tbl_mpse", "mp_plot_upset,tbl_mpse-method" ] }, { "page": "mp_plot_venn-methods", "title": "Plotting the different number of OTU between groups with Venn Diagram.", "topics": [ "mp_plot_venn", "mp_plot_venn,grouped_df_mpse", "mp_plot_venn,grouped_df_mpse-method", "mp_plot_venn,MPSE", "mp_plot_venn,MPSE-method", "mp_plot_venn,tbl_mpse", "mp_plot_venn,tbl_mpse-method" ] }, { "page": "mp_rrarefy-methods", "title": "mp_rrarefy method", "topics": [ "mp_rrarefy", "mp_rrarefy,grouped_df_mpse", "mp_rrarefy,grouped_df_mpse-method", "mp_rrarefy,MPSE", "mp_rrarefy,MPSE-method", "mp_rrarefy,tbl_mpse", "mp_rrarefy,tbl_mpse-method" ] }, { "page": "mp_select_as_tip-methods", "title": "select specific taxa level as rownames of MPSE", "topics": [ "mp_select_as_tip", "mp_select_as_tip,grouped_df_mpse", "mp_select_as_tip,grouped_df_mpse-method", "mp_select_as_tip,MPSE", "mp_select_as_tip,MPSE-method", "mp_select_as_tip,tbl_mpse", "mp_select_as_tip,tbl_mpse-method" ] }, { "page": "mp_stat_taxa-methods", "title": "Count the number and total number taxa for each sample at different taxonomy levels", "topics": [ "mp_stat_taxa", "mp_stat_taxa,grouped_df_mpse", "mp_stat_taxa,grouped_df_mpse-method", "mp_stat_taxa,MPSE", "mp_stat_taxa,MPSE-method", "mp_stat_taxa,tbl_mpse", "mp_stat_taxa,tbl_mpse-method" ] }, { "page": "MPSE", "title": "Construct a MPSE object", "topics": [ "MPSE" ] }, { "page": "MPSE-accessors", "title": "MPSE accessors", "topics": [ "colData<-,MPSE,DataFrame-method", "colData<-,MPSE,NULL-method", "MPSE-accessors", "otutree", "otutree,group_df_mpse", "otutree,MPSE", "otutree,MPSE-method", "otutree,tbl_mpse", "otutree,tbl_mpse-method", "otutree<-", "otutree<-,grouped_df_mpse", "otutree<-,grouped_df_mpse,NULL-method", "otutree<-,grouped_df_mpse,treedata-method", "otutree<-,MPSE", "otutree<-,MPSE,NULL-method", "otutree<-,MPSE,phylo-method", "otutree<-,MPSE,treedata-method", "otutree<-,tbl_mpse", "otutree<-,tbl_mpse,NULL-method", "otutree<-,tbl_mpse,treedata-method", "refsequence", "refsequence,MPSE", "refsequence,MPSE-method", "refsequence<-", "refsequence<-,MPSE", "refsequence<-,MPSE,NULL-method", "refsequence<-,MPSE,XStringSet-method", "rownames<-,MPSE", "rownames<-,MPSE-method", "taxatree", "taxatree,grouped_df_mpse", "taxatree,grouped_df_mpse-method", "taxatree,MPSE", "taxatree,MPSE-method", "taxatree,tbl_mpse", "taxatree,tbl_mpse-method", "taxatree<-", "taxatree<-,grouped_df_mpse", "taxatree<-,grouped_df_mpse,NULL-method", "taxatree<-,grouped_df_mpse,treedata-method", "taxatree<-,MPSE", "taxatree<-,MPSE,NULL-method", "taxatree<-,MPSE,treedata-method", "taxatree<-,tbl_mpse", "taxatree<-,tbl_mpse,NULL-method", "taxatree<-,tbl_mpse,treedata-method", "taxonomy<-", "taxonomy<-,MPSE", "taxonomy<-,MPSE,data.frame-method", "taxonomy<-,MPSE,matrix-method", "taxonomy<-,MPSE,NULL-method", "taxonomy<-,MPSE,taxonomyTable-method", "tax_table", "tax_table,grouped_df_mpse", "tax_table,grouped_df_mpse-method", "tax_table,MPSE", "tax_table,MPSE-method", "tax_table,tbl_mpse", "tax_table,tbl_mpse-method", "[,MPSE,ANY,ANY,ANY-method" ] }, { "page": "MPSE-class", "title": "MPSE class", "topics": [ "MPSE-class" ] }, { "page": "multi_compare", "title": "a container for performing two or more sample test.", "topics": [ "multi_compare" ] }, { "page": "ordplotClass-class", "title": "ordplotClass class", "topics": [ "ordplotClass-class" ] }, { "page": "pcasample-class", "title": "pcasample class", "topics": [ "pcasample-class" ] }, { "page": "print", "title": "print some objects", "topics": [ "print", "print.grouped_df_mpse", "print.MPSE", "print.rarecurve", "print.tbl_mpse" ] }, { "page": "read_qza", "title": "read the qza file, output of qiime2.", "topics": [ "read_qza" ] }, { "page": "scale_fill_diff_cladogram", "title": "Create the scale of mp_plot_diff_cladogram.", "topics": [ "scale_fill_diff_cladogram" ] }, { "page": "set_diff_boxplot_color", "title": "set the color scale of plot generated by mp_plot_diff_boxplot", "topics": [ "set_diff_boxplot_color" ] }, { "page": "set_scale_theme", "title": "adjust the color of heatmap of mp_plot_dist", "topics": [ "set_scale_theme" ] }, { "page": "show-methods", "title": "method extensions to show for diffAnalysisClass or alphasample objects.", "topics": [ "show,alphasample-method", "show,diffAnalysisClass-method", "show,MPSE-method" ] }, { "page": "split_data", "title": "Split Large Vector or DataFrame", "topics": [ "split_data" ] }, { "page": "split_str_to_list", "title": "split a dataframe contained one column", "topics": [ "split_str_to_list" ] }, { "page": "mp_extract_taxonomy-methods", "title": "extract the taxonomy annotation in MPSE object", "topics": [ "mp_extract_taxonomy", "mp_extract_taxonomy,grouped_df_mpse", "mp_extract_taxonomy,grouped_df_mpse-method", "mp_extract_taxonomy,MPSE", "mp_extract_taxonomy,MPSE-method", "mp_extract_taxonomy,tbl_mpse", "mp_extract_taxonomy,tbl_mpse-method", "taxonomy", "taxonomy,grouped_df_mpse", "taxonomy,grouped_df_mpse-method", "taxonomy,MPSE", "taxonomy,MPSE-method", "taxonomy,tbl_mpse", "taxonomy,tbl_mpse-method" ] }, { "page": "theme_taxbar", "title": "theme_taxbar", "topics": [ "theme_taxbar" ] } ], "_readme": "https://github.com/bioc/MicrobiotaProcess/raw/HEAD/README.md", "_rundeps": [ "abind", "ape", "aplot", "base64enc", "Biobase", "BiocGenerics", "Biostrings", "bslib", "cachem", "cli", "cluster", "codetools", "coin", "cpp11", "crayon", "data.table", "DelayedArray", "digest", "dplyr", "dtplyr", "evaluate", "farver", "fastmap", "fontawesome", "fontBitstreamVera", "fontLiberation", "fontquiver", "foreach", "fs", "gdtools", "generics", "GenomicRanges", "ggfun", "ggiraph", "ggnewscale", "ggplot2", "ggplotify", "ggrepel", "ggsignif", "ggstar", "ggtree", "ggtreeExtra", "glue", "gridExtra", "gridGraphics", "gtable", "highr", "htmltools", "htmlwidgets", "IRanges", "isoband", "iterators", "jquerylib", "jsonlite", "knitr", "labeling", "lattice", "lazyeval", "libcoin", "lifecycle", "magrittr", "MASS", "Matrix", "MatrixGenerics", "matrixStats", "memoise", "mgcv", "mime", "modeltools", "multcomp", "mvtnorm", "nlme", "patchwork", "permute", "pillar", "pkgconfig", "plyr", "purrr", "R6", "rappdirs", "RColorBrewer", "Rcpp", "rlang", "rmarkdown", "S4Arrays", "S4Vectors", "S7", "sandwich", "sass", "scales", "Seqinfo", "SparseArray", "stringi", "stringr", "SummarizedExperiment", "survival", "systemfonts", "TH.data", "tibble", "tidyr", "tidyselect", "tidytree", "tinytex", "treeio", "utf8", "vctrs", "vegan", "viridisLite", "withr", "xfun", "XVector", "yaml", "yulab.utils", "zoo" ], "_vignettes": [ { "source": "MicrobiotaProcess.Rmd", "filename": "MicrobiotaProcess.html", "title": "Introduction to MicrobiotaProcess", "author": "| Shuangbin Xu and GuangChuang Yu | School of Basic Medical Sciences, Southern Medical University", "engine": "knitr::rmarkdown", "headings": [ "1. Anatomy of a MPSE", "2. Overview of the design of MicrobiotaProcess package", "3. MicrobiotaProcess profiling", "3.1 bridges other tools", "3.2 alpha diversity analysis", "3.3 calculate alpha index and visualization", "3.4 The visualization of taxonomy abundance", "3.5 Beta diversity analysis", "3.5.1 The distance between samples or groups", "3.5.2 The PCoA analysis", "3.5.3 Hierarchical cluster analysis", "3.6 Biomarker discovery", "4. Need helps?", "5. Session information", "6. References" ], "created": "2019-08-11 14:32:45", "modified": "2026-04-02 03:29:40", "commits": 12 } ], "_score": 9.191612495036614, "_indexed": true, "_nocasepkg": "microbiotaprocess", "_universes": [ "bioc", "xiangpin", "yulab-smu" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.25.0", "date": "2026-11-04T13:46:39.000Z", "distro": "resolute", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/aa4948740e265daa3d205cba8b83f9df06e3dd3f4fe8f32c64e4ca026b1bea09", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.6.2", "os": "linux", "version": "1.25.0", "date": "2026-11-04T13:45:31.000Z", "distro": "resolute", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/b3ea6b58f26310a82552a4539e39980f79a46d39fdb1054df072e93579f74d76", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.5.3", "os": "mac", "version": "1.25.0", "date": "2026-11-04T13:44:44.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/1163d3ed0e19b8367af31629934a16b4b09f0d00d5d87155ac62aef33863d9c4", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.6.2", "os": "mac", "version": "1.25.0", "date": "2026-11-04T13:45:26.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/7d16cf159119b7f40ed1ce49e0179f814d2f247e4ede8efaab1f19d3ff5ece85", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.6.0", "os": "wasm", "version": "1.25.0", "date": "2026-11-04T13:45:00.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/3738e8ad56e2b430384e02640f1a0b2616b472755010427f0ad963e3c0a4d199", "status": "success", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.7.0", "os": "win", "version": "1.25.0", "date": "2026-11-04T13:43:23.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/64533bcf470efd002d995e7bbce0774a267940fa813b3cd1e8e1a105c7062ec6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.5.3", "os": "win", "version": "1.25.0", "date": "2026-11-04T13:43:50.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/439e2d30492677db95102171e5da75505a835d675d18d2653a6b8a7212a29ad9", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" }, { "r": "4.6.2", "os": "win", "version": "1.25.0", "date": "2026-11-04T13:43:26.000Z", "commit": "29492fac06e55c67d2bd49947f79cc398f426e69", "fileid": "https://r2.ropensci.org/194c9d897fe66ca5e2c612841383e195e85667bb57dfa5169549205ca8211df8", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/41839231477" } ] }