data.frame class; The feature abundance table; rownames are features (e.g. OTUs/ASVs/species/genes); column names are samples.

default NULL; data.frame; The sample information table; rownames are samples; columns are sample metadata; If not provided, the function can generate a table automatically according to the sample names in otu_table.

default NULL; data.frame class; The taxonomic information table; rownames are features; column names are taxonomic classes.

default NULL; phylo class; The phylogenetic tree that must be read with the read.tree function of ape package.

default NULL; DNAStringSet, list or DNAbin class; The representative sequences of OTUs/ASVs. The sequences should be read with the readDNAStringSet function in Biostrings package (DNAStringSet class), read.fasta function in seqinr package (list class), or read.FASTA function in ape package (DNAbin class).

default FALSE; Whether tidy the data in the microtable object automatically. If TRUE, the function can invoke the tidy_dataset function.

default c("mitochondria", "chloroplast"); filter mitochondria and chloroplast, or others as needed.

default 0; the relative abundance threshold, such as 0.0001.

default 1; the occurrence frequency threshold. For example, the number 2 represents filtering the feature that occurs less than 2 times. A number smaller than 1 is also allowable. For instance, the number 0.1 represents filtering the feature that occurs in less than 10% samples.

default TRUE; whether include the feature with the threshold.

default FALSE; whether apply this function to taxa_abund list. FALSE means using this function for otu_table

default c("rarefy", "SRS")[1]; "rarefy" represents the classical resampling like rrarefy function of vegan package. "SRS" is scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) <DOI:10.7717/peerj.9593>.

default NULL; libray size. If not provided, use the minimum number across all samples. For "SRS" method, this parameter is passed to Cmin parameter of SRS function of SRS package.

parameters pass to norm function of trans_norm class.

default FALSE; if TRUE, only basic data in microtable object is trimmed. Otherwise, all data, including taxa_abund, alpha_diversity and beta_diversity, are all trimed.

default "OTU"; The name of the column added in the tax_table.

default "ASV_"; the prefix of new names; new names will be newname_prefix + numbers according to the order of row names in otu_table.

a column name in sample_table of microtable object.

default "Genus"; the specific rank in tax_table.

default "basic_files"; directory to save the tables, phylogenetic tree and sequences in microtable object. It will be created if not found.

default ","; the field separator string, used to save tables. Same with sep parameter in write.table function. default ',' correspond to the file name suffix 'csv'. The option '\t' correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'.

parameters passed to write.table.

default NULL; numeric vector (column sequences) or character vector (column names of microtable$tax_table); applied to select columns to calculate abundances according to ordered hierarchical levels. This parameter is very useful when only part of the columns are needed to calculate abundances.

default TRUE; if TRUE, relative abundance is used; if FALSE, absolute abundance (i.e. raw values) will be summed.

default "|"; the symbol to merge and concatenate taxonomic names of different levels.

default FALSE; if TRUE, split the rows to multiple rows according to one or more columns in tax_table when there is multiple mapping information.

default "&"; Separator delimiting collapsed values; only available when split_group = TRUE.

default NULL; one column name used for the splitting in tax_table for each abundance calculation; only available when split_group = TRUE. If not provided, the function will split each column that containing the split_by character.

default "&&"; special character that will be used forcibly to split multiple mapping information in tax_table by default no matter split_group setting.

default "taxa_abund"; directory to save the taxonomic abundance files. It will be created if not found.

default FALSE; Whether merge all tables into one. The merged file format is generally called 'mpa' style.

default FALSE; Whether remove unclassified taxa in which the name ends with '__' generally.

default "__$"; The pattern searched through the merged taxonomic names. See also pattern parameter in grepl function. Only available when rm_un = TRUE. The default "__$" means removing the names end with '__'.

default ","; the field separator string. Same with sep parameter in write.table function. default ',' correspond to the file name suffix 'csv'. The option '\t' correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'.

parameters passed to write.table.

default NULL; one or more indexes in c("Observed", "Coverage", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher", "Pielou"); The default NULL represents that all the measures are calculated. 'Shannon', 'Simpson' and 'InvSimpson' are calculated based on vegan::diversity function; 'Chao1' and 'ACE' depend on the function vegan::estimateR. 'Fisher' index relies on the function vegan::fisher.alpha. "Observed" means the observed species number in a community, i.e. richness. "Coverage" represents good's coverage. It is defined:

Coverage = 1 - \frac{f1}{n}

where n is the total abundance of a sample, and f1 is the number of singleton (species with abundance 1) in the sample. "Pielou" denotes the Pielou evenness index. It is defined:

J = \frac{H'}{\ln(S)}

where H' is Shannon index, and S is the species number.

default FALSE; whether Faith's phylogenetic diversity is calculated. The calculation depends on the function picante::pd. Note that the phylogenetic tree (phylo_tree object in the data) is required for PD.

default "alpha_diversity"; directory name to save the alpha_diversity.csv file.

default NULL; a character vector with one or more elements; c("bray", "jaccard") is used when method = NULL; See the method parameter in vegdist function for more available options, such as 'aitchison' and 'robust.aitchison'.

default FALSE; whether UniFrac indexes (weighted and unweighted) are calculated. Phylogenetic tree is necessary when unifrac = TRUE.

default FALSE; Whether convert abundance to binary data (presence/absence).

default TRUE; Whether forcibly convert abundance to binary data (presence/absence) when method = "jaccard". The reason for this setting is that the Jaccard metric is commonly used for binary data. If force_jaccard_binary = FALSE is set, the conversion will not be enforced, but will instead be based on the setting of the binary parameter.

parameters passed to vegdist function of vegan package.

default "beta_diversity"; directory name to save the beta diversity matrix files.

Whether to make a deep clone.

default NULL; the object of microtable class.

default "Phylum"; taxonomic level, i.e. a column name in tax_table of the input object. The function extracts the abundance from the taxa_abund list according to the names in the list. If the taxa_abund list is NULL, the function can automatically calculate the relative abundance to generate taxa_abund list.

default 0; the mean relative abundance threshold for filtering the taxa with low abundance.

default 10; how many taxa are selected to use. Taxa are ordered by abundance from high to low. This parameter does not conflict with the parameter show. Both can be used. ntaxa = NULL means the parameter will be invalid.

default NULL; calculate mean abundance for each group. Select a column name in microtable$sample_table.

default FALSE; only available when groupmean parameter is provided; Whether output more statistics for each group, including min, max, median and quantile; Thereinto, quantile25 and quantile75 denote 25% and 75% quantiles, respectively.

default TRUE; whether delete the taxonomy lineage in front of the target level.

default TRUE; whether delete the prefix of taxonomy, such as "g__".

default NULL; character string; available when delete_taxonomy_prefix = T; default NULL represents using the "letter+__", e.g. "k__" for Phylum level; Please provide the customized prefix when it is not standard, otherwise the program can not correctly recognize it.

default TRUE; whether show the abundance percentage. If TRUE, the abundance data will be multiplied by 100.

default NULL; character vector; input taxa names to select some taxa.

default NULL; a taxonomic rank, such as "Phylum", used to add the taxonomic information of higher level. It is required for the legend with nested taxonomic levels in the bar plot or the higher taxonomic level in facets of y axis in the heatmap.

default NULL; an integer, used to fix the number of selected abundant taxa in each taxon from higher taxonomic level. If the total number under one taxon of higher level is less than the high_level_fix_nsub, the total number will be used. When high_level_fix_nsub is provided, the taxa number of higher level is calculated as: ceiling(ntaxa/high_level_fix_nsub). Note that ntaxa means either the parameter ntaxa or the taxonomic number obtained by filtering according to the show parameter.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the bars.

default TRUE; Whether the bar shows all the features (including 'Others'). Default TRUE means total abundance are summed to 1 or 100 (percentage). FALSE means 'Others' will not be shown.

default "grey90"; the color for "Others" taxa.

default NULL; a character vector for the facet; group column name of sample_table, such as, "Group"; If multiple facets are needed, please provide ordered names, such as c("Group", "Type"). The latter should have a finer scale than the former one; Please adjust the facet orders in the plot by assigning factors in sample_table before creating trans_abund object or assigning factors in the data_abund table of trans_abund object. When multiple facets are used, please first install package ggh4x using the command install.packages("ggh4x").

default NULL; vector; used to order the sample names in x axis; must be the samples vector, such as c("S1", "S3", "S2").

NULL; a character string; a column name of sample_table in dataset; used to show the sample names in x axis.

default NULL; bar width, see width in geom_bar.

default FALSE; whether add alluvium plot. If TRUE, please first install ggalluvial package.

default FALSE; whether order samples by the clustering.

default FALSE; whether add clustering plot. If clustering_plot = TRUE, clustering will be also TRUE in any case for the clustering.

default 0.2, the dendrogram plot width; available when clustering_plot = TRUE.

default "grey95"; facet background color.

default 11; facet text size.

default FALSE; whether use italic in legend.

default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap;

default 10; x axis text size.

default TRUE; whether retain x text.

default TRUE; whether retain x title.

default 17; y axis title size.

default FALSE; whether flip cartesian coordinates so that horizontal becomes vertical, and vertical becomes horizontal.

default FALSE; whether use nested legend. Need ggnested package to be installed (https://github.com/gmteunisse/ggnested). To make it available, please assign high_level parameter when creating the object.

default FALSE; whether add 'Others' (all the unknown taxa) in each taxon of higher taxonomic level. Only available when ggnested = TRUE.

deprecated. Please use bar_full argument instead.

default rev(RColorBrewer::brewer.pal(n = 11, name = "RdYlBu")); colors palette for the plotting.

default NULL; a character vector for the facet; a group column name of sample_table, such as, "Group"; If multiple facets are needed, please provide ordered names, such as c("Group", "Type"). The latter should have a finer scale than the former one; Please adjust the facet orders in the plot by assigning factors in sample_table before creating trans_abund object or assigning factors in the data_abund table of trans_abund object. When multiple facets are used, please first install package ggh4x using the command install.packages("ggh4x").

default "y"; By default, the labels in facets are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both". When the high_level is found in the object, the function will generate facets for the higher taxonomy in y axis. So the default "y" of the parameter is to make the visualization better when high_level is found. This parameter will be passed to the switch parameter in ggplot2::facet_grid or ggh4x::facet_nested function.

NULL; a character string; a column name of sample_table used to show the sample names in x axis.

default NULL; vector; used to order the sample names in x axis; must be the samples vector, such as, c("S1", "S3", "S2").

default TRUE; whether retain the tile margin.

default FALSE; whether plot the number in heatmap.

default 4; If plot_numbers TRUE, text size in plot.

default NULL; The legend breaks.

default "white"; If withmargin TRUE, use this as the margin color.

default "log10"; color scale.

default .01; the minimum abundance percentage in plot.

default NULL; the maximum abundance percentage in plot, NULL reprensent the max percentage.

default 11; facet text size.

default TRUE; whether retain x text.

default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap;

default 10; x axis text size.

default 11; y axis text size.

default TRUE; whether retain x title.

default TRUE; whether remove grid lines.

default "% Relative\nAbundance"; legend title text.

default FALSE; whether use pheatmap package to plot the heatmap.

paremeters pass to pheatmap when pheatmap = TRUE.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the box.

default NULL; a column name of sample table to show abundance across groups.

default FALSE; whether show points in plot.

default "black"; If show_point TRUE; use the color

default 3; If show_point TRUE; use the size

default .3; If show_point TRUE; use the transparency.

default FALSE; Whether rotate plot.

default TRUE; Whether fill the box with colors.

default "grey95"; The middle line color.

default 1; The middle line size.

default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap;

default 10; x axis text size.

default 17; y axis title size.

parameters pass to geom_boxplot function.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the points and lines.

default TRUE; TRUE: the errorbar is mean±se; FALSE: the errorbar is mean±sd.

default position_dodge(0.1); Position adjustment, either as a string (such as "identity"), or the result of a call to a position adjustment function.

default 1; errorbar line size.

default 0.1; errorbar width.

default 3; point size for taxa.

default 0.8; point transparency.

default 0.8; line size.

default 0.8; line transparency.

default 1; an integer; line type.

default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap;

default 10; x axis text size.

default 17; y axis title size.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for each section.

default 1; how many rows in the plot.

default 11; sample title size.

default FALSE; Whether add the percentage label in each section of pie chart.

default FALSE; whether use italic in legend.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the donut.

default TRUE; whether show the percentage label.

default 1; how many rows in the plot.

default FALSE; whether use italic in legend.

parameters passed to ggpubr::ggdonutchart.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for samples.

parameters passed to ggradar::ggradar function except group.colours parameter.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the samples.

default 4; The size of legend guide for color.

Whether to make a deep clone.

microtable object.

default NULL; a column name of sample_table in the input microtable object used for the statistics across groups.

default NULL; a column name of sample_table used to perform the differential test among groups (from group parameter) for each group (from by_group parameter) separately.

default NULL; a column name of sample_table used to perform paired T test or paired Wilcoxon test for the paired data, such as continuous sampling of individual animals or plant compartments for different plant species (ID). So by_ID in sample_table should be the smallest unit of sample collection without any repetition in it. When the by_ID parameter is provided, the function can automatically perform paired test, and no more parameters is required.

default NULL; a column name of sample_table or a vector with sample names. If provided, sort samples using factor.

default NULL; character vector; If NULL, all indexes will be used; see names of microtable$alpha_diversity, e.g. c("Observed", "Chao1", "Shannon").

default "KW"; see the following available options:

'KW'

Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons <10.1080/00401706.1964.10490181> based on dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum Test for all paired groups When by_ID parameter is provided in creating the object of the class, paired Wilcoxon test will be performed.

't.test'

Student's t-Test for all paired groups. When by_ID parameter is provided in creating the object of the class, paired t-test will be performed.

'anova'

Variance analysis. For one-way anova, the default post hoc test is Duncan's new multiple range test. Please use anova_post_test parameter to change the post hoc method. For multi-way anova, Please use formula parameter to specify the model and see aov for more details

'scheirerRayHare'

Scheirer-Ray-Hare test (nonparametric test) for a two-way factorial experiment; see scheirerRayHare function of rcompanion package

'lm'

Linear Model based on the lm function

'lme'

Linear Mixed Effect Model based on the lmerTest package

'betareg'

Beta Regression for Rates and Proportions based on the betareg package

'glmm'

Generalized linear mixed model (GLMM) based on the glmmTMB package. A family function can be provided using parameter passing, such as: family = glmmTMB::lognormal(link = "log")

'glmm_beta'

Generalized linear mixed model (GLMM) with a family function of beta distribution. This is an extension of the GLMM model in 'glmm' option. The only difference is in glmm_beta the family function is fixed with the beta distribution function, facilitating the fitting for proportional data (ranging from 0 to 1). The link function is fixed with "logit".

default NULL; applied to two-way or multi-factor analysis when method is "anova", "scheirerRayHare", "lm", "lme", "betareg" or "glmm"; specified set for independent variables, i.e. the latter part of a general formula, such as 'block + N*P*K'.

default "fdr" (for "KW", "wilcox", "t.test" methods) or "holm" (for "KW_dunn"); P value adjustment method; For method = 'KW', 'wilcox' or 't.test', please see method parameter of p.adjust function for available options; For method = 'KW_dunn', please see dunn.test::p.adjustment.methods for available options.

default TRUE; For method = 'KW_dunn', TRUE denotes significances are presented by letters; FALSE means significances are shown by asterisk for paired comparison.

default 0.05; Significant level; used for generating significance letters when method is 'anova' or 'KW_dunn'.

default "duncan.test". The post hoc test method for one-way anova. The default option represents the Duncan's new multiple range test. Other available options include "LSD.test" (LSD post hoc test) and "HSD.test" (HSD post hoc test). All those are the function names from agricolae package.

default FALSE; whether conduct Levene's Test for equality of variances. Only available for one-way anova. Significant P value means the variance among groups is not equal.

default FALSE; whether return the original "lm", "lmer" or "glmm" model list in the object.

parameters passed to kruskal.test (when method = "KW") or wilcox.test function (when method = "wilcox") or dunnTest function of FSA package (when method = "KW_dunn") or agricolae::duncan.test/agricolae::LSD.test/agricolae::HSD.test (when method = "anova", one-way anova) or rcompanion::scheirerRayHare (when method = "scheirerRayHare") or stats::lm (when method = "lm") or lmerTest::lmer (when method = "lme") or betareg::betareg (when method = "betareg") or glmmTMB::glmmTMB (when method = "glmm").

default "ggboxplot"; plot type; available options include "ggboxplot", "ggdotplot", "ggviolin", "ggstripchart", "ggerrorplot", "errorbar" and "barerrorbar". The options starting with "gg" are function names coming from ggpubr package. All those methods with ggpubr package use the data_alpha table in the object. "errorbar" represents Mean±SD or Mean±SE plot based on ggplot2 package by invoking the data_stat table in the object. "barerrorbar" denotes "bar plot + error bar". It is similar with "errorbar" and has a bar plot.

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.

default "Shannon"; one alpha diversity index in the object.

default NULL; group name used for the plot.

default NULL; add another plot element; passed to the add parameter of the function (e.g., ggboxplot) from ggpubr package when plot_type starts with "gg" (functions coming from ggpubr package).

default TRUE; whether add significance label using the result of cal_diff function, i.e. object$res_diff; This is manily designed to add post hoc test of anova or other significances to make the label mapping easy.

default "Significance"; select a colname of object$res_diff for the label text when 'Letter' is not in the table, such as 'P.adj' or 'Significance'.

default 3.88; the size of text in added label.

default 4; reserved decimal places when the parameter add_sig_label use numeric column, like 'P.adj'.

default FALSE; whether order x axis by the means of groups from large to small.

default 0.1; the y axis value from which to add the significance asterisk label; the default 0.1 means max(values) + 0.1 * (max(values) - min(values)).

default 0.05; the increasing y axia space to add the label (asterisk or letter); the default 0.05 means 0.05 * (max(values) - min(values)); this parameter is also used to label the letters of anova result with the fixed space.

default 30; number (e.g. 30). Angle of text in x axis.

default 13; x axis text size. NULL means the default size in ggplot2.

default 17; y axis title size.

default 0.9; the bar width when plot_type = "barerrorbar".

default 0.8; the alpha of bar color when plot_type = "barerrorbar".

default 0.9; the dodge width used in position_dodge function of ggplot2 package when plot_type is "errorbar" or "barerrorbar".

default TRUE; TRUE: the errorbar is mean±se; FALSE: the errorbar is mean±sd. Available when plot_type is "errorbar" or "barerrorbar".

default 1; errorbar size. Available when plot_type is "errorbar" or "barerrorbar".

default 0.2; errorbar width. Available when plot_type is "errorbar" or "barerrorbar" and by_group is NULL.

default TRUE; whether add point for mean. Available when plot_type is "errorbar" or "barerrorbar" and by_group is NULL.

default FALSE; whether use black for the color of errorbar when plot_type is "errorbar" or "barerrorbar".

default 3; point size for taxa. Available when plot_type is "errorbar" or "barerrorbar".

default 0.8; point transparency. Available when plot_type is "errorbar" or "barerrorbar".

default FALSE; whether add line. Available when plot_type is "errorbar" or "barerrorbar".

default 0.8; line size when add_line = TRUE. Available when plot_type is "errorbar" or "barerrorbar".

default 2; an integer; line type when add_line = TRUE. The available case is same with line_size.

default "grey50"; line color when add_line = TRUE. Available when by_group is NULL. Other available case is same with line_size.

default 0.5; line transparency when add_line = TRUE. The available case is same with line_size.

default "P.unadj"; the column of res_diff table for the cell of heatmap when formula with multiple factors is found in the method.

default "Significance"; the column of res_diff for the significance label of heatmap.

default "Factors"; the column of res_diff for the x axis of heatmap.

default "Taxa"; the column of res_diff for the y axis of heatmap.

default "P value"; legend title of heatmap.

default 2; Significance label position in the coefficient point and errorbar plot. The formula is Estimate + coefplot_sig_pos * Std.Error. This plot is used when there is only one measure found in the table, and 'Estimate' and 'Std.Error' are both in the column names (such as for lm and lme methods). The x axis is 'Estimate', and y axis denotes 'Factors'. When coefplot_sig_pos is a negative value, the label is in the left of the errorbar. Errorbar size and width in the coefficient point plot can be adjusted with the parameters errorbar_size and errorbar_width. Point size and alpha can be adjusted with parameters point_size and point_alpha. The significance label size can be adjusted with parameter add_sig_text_size. Furthermore, the vertical line around 0 can be adjusted with parameters line_size, line_type, line_color and line_alpha.

parameters passing to ggpubr::ggboxplot function (or other functions shown by plot_type parameter when it starts with "gg") or plot_cor function in trans_env class for the heatmap of multiple factors when formula is found in the res_diff of the object.

Whether to make a deep clone.

an object of microtable class.

default NULL; a matrix name stored in microtable$beta_diversity list, such as "bray" or "jaccard", or a customized matrix; used for ordination, manova, group distance comparision, etc.; Please see cal_betadiv function of microtable class for more details.

default NULL; a column name of sample_table in the input dataset; group information will be used for manova, betadisper or distance comparision.

default "PCoA"; "PCoA", "NMDS", "PCA", "DCA", "PLS-DA" or "OPLS-DA". PCoA: principal coordinates analysis; NMDS: non-metric multidimensional scaling, PCA: principal component analysis; DCA: detrended correspondence analysis; PLS-DA: partial least squares discriminant analysis; OPLS-DA: orthogonal partial least squares discriminant analysis. For the methods details, please refer to the papers <doi:10.1111/j.1574-6941.2007.00375.x> (for PCoA, NMDS, PCA and DCA) and <doi:10.1186/s12859-019-3310-7> (for PLS-DA or OPLS-DA).

default 2; dimensions in the result. For the method option "PCA", "PCoA" or "DCA", the corresponding dimension information will be selected from the original model based on this parameter.. For all the dimension information, please refer to model in the results. For the method option "NMDS", this argument will be passed to the k parameter in the vegan::metaMDS function.

default NULL; available for PCA, DCA or NMDS (NMDS_matrix = TRUE). Default NULL means using the otu_table in the microtable object. For other options, please provide the taxonomic rank names in tax_table, such as "Phylum" or "Genus". In such cases, the data will be merged according to the provided taxonomic levels to generated a new abundance table.

default TRUE; For the NMDS method, whether use a distance matrix as input like PCoA. If it is FALSE, the input will be the abundance table like PCA.

default FALSE; whether species abundance will be square root transformed; only available when method is "PCA" or "DCA". For method "NMDS" and NMDS_matrix = FALSE, please set the autotransform parameter, which will be passed to vegan::metaMDS function directly.

default FALSE; whether species loading in PCA, DCA or NMDS (NMDS_matrix = FALSE) is scaled.

default 0.8; the ratio to scale up the loading; multiply by the maximum distance between samples and origin. Only available when scale_species = TURE.

default NA; number of orthogonal components (for OPLS-DA only). Default NA means the number of orthogonal components is automatically computed. Please also see orthoI parameter in opls function of ropls package.

deprecated. Please use method argument instead.

parameters passed to vegan::rda function when method = "PCA", or vegan::decorana function when method = "DCA", or ape::pcoa function when method = "PCoA", or vegan::metaMDS function when method = "NMDS", or ropls::opls function when method = "PLS-DA" or method = "OPLS-DA" .

default "point"; one or more elements of "point", "ellipse", "chull" and "centroid".

'point'

add sample points

'ellipse'

add confidence ellipse for points of each group

'chull'

add convex hull for points of each group

'centroid'

add centroid line for points in each group

default c(1, 2); selected axis for the visualization; must be numeric vector. The maximum value must not exceed the parameter ncomp in the cal_ordination function.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for different groups.

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector for point shape types of groups, see ggplot2 tutorial.

default NULL; a colname of sample_table to assign colors to different groups in plot.

default NULL; a colname of sample_table to assign shapes to different groups in plot.

default NULL; a vector used to order the groups in the legend of plot.

default NULL; a column name in sample_table; If provided, show the point name in plot.

default 3; point size when "point" is in plot_type parameter. point_size can also be a variable name in sample_table, such as "pH".

default .8; point transparency in plot when "point" is in plot_type parameter.

default 0.6; segment transparency in plot when "centroid" is in plot_type parameter.

default 1; segment size in plot when "centroid" is in plot_type parameter.

default 3; the line type related with centroid in plot when "centroid" is in plot_type parameter.

default TRUE; whether fill colors to the area of ellipse or chull.

default 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in plot_type parameter.

default .9; confidence level of ellipse when "ellipse" is in plot_type parameter.

default "t"; ellipse type when "ellipse" is in plot_type parameter; see type in stat_ellipse.

default c(1, 1); a numerical vector with two values used to represent the insertion position of the stress text. The first one denotes the x-axis, while the second one corresponds to the y-axis. The assigned position is determined by multiplying the respective value with the maximum point on the corresponding coordinate axis. Thus, the x-axis position is equal to max(points of x axis) * NMDS_stress_pos[1], and the y-axis position is equal to max(points of y axis) * NMDS_stress_pos[2]. Negative values can also be utilized for the negative part of the axis. NMDS_stress_pos = NULL denotes no stress text to show.

default ""; If NMDS_stress_pos is not NULL, this parameter can be used to add text in front of the stress value.

default FALSE; whether show the loading using arrow.

default 10; the number of taxa used for the loading. Only available when loading_arrow = TRUE.

default NULL; which level of taxonomic table will be used. Default NULL means using the taxa_level parameter in the previous cal_ordination function.

default "black"; the color of taxa text. Only available when loading_arrow = TRUE.

default "grey30"; the color of taxa arrow. Only available when loading_arrow = TRUE.

default 3; the size of taxa text. Only available when loading_arrow = TRUE.

default FALSE; whether show the prefix (e.g., g__) in the taxa text. Only available when loading_arrow = TRUE.

default FALSE; whether using italic for the taxa text. Only available when loading_arrow = TRUE.

default TRUE; TRUE represents test for all the groups, i.e. the overall test; FALSE represents test for all the paired groups.

default NULL; other specified group set for manova, such as "Group + Type" and "Group*Type". Please also see the formula parameter (only right-hand side) in adonis2 function of vegan package. The parameter manova_set has higher priority than manova_all parameter. If manova_set is provided; manova_all is disabled.

default NULL; a column name of sample_table used for manova. If NULL, search group variable stored in the object. Available when manova_set is not provided.

default NULL; one column name in sample_table; used to perform paired comparisions within each group. Only available when manova_all = FALSE and manova_set is not provided.

default "fdr"; p.adjust method; available when manova_all = FALSE; see method parameter of p.adjust function for available options.

default "terms"; same with the by parameter in adonis2 function of vegan package.

default TRUE; Whether automatically set the options for by parameter ("marginal" or "terms") when manova_set is provided. The primary reason for setting this parameter is that using marginal effects (also known as "Type III" effects) is more robust for unbalanced experimental designs. Since the option by = "margin" in the adonis2 function ignores main effects when interaction effects are present, we automatically set by = "margin" when there are no interaction effects, and set by = "terms" when interaction effects exist. If the user wants to use parameter by, please set by_auto_set = FALSE. Note that this parameter is only available when manova_set is provided.

default 999; same with the permutations parameter in adonis2 function of vegan package.

parameters passed to adonis2 function of vegan package.

default FALSE; whether perform paired test between any two combined groups from all the input groups.

default NULL; a column name of sample_table. If NULL, search group variable stored in the object.

default NULL; one column name in sample_table; used to perform paired comparisions within each group. Only available when paired = TRUE.

default "fdr"; p.adjust method; available when paired = TRUE; see method parameter of p.adjust function for available options.

default 999; same with the permutations parameter in anosim function of vegan package.

parameters passed to anosim function of vegan package.

parameters passed to betadisper function.

default TRUE; whether obtain distance table of paired samples within groups; if FALSE, obtain distances of paired samples between any two groups.

default NULL; one colname name of sample_table in microtable object. If provided, transform distances by the provided by_group parameter. This is especially useful for ordering and filtering values further. When within_group = TRUE, the result of by_group parameter is the format of paired groups. When within_group = FALSE, the result of by_group parameter is the format same with the group information in sample_table.

default NULL; a vector representing the ordered elements of group parameter; only useful when within_group = FALSE.

default TRUE; a character string to separate the group names after merging them into a new name.

default NULL; a column name of object$res_group_distance used for the statistics; If NULL, use the group inside the object.

default NULL; a column of object$res_group_distance used to perform the differential test among elements in group parameter for each element in by_group parameter. So by_group has a larger scale than group parameter. This by_group is very different from the by_group parameter in the cal_group_distance function.

default NULL; a column of object$res_group_distance used to perform paired t test or paired wilcox test for the paired data, such as the data of plant compartments for different plant species (ID). So by_ID should be the smallest unit of sample collection without any repetition in it.

parameters passed to cal_diff function of trans_alpha class.

default NULL; a vector used to order the groups in the plot.

parameters (except measure) passed to plot_alpha function of trans_alpha class.

default RColorBrewer::brewer.pal(8, "Dark2"); color palette for the text.

default NULL; beta diversity index; If NULL, using the measure when creating object

default NULL; if provided, use this group to assign color.

default NULL; if provided, use this as label.

Whether to make a deep clone.

an object of microtable class.

default "Genus"; character string or data.frame; a character string represents selecting the corresponding data from microtable$taxa_abund; data.frame denotes other customized input. See the following available options:

'Genus'

use Genus level table in microtable$taxa_abund, or other specific taxonomic rank, e.g., 'Phylum'. If an input level (e.g., ASV) is not found in the names of taxa_abund list, the function will use otu_table to calculate relative abundance of features.

'all'

use all the levels stored in microtable$taxa_abund.

other input

must be a data.frame object. It should have the same format with the tables in microtable$taxa_abund, i.e. rows are features; columns are samples with same names in sample_table.

default NULL; the response variable in sample_table of input microtable object.

default 1; the CPU thread used.

default 3/4; the ratio of the data used for the training.

parameters pass to preProcess function of caret package.

default 300; maximal number of importance source runs; passed to the maxRuns parameter in Boruta function of Boruta package.

default 0.01; p value passed to the pValue parameter in Boruta function of Boruta package.

default 4; repetition runs for the feature selection.

parameters pass to Boruta function of Boruta package.

default 'repeatedcv'; 'repeatedcv': Repeated k-Fold cross validation; see method parameter in trainControl function of caret package for available options.

default TRUE; should class probabilities be computed for classification models?; see classProbs parameter in caret::trainControl function.

default TRUE; see savePredictions parameter in caret::trainControl function.

parameters pass to trainControl function of caret package.

default "rf"; "rf": random forest; see method in train function of caret package for other options. For method = "rf", the tuneGrid is set: expand.grid(mtry = seq(from = 1, to = max.mtry))

default 2; for method = "rf"; maximum mtry used in the tuneGrid to do hyperparameter tuning to optimize the model.

default 500; for method = "rf"; Number of trees to grow. The default 500 is same with the ntree parameter in randomForest function in randomForest package. When it is a vector with more than one element, the function will try to optimize the model to select a best one, such as c(100, 500, 1000).

parameters pass to caret::train function.

default FALSE; whether calculate feature significance in 'rf' model using rfPermute package; only available for method = "rf" in cal_train function.

parameters pass to varImp function of caret package. If rf_feature_sig is TURE and train_method is "rf", the parameters will be passed to rfPermute function of rfPermute package.

default NULL; "MeanDecreaseAccuracy" (Default) or "MeanDecreaseGini" for random forest classification; "%IncMSE" (Default) or "IncNodePurity" for random forest regression; Only available when rf_feature_sig = TRUE in function cal_feature_imp, which generate "MeanDecreaseGini" (and "MeanDecreaseAccuracy") or "%IncMSE" (and "IncNodePurity") in the column names of res_feature_imp; Function can also generate "Significance" according to the p value.

default FALSE; whether show the features with different significant groups; Only available when "Significance" is found in the data.

parameters pass to plot_diff_bar function of trans_diff package.

default NULL; see positive parameter in confusionMatrix function of caret package; If positive_class is NULL, use the first group in data as the positive class automatically.

default TRUE; whether plot the confusion matrix.

default TRUE; whether plot the statistics.

default "pred"; 'pred' or 'train'; 'pred' represents using prediction results; 'train' represents using training results.

default c("ROC", "PR")[1]; 'ROC' represents ROC (Receiver Operator Characteristic) curve; 'PR' represents PR (Precision-Recall) curve.

default "all"; 'all' represents all the classes in the model; 'add' represents all adding micro-average and macro-average results, see https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html; other options should be one or more class names, same with the names in Group column of res_ROC$res_roc from cal_ROC function.

default RColorBrewer::brewer.pal(8, "Dark2"); colors used in the plot.

default TRUE; whether add AUC in the legend.

default FALSE; If TRUE, show the method in the legend though only one method is found.

parameters pass to geom_path function of ggplot2 package.

parameters pass to caretList function of caretEnsemble package.

parameters pass to resamples function of caret package.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for the box.

parameters pass to geom_boxplot function of ggplot2 package.

Whether to make a deep clone.

default NULL; microtable object.

default "lefse". Some methods (e.g., "lefse", "KW", "wilcox", "anova", "lm", "betareg", "glmm" and "glmm_beta") invoke the taxa_abund list (generally relative abundance data) of input microtable object for the analysis. Some (e.g., "metastat", "metagenomeSeq", "ALDEx2_t", "DESeq2", "edgeR", "ancombc2" and "linda") use the otu_table of input microtable object for the analysis. Available options include:

'lefse'

LEfSe method based on Segata et al. (2011) <doi:10.1186/gb-2011-12-6-r60>

'rf'

random forest and non-parametric test method based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>

'metastat'

Metastat method for all paired groups based on White et al. (2009) <doi:10.1371/journal.pcbi.1000352>

'metagenomeSeq'

zero-inflated log-normal model-based differential test method from metagenomeSeq package.

'KW'

KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons when group number > 2; see dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum and Signed Rank Tests for all paired groups

't.test'

Student's t-Test for all paired groups

'anova'

ANOVA for one-way or multi-factor analysis; see cal_diff function of trans_alpha class

'scheirerRayHare'

Scheirer Ray Hare test for nonparametric test used for a two-way factorial experiment; see scheirerRayHare function of rcompanion package

'lm'

Linear Model based on the lm function

'ALDEx2_t'

runs Welch's t and Wilcoxon tests with ALDEx2 package; see also the test parameter in ALDEx2::aldex function; ALDEx2 uses the centred log-ratio (clr) transformation and estimates per-feature technical variation within each sample using Monte-Carlo instances drawn from the Dirichlet distribution; Reference: <doi:10.1371/journal.pone.0067019> and <doi:10.1186/2049-2618-2-15>; require ALDEx2 package to be installed (https://bioconductor.org/packages/release/bioc/html/ALDEx2.html)

'ALDEx2_kw'

runs Kruskal-Wallace and generalized linear model (glm) test with ALDEx2 package; see also the test parameter in ALDEx2::aldex function.

'DESeq2'

Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution based on the DESeq2 package.

'edgeR'

The exactTest method of edgeR package is implemented.

'ancombc2'

Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) based on the ancombc2 function from ANCOMBC package. If the fix_formula parameter is not provided, the function can automatically assign it by using group parameter. For this method, the group parameter is directly passed to the group parameter of ancombc2 function. Reference: <doi:10.1038/s41467-020-17041-7><10.1038/s41592-023-02092-7>; Require ANCOMBC package to be installed (https://bioconductor.org/packages/release/bioc/html/ANCOMBC.html)

'linda'

Linear Model for Differential Abundance Analysis of High-dimensional Compositional Data based on the linda function of MicrobiomeStat package. For linda method, please provide either the group parameter or the formula parameter. When the formula parameter is provided, it should start with '~' as it is directly used by the linda function. If the group parameter is used, the prefix '~' is not necessary as the function can automatically add it. The parameter feature.dat.type = 'count' has been fixed. Other parameters can be passed to the linda function. Reference: <doi:10.1186/s13059-022-02655-5>

'maaslin2'

finding associations between metadata and potentially high-dimensional microbial multi-omics data based on the Maaslin2 package. Using this option can invoke the trans_env$cal_cor function with method = "maaslin2".

'betareg'

Beta Regression based on the betareg package. Please see the beta_pseudo parameter for the use of pseudo value when there is 0 or 1 in the data

'lme'

Linear Mixed Effect Model based on the lmerTest package. In the return table, the significance of fixed factors are tested by function anova. The significance of 'Estimate' in each term of fixed factors comes from the model.

'glmm'

Generalized linear mixed model (GLMM) based on the glmmTMB package. The formula and family parameters are needed. Please refer to glmmTMB package to select the family function, e.g. family = glmmTMB::lognormal(link = "log"). The usage of formula is similar with that in 'lme' method. For more available parameters, please see glmmTMB::glmmTMB function and use parameter passing. In the result, Conditional R2 and Marginal R2 represent the variance explained by both fixed and random effects and the variance explained by fixed effects, respectively. For more details on R2 calculation, please refer to the article <doi: 10.1098/rsif.2017.0213>. The significance of fixed factors are tested by Chi-square test from function car::Anova. The significance of 'Estimate' in each term of fixed factors comes from the model.

'glmm_beta'

Generalized linear mixed model with a family function of beta distribution, developed for the relative abundance (ranging from 0 to 1) of taxa specifically. This is an extension of the GLMM model in 'glmm' option. The only difference is in glmm_beta the family function is fixed with the beta distribution function, i.e. family = glmmTMB::beta_family(link = "logit"). Please see the beta_pseudo parameter for the use of pseudo value when there is 0 or 1 in the data

default NULL; sample group used for the comparision; a colname of input microtable$sample_table; It is necessary when method is not "anova" or method is "anova" but formula is not provided. Once group is provided, the return res_abund will have mean and sd values for group.

default "all"; 'all' represents using abundance data of all taxonomic ranks; For testing at a specific rank, provide taxonomic rank name, such as "Genus". If the provided taxonomic name is neither 'all' nor a colname in tax_table of input dataset (e.g., "ASV"), the function will use the features in input microtable$otu_table automatically. Note that a specific level (e.g., "ASV") should be provided for method: "metastat", "metagenomeSeq", "ALDEx2_t", "DESeq2", "edgeR", "ancombc2", "linda", "maaslin2".

default 0; the abundance threshold, such as 0.0005 when the input is relative abundance; only available when method != "metastat". The features with abundances lower than filter_thres will be filtered.

default 0.05; significance threshold to select taxa when method is "lefse" or "rf"; or used to generate significance letters when method is 'anova' or 'KW_dunn' like the alpha parameter in cal_diff of trans_alpha class.

default "fdr"; p.adjust method; see method parameter of p.adjust function for other available options; "none" means disable p value adjustment; So when p_adjust_method = "none", P.adj is same with P.unadj.

default NULL; feature abundance transformation method in the class trans_norm, such as 'AST' for the arc sine square root transformation. Only available when method is one of "KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "betareg" and "lme".

default TRUE; whether remove unknown features that donot have clear classification information.

default NULL; sample sub group used for sub-comparision in lefse; Segata et al. (2011) <doi:10.1186/gb-2011-12-6-r60>.

default 10; sample numbers required in the subgroup test.

default FALSE; whether remove the features strictly in the sub-checking. FALSE means only removing the features that have different orders of medians across sub-groups with those across groups and the statistics are also significant. TRUE means removing the features that are not significant in one (or more) sub-test or have different orders of medians across sub-groups with those across groups.

default NULL; The significance threshold in the test for lefse sub-groups. NULL means it is same with alpha.

default 1000000; normalization value used in lefse to scale abundances for each level. A lefse_norm value < 0 (e.g., -1) means no normalization same with the LEfSe python version.

default 0.6667; sample number ratio used in each bootstrap for method = "lefse" or "rf".

default 30; bootstrap test number for method = "lefse" or "rf".

default 2; the type of feature importance in random forest when method = "rf". Same with type parameter in importance function of randomForest package. 1=mean decrease in accuracy (MeanDecreaseAccuracy), 2=mean decrease in node impurity (MeanDecreaseGini).

default NULL; a vector used for selecting the required groups for paired testing instead of all paired combinations across groups; Available when method is "metastat", "metagenomeSeq", "ALDEx2_t" or "edgeR".

default 1; Filter features to have at least 'counts' counts.; see the count parameter in MRcoefs function of metagenomeSeq package.

default c("wi.eBH", "kw.eBH"); which column of the final result is used as the significance asterisk assignment; applied to method = "ALDEx2_t" or "ALDEx2_kw"; the first element is provided to "ALDEx2_t"; the second is provided to "ALDEx2_kw"; for "ALDEx2_t", the available choice is "wi.eBH" (Expected Benjamini-Hochberg corrected P value of Wilcoxon test) and "we.eBH" (Expected BH corrected P value of Welch's t test); for "ALDEx2_kw"; for "ALDEx2_t", the available choice is "kw.eBH" (Expected BH corrected P value of Kruskal-Wallace test) and "glm.eBH" (Expected BH corrected P value of glm test).

default NULL; a column of sample_table used to perform the differential test among groups (group parameter) for each group (by_group parameter). So by_group has a higher level than group parameter. Same with the by_group parameter in trans_alpha class. Only available when method is one of c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare").

default NULL; a column of sample_table used to perform paired t test or paired wilcox test for the paired data, such as the data of plant compartments for different plant species (ID). So by_ID in sample_table should be the smallest unit of sample collection without any repetition in it. Same with the by_ID parameter in trans_alpha class.

default .Machine$double.eps; the pseudo value used when the parameter method is 'betareg' or 'glmm_beta'. As the beta distribution function limits 0 < response value < 1, a pseudo value will be added for the data that equal to 0. The data that equal to 1 will be replaced by 1/(1 + beta_pseudo).

parameters passed to cal_diff function of trans_alpha class when method is one of "KW", "KW_dunn", "wilcox", "t.test", "anova", "betareg", "lme", "glmm" or "glmm_beta"; passed to randomForest::randomForest function when method = "rf"; passed to ANCOMBC::ancombc2 function when method is "ancombc2" (except tax_level, global and fix_formula parameters); passed to ALDEx2::aldex function when method = "ALDEx2_t" or "ALDEx2_kw"; passed to DESeq2::DESeq function when method = "DESeq2"; passed to MicrobiomeStat::linda function when method = "linda"; passed to trans_env$cal_cor function when method = "maaslin2".

default 1:10; numeric vector; the sequences of taxa (1:n) selected in the plot; If n is larger than the number of total significant taxa, automatically use the total number as n.

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.

default NULL; character vector to provide taxa names. The taxa names should be same with the names shown in the plot, not the 'Taxa' column names in object$res_diff$Taxa.

default TRUE; whether use the simplified taxonomic name.

default TRUE; whether retain the taxonomic prefix.

default NULL; a vector to order groups, i.e. reorder the legend and colors in plot; If NULL, the function can first check whether the group column of sample_table is factor. If yes, use the levels in it. If provided, overlook the levels in the group of sample_table.

default FALSE; whether order the taxa in x axis by the means of abundances from large to small. If TRUE, all other factors in the data will become invalid.

default TRUE; whether flip cartesian coordinates so that horizontal becomes vertical, and vertical becomes horizontal.

default TRUE; whether add the significance label to the plot.

default 45; number (e.g. 45). Angle of text in x axis.

default 13; x axis text size. NULL means the default size in ggplot2. If coord_flip = TRUE, it represents the text size of the y axis.

default 17; y axis title size. If coord_flip = TRUE, it represents the title size of the x axis (i.e. "Relative abundance").

parameters passed to plot_alpha function of trans_alpha class.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for different groups.

default FALSE; whether match the colors to groups in order to fix the color in each group when part of groups are not shown in the plot. When color_group_map = TRUE, the group_order inside the object will be used as full groups set to guide the color extraction.

default 1:10; numeric vector; the taxa numbers used in the plot, i.e. 1:n.

default NULL; threshold value of indicators for selecting taxa, such as 3 for LDA score of LEfSe.

default NULL; this is used to select the paired group when multiple comparisions are generated; The input select_group must be one of object$res_diff$Comparison.

default FALSE; whether keep the taxonomic full lineage names.

default TRUE; whether retain the taxonomic prefix, such as "g__".

default NULL; a vector to order the legend and colors in plot; If NULL, the function can first determine whether the group column of microtable$sample_table is factor. If yes, use the levels in it. If provided, this parameter can overwrite the levels in the group of microtable$sample_table.

default TRUE; whether aggregate the features for each group.

default TRUE; whether display the features of two groups on opposite sides of the coordinate axes when there are only two groups in total.

default TRUE; whether flip cartesian coordinates so that horizontal becomes vertical, and vertical becomes horizontal.

default FALSE; whether add significance label (asterisk) above the bar.

default 0.1; the axis position (Value + add_sig_increase * max(Value)) from which to add the significance label; only available when add_sig = TRUE.

default 5; the size of added significance label; only available when add_sig = TRUE.

default 45; number ranging from 0 to 90; used to make x axis text generate angle to reduce text overlap; only available when coord_flip = FALSE.

default 10; text size of x axis.

default NULL; text size of y axis. NULL means default ggplot2 value.

deprecated. Please use ytext_size argument instead.

default "P.unadj"; the column of data for the cell of heatmap when formula with multiple factors is found in the method.

default "Significance"; the column of data for the significance label of heatmap.

default "Factors"; the column of data for the x axis of heatmap.

default "Taxa"; the column of data for the y axis of heatmap.

default "P value"; legend title of heatmap.

parameters passing to geom_bar for the bar plot or plot_cor function in trans_env class for the heatmap of multiple factors when formula is found in the method.

default RColorBrewer::brewer.pal(8, "Dark2"); color palette used in the plot.

default NULL; a vector to order the legend in plot; If NULL, the function can first check whether the group column of sample_table is factor. If yes, use the levels in it. If provided, this parameter can overwrite the levels in the group of sample_table. If the number of provided group_order is less than the number of groups in res_diff$Group, the function will select the groups of group_order automatically.

default 200; integer; The taxa number used in the background tree plot; select the taxa according to the mean abundance .

default NULL; The mean relative abundance used to filter the taxa with low abundance.

default NULL; integer; The feature number used in the plot; select the features according to the metric (method = "lefse" or "rf") from high to low.

default 4; the taxonomic level for marking the label with letters, root is the largest.

default NULL; character vector; The features to show in the plot with full label names, not the letters.

default FALSE; whether only use the the select features in the parameter select_show_labels.

default "|"; the seperate character in the taxonomic information.

default 0.2; numberic, size of branch.

default 0.2; shading of the color.

default 2; basic size for the clade label; please also see clade_label_size_add and clade_label_size_log.

default 5; added basic size for the clade label; see the formula in clade_label_size_log parameter.

default exp(1); the base of log function for added size of the clade label; the size formula: clade_label_size + log(clade_label_level + clade_label_size_add, base = clade_label_size_log); so use clade_label_size_log, clade_label_size_add and clade_label_size can totally control the label size for different taxonomic levels.

default 1; scale for the node size.

default 1; offset for the node size.

default 22; shape used in the annotation legend.

default 5; size used in the annotation legend.

Whether to make a deep clone.

the object of microtable Class.

default NULL; either numeric vector or character vector to select columns in microtable$sample_table, i.e. dataset$sample_table. This parameter should be used in the case that all the required environmental data is in sample_table of your microtable object. Otherwise, please use add_data parameter.

default NULL; data.frame format; provide the environmental data in the format data.frame; rownames should be sample names. This parameter should be used when the microtable$sample_table object does not have environmental data. Under this circumstance, the env_cols parameter can not be used because no data can be selected.

default FALSE; whether convert all the character or factor columns to numeric type using the dropallfactors function. If TRUE, character columns will first be attempted to convert to numeric. If that fails, they will be converted to the factor type and then to numeric.

default FALSE; whether scale environmental variables to zero mean and unit variance.

default FALSE; Whether fill the NA (missing value) in the environmental data; If TRUE, the function can run the interpolation with the mice package.

default NULL; a colname of sample_table used to compare values across groups.

default NULL; perform differential test among groups (group parameter) within each group (by_group parameter).

default "KW"; see the following available options:

'KW'

KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons, see dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum and Signed Rank Tests for all paired groups

't.test'

Student's t-Test for all paired groups

'anova'

Duncan's new multiple range test for one-way anova; see duncan.test function of agricolae package. For multi-factor anova, see aov

'scheirerRayHare'

Scheirer Ray Hare test for nonparametric test used for a two-way factorial experiment; see scheirerRayHare function of rcompanion package

'lm'

Linear model based on the lm function

'lme'

lme: Linear Mixed Effect Model based on the lmerTest package. The formula parameter should be provided.

'glmm'

Generalized linear mixed model (GLMM) based on the glmmTMB package. The formula and family parameters are needed. Please refer to glmmTMB package to select the family function, e.g. family = glmmTMB::lognormal(link = "log"). The usage of formula is similar with that in 'lme' method. For the details of return table, please refer to the help document of trans_diff class.

parameters passed to cal_diff function of trans_alpha class.

parameters passed to plot_alpha in trans_alpha class. Please see plot_alpha function of trans_alpha for all the available parameters.

default NULL; a colname of sample_table; used to perform calculations for different groups.

default TRUE; whether use GGally::ggpairs function to plot the correlation results. If ggpairs = FALSE, the function will output a table with all the values instead of a graph. In this case, the function will call cal_cor to calculate autocorrelation instead of using the ggpairs function in GGally, so please use parameter passing to control more options.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette.

default 0.8; the alpha value to add transparency in colors; useful when group is not NULL.

parameters passed to GGally::ggpairs when ggpairs = TRUE or passed to cal_cor of trans_env class when ggpairs = FALSE.

default c("RDA", "dbRDA", "CCA")[1]; the ordination method.

default FALSE; whether perform the feature selection based on forward selection method.

default NULL; the taxonomic level used in RDA or CCA. Default NULL means using the merged data at "Genus" level. "ASV" or "OTU" can also be provided for the use of otu_table in microtable object.

default NULL; relative abundance threshold used to filter taxa when method is "RDA" or "CCA".

default NULL; a name of beta diversity matrix; only available when parameter method = "dbRDA"; If not provided, use the first beta diversity matrix in the microtable$beta_diversity automatically.

default NULL; additional distance matrix provided, when the user does not want to use the beta diversity matrix within the dataset; only available when method = "dbRDA".

paremeters passed to dbrda, rda or cca function according to the method parameter.

parameters passed to anova function.

the parameters passed to vegan::envfit function.

default 10; taxa number shown in the plot.

default FALSE; whether adjust the arrow length to be clearer.

default 0.1; used for scaling up the minimum of env arrow; multiply by the maximum distance between samples and origin.

default 0.8; used for scaling up the maximum of env arrow; multiply by the maximum distance between samples and origin.

default 0.1; used for scaling up the minimum of tax arrow; multiply by the maximum distance between samples and origin.

default 0.8; used for scaling up the maximum of tax arrow; multiply by the maximum distance between samples and origin.

default NULL; a colname of sample_table to assign colors to different groups.

default NULL; a colname of sample_table to assign shapes to different groups.

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector for point shape types of groups, see ggplot2 tutorial.

default "black"; environmental variable text color.

default "grey30"; environmental variable arrow color.

default "firebrick1"; taxa text color.

default "firebrick1"; taxa arrow color.

default 3.7; environmental variable text size.

default 3; taxa text size.

default FALSE; whether show the prefix (e.g., g__) of taxonomic information in the text.

default TRUE; "italic"; whether use "italic" style for the taxa text.

default "point"; plotting type of samples; one or more elements of "point", "ellipse", "chull", "centroid" and "none"; "none" denotes nothing.

'point'

add point

'ellipse'

add confidence ellipse for points of each group

'chull'

add convex hull for points of each group

'centroid'

add centroid line of each group

default 3; point size in plot when "point" is in plot_type. point_size can also be a variable name in sample_table, such as "pH".

default .8; point transparency in plot when "point" is in plot_type.

default 0.6; segment transparency in plot when "centroid" is in plot_type.

default 1; segment size in plot when "centroid" is in plot_type.

default 3; an integer; the line type related with centroid in plot when "centroid" is in plot_type.

default TRUE; whether fill colors to the area of ellipse or chull.

default 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in plot_type.

default .9; confidence level of ellipse when "ellipse" is in plot_type.

default "t"; ellipse type when "ellipse" is in plot_type; see type parameter in stat_ellipse function of ggplot2 package.

default NULL; the column name in sample table, if provided, show the point name in plot.

default NULL; numeric vector to adjust the env text x axis position; passed to nudge_x parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_x should be something like c(0.1, 0, -0.2, 0, 0). Note that this parameter and env_nudge_y is generally used when the automatic text adjustment is not very well.

default NULL; numeric vector to adjust the env text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_y should be something like c(0.1, 0, -0.2, 0, 0).

default NULL; numeric vector to adjust the taxa text x axis position; passed to nudge_x parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_x should be something like c(0.3, -0.2, 0).

default NULL; numeric vector to adjust the taxa text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_y should be something like c(-0.2, 0, 0.4).

paremeters passed to geom_point for controlling sample points.

default FALSE; whether use partial mantel test; If TRUE, use other all measurements as the zdis in each calculation.

default NULL; additional distance matrix provided when the beta diversity matrix in the dataset is not used.

default NULL; a name of beta diversity matrix. If necessary and not provided, use the first beta diversity matrix.

default "pearson"; one of "pearson", "spearman" and "kendall"; correlation method; see method parameter in vegan::mantel function.

default "fdr"; p.adjust method; see method parameter of p.adjust function for available options.

default NULL; one column name or number in sample_table; used to perform mantel test for different groups separately.

paremeters passed to mantel of vegan package.

default "Genus"; "Genus", "all" or "other"; "Genus" or other taxonomic names (e.g., "Phylum", "ASV"): invoke taxonomic abundance table in taxa_abund list of the microtable object; "all": merge all the taxonomic abundance tables in taxa_abund list into one; "other": provide additional taxa names by assigning other_taxa parameter.

default "pearson"; "pearson", "spearman", "kendall" or "maaslin2"; correlation method. "pearson", "spearman" or "kendall" all refer to the correlation analysis based on the cor.test function in R. "maaslin2" is the method in Maaslin2 package for finding associations between metadata and potentially high-dimensional microbial multi-omics data.

default FALSE; whether perform partial correlation based on the ppcor package. Available when method is "pearson", "spearman" or "kendall".

default NULL; selected environmental variable names used as third group of variables in all the partial correlations. If NULL; all the variables (except the one for correlation) in the environmental data will be used as the third group of variables. Otherwise, the function will control for the provided variables (one or more) in all the partial correlations, and the variables in partial_fix will not be employed anymore in the correlation analysis.

default NULL; additional data table to be used. Row names must be sample names.

default 0; the abundance threshold, such as 0.0005 when the input is relative abundance. The features with abundances lower than filter_thres will be filtered. This parameter cannot be applied when add_abund_table parameter is provided.

default NULL; integer; a number used to select high abundant taxa; only useful when use_data parameter is a taxonomic level, e.g., "Genus".

default NULL; character vector containing a series of feature names; available when use_data = "other"; provided names should be standard full names used to select taxa from all the tables in taxa_abund list of the microtable object; please refer to the example.

default "fdr"; p.adjust method; see method parameter of p.adjust function for available options. p_adjust_method = "none" can disable the p value adjustment.

default "All"; "All", "Taxa" or "Env"; P value adjustment type. "Env": adjustment for each environmental variable separately; "Taxa": adjustment for each taxon separately; "All": adjustment for all the data together no matter whether by_group is provided.

default NULL; one column name or number in sample_table; calculate correlations for different groups separately.

default NULL; numeric or character vector to select one column in sample_table for selecting samples; together with group_select.

default NULL; the group name used; remain samples within the group.

default TRUE; Whether use the complete taxonomic name of taxa.

default "tmp_input"; the temporary folder used to save the input files for Maaslin2.

default "tmp_output"; the temporary folder used to save the output files of Maaslin2.

deprecated. Please use method argument instead.

parameters passed to Maaslin2 function of Maaslin2 package.

default c("#053061", "white", "#A50026"); colors with only three values representing low, middle and high values.

default NULL; a customized palette with more color values to be used instead of the parameter color_vector.

default NULL; character vector; used to filter features that only have labels in the filter_feature vector. For example, filter_feature = "" can be used to remove features that only have "", no any "*".

default NULL; character vector; used to filter environmental variables that only have labels in the filter_env vector. For example, filter_env = "" can be used to remove features that only have "", no any "*".

default FALSE; whether use the complete taxonomic name.

default TRUE; whether retain the taxonomic prefix.

default NULL; character vector; customized text for y axis; shown in the plot from the top down. The input should be consistent with the feature names in the res_cor table.

default NULL; character vector; customized text for x axis.

default 30; number ranging from 0 to 90; used to adjust x axis text angle.

default 10; x axis text size.

default "black"; x axis text color.

default FALSE; whether use italic for y axis text.

default NULL; y axis text size. NULL means default ggplot2 value.

default "black"; y axis text color.

default "right"; "left" or "right"; the y axis text position.

default 4; the size of significance label shown in the cell.

default NULL; font family used.

default "none"; add clustering dendrogram for ggplot2 based heatmap. Available options: "none", "row", "col" or "both". "none": no any clustering used; "row": add clustering for rows; "col": add clustering for columns; "both": add clustering for both rows and columns.

default 0.2, the dendrogram plot height for rows; available when cluster_ggplot is not "none".

default 0.2, the dendrogram plot height for columns; available when cluster_ggplot is not "none".

default "grey50"; the color for the missing values.

default "identity"; the transformation for continuous scales in the legend; see the trans item in ggplot2::scale_colour_gradientn.

deprecated. Please use ytext_italic argument instead.

deprecated. Please use ytext_position argument instead.

paremeters passed to ggplot2::geom_tile.

default NULL; a single numeric or character value, a vector, or a distance matrix used for the x axis. If x is a single value, it will be used to select the column of data_env in the object. If x is a distance matrix, it will be transformed to be a vector.

default NULL; a single numeric or character value, a vector, or a distance matrix used for the y axis. If y is a single value, it will be used to select the column of data_env in the object. If y is a distance matrix, it will be transformed to be a vector.

default NULL; a character vector; if length is 1, must be a colname of sample_table in the input dataset; Otherwise, group should be a vector having same length with x/y (for vector) or column number of x/y (for matrix).

default NULL; a vector used to order groups, i.e. reorder the legend and colors in plot when group is not NULL; If group_order is NULL and group is provided, the function can first check whether the group column of sample_table is factor. If group_order is provided, disable the group orders or factor levels in the group column of sample_table.

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.

default NULL; a numeric vector for point shape types of groups when group is not NULL, see ggplot2 tutorial.

default c("cor", "lm")[1]; "cor": correlation; "lm" for regression.

default "pearson"; one of "pearson", "kendall" and "spearman"; correlation method.

default ";"; the separator string between different label parts.

default "left"; can be numeric or character vector of the same length as the number of groups and/or panels. If too short, they will be recycled.

numeric

value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates"

character

allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.

default "top"; same usage with label.x.npc; also see label.y.npc parameter of ggpubr::stat_cor function.

default NULL; x axis absolute position for adding the statistic label.

default NULL; x axis absolute position for adding the statistic label.

default ""; the title of x axis.

default ""; the title of y axis.

default 5; point size value.

default 0.6; alpha value for the point color transparency.

default 0.8; line size value.

default "black"; fitted line color; only available when group = NULL.

default TRUE; Whether show the confidence interval for the fitting.

default "grey70"; the color to fill the confidence interval when line_se = TRUE.

default 0.5; alpha value for the color transparency of line confidence interval.

default 4; trim the decimal places of p value.

default 3; trim the decimal places of correlation coefficient.

default TRUE; whether include the equation in the label when type = "lm".

default 2; trim the decimal places of first coefficient in regression.

default 2; trim the decimal places of second coefficient in regression.

default 2; trim the decimal places of R square in regression.

other arguments passed to geom_text or geom_label.

Whether to make a deep clone.

the object of microtable Class.

default "FAPROTAX"; "FAPROTAX" or "NJC19"; select a prokaryotic trait database:

'FAPROTAX'

FAPROTAX; Reference: Louca et al. (2016). Decoupling function and taxonomy in the global ocean microbiome. Science, 353(6305), 1272. <doi:10.1126/science.aaf4507>

'NJC19'

NJC19: Lim et al. (2020). Large-scale metabolic interaction network of the mouse and human gut microbiota. Scientific Data, 7(1). <10.1038/s41597-020-0516-5>. Note that the matching in this database is performed at the species level, hence utilizing it demands a higher level of precision in regards to the assignments of species in the taxonomic information table.

default "FUNGuild"; "FUNGuild" or "FungalTraits"; select a fungal trait database:

'FUNGuild'

Nguyen et al. (2016) FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology, 20(1), 241-248, <doi:10.1016/j.funeco.2015.06.006>

'FungalTraits'

version: FungalTraits_1.2_ver_16Dec_2020V.1.2; Polme et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity 105, 1-16 (2020). <doi:10.1007/s13225-020-00466-2>

default c("Highly Probable", "Probable", "Possible"). Selected 'confidenceRanking' when fungi_database = "FUNGuild".

default FALSE; whether use abundance of taxa. If FALSE, calculate the functional population percentage. If TRUE, calculate the functional individual percentage.

default TRUE; whether to use percentages in the result. If TRUE, value is bounded between 0 and 100. If FALSE, the result is relative proportion ('abundance_weighted = FALSE') or relative abundance ('abundance_weighted = TRUE') bounded between 0 and 1.

default 2; remained decimal places.

default NULL; the function name.

default TRUE; whether use group names as the facets in the plot, see trans_func$func_group_list object.

default NULL; character vector; to sort the x axis text; can be also used to select partial samples to show.

default "#00008B"; the color used as the low end in the color gradient.

default "#9E0142"; the color used as the high end in the color gradient.

default NULL; the folder path, e.g., ncbi-blast-2.5.0+/bin ; blast tools folder downloaded from "ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+" ; e.g., ncbi-blast-2.5.0+-x64-win64.tar.gz for windows system; if blast_tool_path is NULL, search the tools in the environmental path variable.

default "Tax4Fun2_ReferenceData_v2"; the path that points to files used in the prediction; The directory must contain the Ref99NR or Ref100NR folder; download Ref99NR.zip from "https://cloudstor.aarnet.edu.au/plus/s/DkoZIyZpMNbrzSw/download" or Ref100NR.zip from "https://cloudstor.aarnet.edu.au/plus/s/jIByczak9ZAFUB4/download".

default NULL; The temporary folder to store the logfile, intermediate file and result files; if NULL, use the default temporary in the computer system.

default 'Ref99NR'; "Ref99NR" or "Ref100NR"; Ref99NR: 99% clustering reference database; Ref100NR: no clustering.

default TRUE; whether normalize the result by the 16S rRNA copy number in the genomes.

default 97; the sequences identity threshold used for finding the nearest species.

default TRUE; whether use UProC to functionally anotate the genomes in the reference data.

default 1; the threads used in the blastn.

default FALSE; Different to Tax4Fun, when converting from KEGG functions to KEGG pathways, Tax4Fun2 does not equally split KO gene abundances between pathways a functions is affiliated to. The full predicted abundance is affiliated to each pathway. Use TRUE to split the abundances (default is FALSE).

Whether to make a deep clone.

default NULL; the object of microtable class. Default NULL means customized analysis.

default NULL; NULL or one of "bray", "pearson", "spearman", "sparcc", "bicor", "cclasso" and "ccrepe"; All the methods refered to NetCoMi package are performed based on netConstruct function of NetCoMi package and require NetCoMi to be installed from Github (https://github.com/stefpeschel/NetCoMi); For the algorithm details, please see Peschel et al. 2020 Brief. Bioinform <doi: 10.1093/bib/bbaa290>;

NULL

NULL denotes non-correlation network, i.e. do not use correlation-based network. If so, the return res_cor_p list will be NULL.

'bray'

1-B, where B is Bray-Curtis dissimilarity; based on vegan::vegdist function

'pearson'

Pearson correlation; If use_WGCNA_pearson_spearman and use_NetCoMi_pearson_spearman are both FALSE, use the function cor.test in R; use_WGCNA_pearson_spearman = TRUE invoke corAndPvalue function of WGCNA package; use_NetCoMi_pearson_spearman = TRUE invoke netConstruct function of NetCoMi package

'spearman'

Spearman correlation; other details are same with the 'pearson' option

'sparcc'

SparCC algorithm (Friedman & Alm, PLoS Comp Biol, 2012, <doi:10.1371/journal.pcbi.1002687>); use NetCoMi package when use_sparcc_method = "NetCoMi"; use SpiecEasi package when use_sparcc_method = "SpiecEasi" and require SpiecEasi to be installed from Github (https://github.com/zdk123/SpiecEasi)

'bicor'

Calculate biweight midcorrelation efficiently for matrices based on WGCNA::bicor function; This option can invoke netConstruct function of NetCoMi package; Make sure WGCNA and NetCoMi packages are both installed

'cclasso'

Correlation inference of Composition data through Lasso method based on netConstruct function of NetCoMi package; for details, see NetCoMi::cclasso function

'ccrepe'

Calculates compositionality-corrected p-values and q-values for compositional data using an arbitrary distance metric based on NetCoMi::netConstruct function; also see NetCoMi::ccrepe function

default FALSE; whether use WGCNA package to calculate correlation when cor_method = "pearson" or "spearman".

default FALSE; whether use NetCoMi package to calculate correlation when cor_method = "pearson" or "spearman". The important difference between NetCoMi and others is the features of zero handling and data normalization; See <doi: 10.1093/bib/bbaa290>.

default c("NetCoMi", "SpiecEasi")[1]; use NetCoMi package or SpiecEasi package to perform SparCC when cor_method = "sparcc".

default "OTU"; taxonomic rank; 'OTU' denotes using feature abundance table; other available options should be one of the colnames of tax_table of input dataset.

default 0; the relative abundance threshold.

default 1; the CPU thread number; available when use_WGCNA_pearson_spearman = TRUE or use_sparcc_method = "SpiecEasi".

default 100; SparCC simulation number for bootstrap when use_sparcc_method = "SpiecEasi".

default NULL; numeric or character vector to select the column names of environmental data in dataset$sample_table; the environmental data can be used in the correlation network (as the nodes) or FlashWeave network.

default NULL; provide environmental variable table additionally instead of env_cols parameter; rownames must be sample names.

parameters pass to NetCoMi::netConstruct for other operations, such as zero handling and/or data normalization when cor_method and other parameters refer to NetCoMi package.

default "COR"; "COR", "SpiecEasi", "gcoda", "FlashWeave" or "beemStatic"; network_method = NULL means skipping the network construction for the customized use. The option details:

'COR'

correlation-based network; use the correlation and p value matrices in res_cor_p list stored in the object; See Deng et al. (2012) <doi:10.1186/1471-2105-13-113> for other details

'SpiecEasi'

SpiecEasi network; relies on algorithms of sparse neighborhood and inverse covariance selection; belong to the category of conditional dependence and graphical models; see https://github.com/zdk123/SpiecEasi for installing the R package; see Kurtz et al. (2015) <doi:10.1371/journal.pcbi.1004226> for the algorithm details

'gcoda'

hypothesize the logistic normal distribution of microbiome data; use penalized maximum likelihood method to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data; belong to the category of conditional dependence and graphical models; depend on the R NetCoMi package https://github.com/stefpeschel/NetCoMi; see FANG et al. (2017) <doi:10.1089/cmb.2017.0054> for the algorithm details

'FlashWeave'

FlashWeave network; Local-to-global learning framework; belong to the category of conditional dependence and graphical models; good performance on heterogenous datasets to find direct associations among taxa; see https://github.com/meringlab/FlashWeave.jl for installing julia language and FlashWeave package; julia must be in the computer system env path, otherwise the program can not find it; see Tackmann et al. (2019) <doi:10.1016/j.cels.2019.08.002> for the algorithm details

'beemStatic'

beemStatic network; extend generalized Lotka-Volterra model to cases of cross-sectional datasets to infer interaction among taxa based on expectation-maximization algorithm; see https://github.com/CSB5/BEEM-static for installing the R package; see Li et al. (2021) <doi:10.1371/journal.pcbi.1009343> for the algorithm details

default 0.01; the p value threshold for the correlation-based network.

default "fdr"; p value adjustment method, see method parameter of p.adjust function for available options, in which COR_p_adjust = "none" means giving up the p value adjustment.

default TRUE; whether use correlation coefficient as the weight of edges; FALSE represents weight = 1 for all edges.

default 0.6; correlation coefficient threshold for the correlation network.

default FALSE; whether use random matrix theory (RMT) based method to determine the correlation coefficient; see https://doi.org/10.1186/1471-2105-13-113

default c(0.01, 0.8); the low and high value threshold used for the RMT optimization; only useful when COR_optimization = TRUE.

default 0.01; the interval of correlation coefficient used for RMT optimization; only useful when COR_optimization = TRUE.

default "mb"; either 'glasso' or 'mb';see spiec.easi function in package SpiecEasi and https://github.com/zdk123/SpiecEasi.

default NULL; The temporary directory used to save the temporary files for running FlashWeave; If not assigned, use the system user temp.

default FALSE; whether use env data for the optimization, If TRUE, the function automatically find the env_data in the object and generate a file for meta_data_path parameter of FlashWeave package.

default "alpha=0.01,sensitive=true,heterogeneous=true"; the parameters passed to julia FlashWeave package; user can change the parameters or add more according to FlashWeave help document; An exception is meta_data_path parameter as it is generated based on the data inside the object, see FlashWeave_meta_data parameter for the description.

default NULL; The path of FlashWeave output gml file for customized usage. This parameter is provided for some customized needs. For instance, it can be cumbersome to input bacterial and fungal abundances as separate input files for network analysis using the above parameter. Users can run FlashWeave on their own, and then provide the resulting gml file to this parameter, which allows them to continue using other functions.

default 0.001; for network_method = "beemStatic"; the threshold used to limit the number of interactions (strength); same with the t.strength parameter in showInteraction function of beemStatic package.

default 0.8; for network_method = "beemStatic"; the threshold used to limit the number of interactions (stability); same with the t.stab parameter in showInteraction function of beemStatic package.

default "Phylum"; one or more taxonomic rank name; used to add taxonomic rank name to network node properties.

default TRUE; whether delete the nodes without any link.

default FALSE; whether use OTU name as representatives of taxa when taxa_level != "OTU". Default FALSE means using taxonomic information of taxa_level instead of OTU name.

parameters pass to SpiecEasi::spiec.easi when network_method = "SpiecEasi"; pass to NetCoMi::netConstruct when network_method = "gcoda"; pass to beemStatic::func.EM when network_method = "beemStatic".

default "cluster_fast_greedy"; the method used to find the optimal community structure of a graph; the following are available functions (options) from igraph package:
"cluster_fast_greedy", "cluster_walktrap", "cluster_edge_betweenness",
"cluster_infomap", "cluster_label_prop", "cluster_leading_eigen",
"cluster_louvain", "cluster_spinglass", "cluster_optimal".
For the details of these functions, please see the help document, such as help(cluster_fast_greedy); Note that the default "cluster_fast_greedy" method can not be applied to directed network. If directed network is provided, the function can automatically switch the default method from "cluster_fast_greedy" to "cluster_walktrap".

default "M"; the prefix of module names; module names are made of the module_name_prefix and numbers; numbers are assigned according to the sorting result of node numbers in modules with decreasing trend.

default "network.gexf"; file path to save the network.

parameters pass to gexf function of rgexf package except for nodes, edges, edgesLabel, edgesWeight, nodesAtt, edgesAtt and meta.

default TRUE; whether calculate the node roles <doi:10.1038/nature03288; 10.1186/1471-2105-13-113>. The role of node i is characterized by its within-module connectivity (zi) and among-module connectivity (Pi) as follows

z_i = \dfrac{k_{ib} - \bar{k_b}}{\sigma_{k_b}}

P_i = 1 - \displaystyle\sum_{c=1}^{N_M} \biggl(\frac{k_{ic}}{k_i}\biggr)^2

where k_{ib} is the number of links of node i to other nodes in its module b, \bar{k_b} and \sigma_{k_b} are the average and standard deviation of within-module connectivity, respectively over all the nodes in module b, k_i is the number of links of node i in the whole network, k_{ic} is the number of links from node i to nodes in module c, and N_M is the number of modules in the network.

parameters passed to as_adjacency_matrix function of igraph package.

default "igraph"; The available options:

'igraph'

call plot.igraph function in igraph package for a static network; see plot.igraph for the parameters

'ggraph'

call ggraph function in ggraph package for a static network

'networkD3'

use forceNetwork function in networkD3 package for a dynamic network; see forceNetwork function for the parameters

default "name"; node label shown in the plot for method = "ggraph" or method = "networkD3"; Please see the column names of object$res_node_table, which is the returned table of function object$get_node_table; User can select other column names in res_node_table.

default NULL; node color assignment for method = "ggraph" or method = "networkD3"; Select a column name of object$res_node_table, such as "module".

default "fr"; for method = "ggraph"; see layout parameter of create_layout function in ggraph package.

default 2; for method = "ggraph"; the node size.

default TRUE; for method = "ggraph"; whether show the label text of nodes.

default NULL; for method = "ggraph"; a column name of object$res_node_table used to assign label text colors.

default 3; for method = "ggraph"; the node label text size.

default TRUE; used for method = "networkD3"; logical value to enable node colour legends; Please see the legend parameter in networkD3::forceNetwork function.

default TRUE; used for method = "networkD3"; logical value to enable (TRUE) or disable (FALSE) zooming; Please see the zoom parameter in networkD3::forceNetwork function.

parameters passed to plot.igraph function when method = "igraph" or forceNetwork function when method = "networkD3".

default 1; 1 or 2; 1 represents taxa roles plot (node roles include Module hubs, Network hubs, Connectors and Peripherals <doi:10.1038/nature03288; 10.1186/1471-2105-13-113>). The 'p' column (Pi, among-module connectivity) in res_node_table table is used in x-axis. The 'z' column (Zi, within-module connectivity) is used in y-axis; 2 represents the layered plot with taxa as x axis and the index (e.g., Zi and Pi) as y axis. Please refer to res_node_table data stored in the object for the detailed information.

default FALSE; for use_type=1; TRUE: use background colors for each area; FALSE: use classic point colors.

default NULL; for use_type=1; color palette for background or points.

default FALSE; for use_type = 1; whether add labels for the points.

default c("Network hubs", "Module hubs", "Connectors"); If add_label = TRUE, which part in taxa_roles column is used to show labels; character vectors.

default "name"; If add_label = TRUE; which column of object$res_node_table is used to label the text.

default 4; The text size of the label.

default "grey50"; The text color of the label.

default FALSE; whether use italic style for the label text.

default FALSE; whether parse the label text. See the parse parameter in ggrepel::geom_text_repel function.

default FALSE; for use_type=1; whether plot the modules information.

default c(0, 1); for use_type=1; x axis range when roles_color_background = FALSE.

default "Phylum"; for use_type=2; used taxonomic level in x axis.

default c("z", "p"); for use_type=2; indexes used in y axis. Please see res_node_table in the object for other available indexes.

default 1:10; for use_type=2; showed number in x axis, sorting according to the nodes number.

default "Phylum"; for use_type=2; variable for color.

default "taxa_roles"; for use_type=2; variable for shape.

default "Abundance"; for use_type=2; used for point size; a fixed number (e.g. 5) is also acceptable.

default RColorBrewer::brewer.pal(12, "Paired"); for use_type=2; color vector.

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); for use_type=2; shape vector, see ggplot2 tutorial for the shape meaning.

parameters pass to geom_point function of ggplot2 package.

default NULL; provide the node names that will be used in the sub-network.

default NULL; provide the edge label or numbers that need to be remained. For the edge label, it should must be "+" or "-". For the numbers, they should fall within the range of rows in res_edge_table of the object.

default TRUE; whether remove the nodes without any edge in the sub-network. So this function can also be used to remove the nodes withou any edge when node and edge are both NULL.

default FALSE; whether remain the nodes and edges that related to the nodes provided in node parameter. When this parameter is set to TRUE, the network will filter based on edges rather than directly on nodes. The logic is that if at least one of the two nodes connected by an edge is included in the nodes provided by the node parameter, the edge will be retained. Otherwise, it will be filtered out. When this parameter is set to FALSE, the network will filter directly based on the node parameter. Any nodes not included in the node parameter will be filtered out.

default TRUE; whether return the network with igraph format. If FALSE, return trans_network object.

parameters pass to bootstrap_p function in poweRlaw package.

default "Phylum"; taxonomic rank.

default TRUE; If TRUE, plot the summed positive linkages; If FALSE, plot the summed negative linkages.

default NULL; number of taxa presented in the plot.

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for taxa.

default c("chorddiag", "circlize")[1]; chorddiag package <https://github.com/mattflor/chorddiag> or circlize package.

pass to chorddiag::chorddiag function when method = "chorddiag" or circlize::chordDiagram function when method = "circlize". Note that for circlize::chordDiagram function, keep.diagonal, symmetric and self.link parameters have been fixed to fit the input data.

default 100; simulation number of random network.

default FALSE; whether output each simulated network result.

default "module"; which column to use as the 'community'; must be one of the name of res_node_table from function get_node_table.

default TRUE; whether sum abundance of taxa. TRUE: sum the abundance for a taxon across all samples; FALSE: sum the frequency for a taxon across all samples.

Whether to make a deep clone.

the microtable object or data.frame object. If it is data.frame object, please make sure that rows are samples, and columns are features.

default "rarefy"; See the following available options.

Methods for normalization:

• "rarefy": classic rarefaction based on R sample function.

• "SRS": scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) <doi:10.7717/peerj.9593>.

• "clr": Centered log-ratio normalization <ISBN:978-0-412-28060-3> <doi: 10.3389/fmicb.2017.02224>. It is defined:

  clr_{ki} = \log\frac{x_{ki}}{g(x_i)}

  where x_{ki} is the abundance of kth feature in sample i, g(x_i) is the geometric mean of abundances for sample i. A pseudocount need to be added to deal with the zero. For more information, please see the 'clr' method in decostand function of vegan package.

• "rclr": Robust centered log-ratio normalization <doi:10.1128/msystems.00016-19>. It is defined:

  rclr_{ki} = \log\frac{x_{ki}}{g(x_i > 0)}

  where x_{ki} is the abundance of kth feature in sample i, g(x_i > 0) is the geometric mean of abundances (> 0) for sample i. In rclr, zero values are kept as zeroes, and not taken into account.

• "GMPR": Geometric mean of pairwise ratios <doi: 10.7717/peerj.4600>. For a given sample i, the size factor s_i is defined:

  s_i = \biggl( {\displaystyle\prod_{j=1}^{n} Median_{k|c_{ki}c_{kj} \ne 0} \lbrace \dfrac{c_{ki}}{c_{kj}} \rbrace} \biggr) ^{1/n}

  where k denotes all the features, and n denotes all the samples. For sample i, GMPR = \frac{x_{i}}{s_i}, where x_i is the feature abundances of sample i.

• "CSS": Cumulative sum scaling normalization based on the metagenomeSeq package <doi:10.1038/nmeth.2658>. For a given sample j, the scaling factor s_{j}^{l} is defined:

  s_{j}^{l} = {\displaystyle\sum_{i|c_{ij} \leqslant q_{j}^{l}} c_{ij}}

  where q_{j}^{l} is the lth quantile of sample j, that is, in sample j there are l features with counts smaller than q_{j}^{l}. c_{ij} denotes the count (abundance) of feature i in sample j. For l = 0.95m (feature number), q_{j}^{l} corresponds to the 95th percentile of the count distribution for sample j. Normalized counts \tilde{c_{ij}} = (\frac{c_{ij}}{s_{j}^{l}})(N), where N is an appropriately chosen normalization constant.

• "TSS": Total sum scaling. Abundance is divided by the sequencing depth. For a given sample j, normalized counts is defined:

  \tilde{c_{ij}} = \frac{c_{ij}}{\sum_{i=1}^{N_{j}} c_{ij}}

  where c_{ij} is the counts of feature i in sample j, and N_{j} is the feature number of sample j.

• "eBay": Empirical Bayes approach to normalization <10.1186/s12859-020-03552-z>. The implemented method is not tree-related. In the output, the sum of each sample is 1.

• "TMM": Trimmed mean of M-values method based on the normLibSizes function of edgeR package <doi: 10.1186/gb-2010-11-3-r25>.

• "DESeq2": Median ratio of gene counts relative to geometric mean per gene based on the DESeq function of DESeq2 package <doi: 10.1186/s13059-014-0550-8>. This option can invoke the trans_diff class and extract the normalized data from the original result. Note that either group or formula should be provided. The scaling factor is defined:

  s_{j} = Median_{i} \frac{c_{ij}}{\bigl( {\prod_{j=1}^{n} c_{ij}} \bigr) ^{1/n}}

  where c_{ij} is the counts of feature i in sample j, and n is the total sample number.

• "Wrench": Group-wise and sample-wise compositional bias factor <doi: 10.1186/s12864-018-5160-5>. Note that condition parameter is necesary to be passed to condition parameter in wrench function of Wrench package. As the input data must be microtable object, so the input condition parameter can be a column name of sample_table. The scaling factor is defined:

  s_{j} = \frac{1}{p} \sum_{ij} W_{ij} \frac{X_{ij}}{\overline{X_{i}}}

  where X_{ij} represents the relative abundance (proportion) for feature i in sample j, \overline{X_{i}} is the average proportion of feature i across the dataset, W_{ij} represents a weight specific to each technique, and p is the feature number in sample.

• "RLE": Relative log expression.

Methods based on decostand function of vegan package:

• "total": divide by margin total (default MARGIN = 1, i.e. rows - samples).

• "max": divide by margin maximum (default MARGIN = 2, i.e. columns - features).

• "normalize": make margin sum of squares equal to one (default MARGIN = 1).

• "range": standardize values into range 0...1 (default MARGIN = 2). If all values are constant, they will be transformed to 0.

• "standardize": scale x to zero mean and unit variance (default MARGIN = 2).

• "pa": scale x to presence/absence scale (0/1).

• "log": logarithmic transformation.

Other methods for transformation:

• "AST": Arc sine square root transformation.

default NULL; libray size for rarefaction when method = "rarefy" or "SRS". If not provided, use the minimum number across all samples. For "SRS" method, this parameter is passed to Cmin parameter of SRS function of SRS package.

default 123; random seed. Available when method = "rarefy" or "SRS".

default TRUE; see sample for the random sampling; Available when method = "rarefy".

default 1; add pseudocount for those features with 0 abundance when method = "clr".

default 10; the intersecting taxa number between paired sample for method = "GMPR".

default 1; the minimum number of counts required to calculate ratios for method = "GMPR".

default NULL; Only available when method = "Wrench". This parameter is passed to the condition parameter of wrench function in Wrench package It must be a column name of sample_table or a vector with same length of samples.

default NULL; 1 = samples, and 2 = features of abundance table; only available when method comes from decostand function of vegan package. If MARGIN is NULL, use the default value in decostand function.

default 2; The logarithm base.

parameters pass to vegan::decostand, or metagenomeSeq::cumNorm when method = "CSS", or edgeR::normLibSizes when method = "TMM" or "RLE", or trans_diff class when method = "DESeq2", or wrench function of Wrench package when method = "Wrench".

Whether to make a deep clone.

the object of microtable Class.

default 0; the relative abundance threshold.

default NULL; how many taxa the user want to keep, if provided, filter_thres parameter will be forcible invalid.

default NULL; which column name in sample_table is selected as the group for the following selection.

default NULL; one or more elements in group, used to select samples.

default NULL; number or name vector to select the environmental data in dataset$sample_table.

default NULL; provide environmental data table additionally.

default FALSE; whether fill the NA in environmental data based on the method in mice package.

default NULL; numeric or character vector to select env_data; if provide multiple variables or NULL, use PCA (principal component analysis) to reduce dimensionality.

default seq(0, 1, 0.02); see break.pts parameter in mantel.correlog of vegan package.

default FALSE; see cutoff parameter in mantel.correlog.

parameters pass to mantel.correlog function in vegan package.

default 22; the number for selecting point shape type; see ggplot2 manual for the number meaning.

default 3; the point size.

default TRUE; whether use abundance-weighted method.

default TRUE; whether use abundance-weighted method.

default FALSE; see exclude.conspecifics parameter in comdistnt function of picante package.

default FALSE; whether use bmntd.big function of iCAMP package to calculate betaMNTD. This method can store the phylogenetic distance matrix on the disk to lower the memory spending and perform the calculation parallelly.

default FALSE; whether use bmntd.big function of iCAMP package automatically when the feature number is large.

default NULL; the temporary directory used to place the large tree file; If NULL; use the system user tempdir.

paremeters pass to iCAMP::pdist.big function.

default 1000; simulation runs.

default "taxa.labels"; The available options include "taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap"and "trialswap"; see null.model parameter of ses.mntd function in picante package for the algorithm details.

default TRUE; whether use weighted abundance.

default 1000; iteration number for part null models to perform; see iterations parameter of picante::randomizeMatrix function.

default 1000; simulation number of null model.

default "taxa.labels"; The available options include "taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap"and "trialswap"; see null.model parameter of ses.mntd function in picante package for the algorithm details.

default TRUE; whether use abundance-weighted method.

default FALSE; see comdistnt in picante package.

default FALSE; whether use bmntd.big function of iCAMP package to calculate betaMNTD. This method can store the phylogenetic distance matrix on the disk to lower the memory spending and perform the calculation parallelly.

default FALSE; whether to make use_iCAMP to be TRUE when the feature number is large.

default NULL; the temporary directory used to place the large tree file; If NULL; use the system user tempdir.

default 2; the CPU thread number.

default 1000; iteration number for part null models to perform; see iterations parameter of picante::randomizeMatrix function.

default 1000; simulation runs.

default TRUE; whether show the calculation process message.

default "independentswap"; see more available options in randomizeMatrix function of picante package.

default TRUE; whether use ses.betaMNTD (betaNTI); if False, use ses.betaMPD (betaNRI).

default NULL; a column name in sample_table of microtable object. If provided, the analysis will be performed for each group instead of the whole.

default "taxa.labels"; Null model to use; see null.model parameter in ses.mpd function of picante package for available options.

default FALSE; Should mean nearest relative distances for each species be weighted by species abundance?

default 999; Number of randomizations.

paremeters pass to ses.mpd function in picante package.

default "taxa.labels"; Null model to use; see null.model parameter in ses.mntd function of picante package for available options.

default FALSE; Should mean nearest taxon distances for each species be weighted by species abundance?

default 999; Number of randomizations.

paremeters pass to ses.mntd function in picante package.

default NULL; one column name or number in sample_table; calculate C-score for different groups separately.

paremeters pass to bipartite::C.score function.

default "tNST"; 'tNST' or 'pNST'. See the help document of tNST or pNST function in NST package for more details.

a colname of sample_table in microtable object; the function can select the data from sample_table to generate a one-column (n x 1) matrix and provide it to the group parameter of tNST or pNST function.

paremeters pass to NST::tNST or NST::pNST function; see the document of corresponding function for more details.

default "nst.boot"; "nst.boot" or "nst.panova"; see NST::nst.boot function or NST::nst.panova function for the details.

paremeters pass to NST::nst.boot when method = "nst.boot" or NST::nst.panova when method = "nst.panova".

default 10; which column is selected for the conversion. See the columns of res_NST$index.pair stored in the object.

Whether to make a deep clone.

the object of microtable class or a matrix-like table (data.frame or matrix object). If dataset is a matrix-like table, features must be rows.

default NULL; NULL, "numratio" or "seqratio"; "numratio": calculate the percentage of feature number; "seqratio": calculate the percentage of feature abundance; NULL: no additional percentage.

default "&"; the joint mark for generating multi-sample names.

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete.

default TRUE; whether fill the area color.

default 4.5; text size in plot.

default 6; name size in plot.

default NULL; name position in plot.

default .3; alpha for transparency.

default 1.1; cycle line size.

default FALSE; whether use petal plot.

default "#BEAED4"; color of the petals; If petal_color only has one color value, all the petals will be assigned with this color value. If petal_color has multiple colors, and the number of color values is smaller than the petal number, the function can append more colors automatically with the color interpolation.

default "#BEBADA"; color of the center in the petal plot.

default 4; the length of the ellipse.

default 1; scaling up the size of the ellipse.

default c(-12, 12); the width of the plot.

default 40; petal center circle size.

default 4; the distance of text to circle.

default 2.3; the distance of title to circle.

default 1.3; the distance of data text to circle.

default 40; the distance between two data text.

default NULL; other characters used to show in the plot.

default c(1, 1); the text position for text in other_text_show.

default 5; the text size for text in other_text_show.

default TRUE; whether add the left bar plot to show the feature number of each sample.

default FALSE; TRUE is used to sort samples according to the number of features in each sample. FALSE means the sample order is same with that in sample_table of the raw dataset.

default "Intersection set"; y axis title of upper plot.

default 15; y axis title size of upper plot.

default 4; y axis text size of upper plot.

default "grey70"; bar fill color of upper plot.

default 0.9; bar width of upper plot.

default 12; y axis text size, i.e. sample name size, of bottom sample plot.

default 1; bottom plot height relative to the upper bar plot. 1 represents the height of bottom plot is same with the upper bar plot.

default 3; point size of bottom plot.

default "black"; point color of bottom plot.

default "grey95"; fill color for the striped background in the bottom sample plot. If the parameter length is 1, use "white" to distinguish the color stripes. If the parameter length is greater than 1, use all provided colors.

default 1; the color transparency for the parameter bottom_background_fill.

default 0.5; the line width in the bottom plot.

default "black"; the line color in the bottom plot.

default 0.3; left bar plot width relative to the right bottom plot.

default "grey70"; fill color for the left bar plot presenting feature number.

default 1; the color transparency for the parameter left_bar_fill.

default 0.9; bar width of left plot.

default 10; x axis text size of the left bar plot.

default "white"; fill color for the striped background in the left plot. If the parameter length is 1, use "white" to distinguish the color stripes. If the parameter length is greater than 1, use all provided colors.

default 1; the color transparency for the parameter left_background_fill.

default TRUE; whether only use OTUs occurrence frequency, i.e. presence/absence data; if FALSE, use abundance data.

Whether to make a deep clone.