Package 'microeco'

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

data.frame; default NULL; 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.

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

phylo; default NULL; The phylogenetic tree; use read.tree function in ape package for input.

DNAStringSet or list format; default NULL; The representative sequences; use read.fasta function in seqinr package or readDNAStringSet function in Biostrings package for input.

default FALSE; Whether trim the files in the microtable object automatically; If TRUE, running the functions in microtable class can invoke the tidy_dataset function automatically.

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 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 column name used in the tax_table.

default "ASV_"; the prefix of new names; new names will be newname_prefix + numbers according to the rownames order of 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 or character vector of colnames of microtable$tax_table; applied to select columns to merge and calculate abundances according to ordered hierarchical levels. This is very useful if there are commented columns or some columns with multiple structure that cannot be used directly.

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. Very useful when multiple mapping information exist.

default "&&"; Separator delimiting collapsed values; only useful when split_group = TRUE; see sep parameter in separate_rows function of tidyr package.

default NULL; character vector or list; only useful when split_group = TRUE; character vector: fixed column or columns used for the splitting in tax_table for each abundance calculation; list: containing more character vectors to assign the column names to each calculation, such as list(c("Phylum"), c("Phylum", "Class")).

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) when method is not "jaccard". TRUE is used for "jaccard" automatically.

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 relative abundance threshold for filtering the taxa with low abundance.

default 10; how many taxa are selected to show. 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; show the abundance percentage.

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 necessary for the legend with nested taxonomic levels in the bar plot.

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.

Capture unknown parameters.

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").

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 10; x axis text size.

default 11; y axis text size.

default TRUE; whether retain x text.

default TRUE; whether retain x title.

default TRUE; whether remove grid lines.

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

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 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.

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

't.test'

Student's t-Test for all paired groups

'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. Other available options include "LSD.test" and "HSD.test". All those are the function names in agricolae package.

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", and "errorbar". 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.

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; wheter 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) used to make x axis text generate angle.

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 in plot; applied when by_group is not NULL.

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

default 1; errorbar size. Available when plot_type = "errorbar".

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

default 3; point size for taxa. Available when plot_type = "errorbar".

default 0.8; point transparency. Available when plot_type = "errorbar".

default FALSE; whether add line. Available when plot_type = "errorbar".

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

default 2; an integer; line type when add_line = TRUE. Available when plot_type = "errorbar".

default "grey50"; line color when add_line = TRUE. Available when plot_type = "errorbar" and by_group is NULL.

default 0.5; line transparency when add_line = TRUE. Available when plot_type = "errorbar".

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.

the 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; sample group used for manova, betadisper or group distance comparision.

default "PCoA"; "PCoA", "NMDS", "PCA", "DCA" or "PLS-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. 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).

default 3; dimensions shown in the results (except method "NMDS").

default FALSE; whether species abundance will be square transformed; only available when method is "PCA" or "DCA".

default FALSE; whether species loading in PCA or DCA 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.

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".

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 of each group

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.

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 "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 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.

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.

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.

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

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"; see the following available options:

'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 cor_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 at 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, the function will use the features in input microtable$otu_table automatically.

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 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 1:20; numeric vector; the taxa numbers (1:n) selected in the plot; If the n is larger than the number of total significant taxa, automatically use all the taxa.

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

default NULL; this is used to select the paired groups. This parameter is especially useful when the comparision methods is applied to paired groups; The input select_group must be one of object$res_diff$Comparison.

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 0.9; the bar width in plot.

default TRUE; whether use SE in plot, if FALSE, use SD.

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

default "Significance"; select a colname of object$res_diff for the label text, such as 'P.adj' or 'Significance'.

default "black"; the color for the label text when add_sig = TRUE.

default 3.88; the size of text in added label.

default 0.01; the tip length for the added line when add_sig = TRUE.

default 1.01; the y axis position from which to add the label; the default 1.01 means 1.01 * Value; For method != "anova", all the start positions are same, i.e. Value = max(Mean+SD or Mean+SE); For method = "anova"; the stat position is calculated for each point, i.e. Value = Mean+SD or Mean+SE.

default 0.05; the increasing y axia space to add label for paired groups; the default 0.05 means 0.05 * y_start * Value; In addition, this parameter is also used to label the letters of anova result with the fixed (1 + y_increase) * y_start * Value.

default 10; the size for the y axis text, i.e. feature text.

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

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.

parameters passed to ggsignif::stat_signif when add_sig = TRUE.

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; the text size of x axis.

default 12; the size for the y axis text.

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 the characters or factors to numeric values.

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.

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; If use RDA or CCA, provide the taxonomic rank, such as "Phylum" or "Genus"; If use otu_table; please set taxa_level = "OTU".

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 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.

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.

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.

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 FALSE; whether use pheatmap package to plot the heatmap.

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 italic type for y lab text.

default FALSE; whether use the complete taxonomic name.

default TRUE; whether retain the taxonomic prefix.

default NULL; character vector; provide customized text order for y axis; shown in the plot from the top down.

default NULL; character vector; provide customized text order 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 NULL; y axis text size. NULL means default ggplot2 value.

default "black"; y axis text color.

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

default NULL; font family used in ggplot2; only available when pheatmap = FALSE.

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. Only available when pheatmap = FALSE.

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 "right"; "left" or "right"; the y axis text position for ggplot2 based heatmap.

default NULL; provide x axis text labels additionally; only available when pheatmap = TRUE.

default "grey50"; the color for the missing values when pheatmap = FALSE.

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

paremeters passed to ggplot2::geom_tile or pheatmap::pheatmap, depending on the parameter pheatmap is FALSE or TRUE.

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 FALSE; whether keep the intermediate file, that is, the feature table in local place.

default NULL; the folder, see Tax4Fun function in Tax4Fun package.

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 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.

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

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