R: Create 'trans_env' object to analyze the association between...

trans_env {microeco}

R Documentation

Create `trans_env` object to analyze the association between environmental factor and microbial community.

Description

This class is a wrapper for a series of operations associated with environmental measurements, including redundancy analysis, mantel test, correlation analysis and linear fitting.

Methods

Method `new()`

Usage

trans_env$new(
  dataset = NULL,
  env_cols = NULL,
  add_data = NULL,
  character2numeric = FALSE,
  standardize = FALSE,
  complete_na = FALSE
)

Arguments

dataset: the object of microtable Class.
env_cols: 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.
add_data: 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.
character2numeric: default FALSE; whether convert the characters or factors to numeric values.
standardize: default FALSE; whether scale environmental variables to zero mean and unit variance.
complete_na: default FALSE; Whether fill the NA (missing value) in the environmental data; If TRUE, the function can run the interpolation with the mice package.

Returns

data_env stored in the object.

Examples

data(dataset)
data(env_data_16S)
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])

Method `cal_diff()`

Differential test of environmental variables across groups.

Usage

trans_env$cal_diff(
  group = NULL,
  by_group = NULL,
  method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lm",
    "lme", "glmm")[1],
  ...
)

Arguments

group

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

by_group

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

method

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.

Returns

res_diff stored in the object. In the data frame, 'Group' column means that the group has the maximum median or mean value across the test groups; For non-parametric methods, median value; For t.test, mean value.

Examples

\donttest{
t1$cal_diff(group = "Group", method = "KW")
t1$cal_diff(group = "Group", method = "anova")
}

Method `plot_diff()`

Plot environmental variables across groups and add the significance label.

Usage

trans_env$plot_diff(...)

Arguments

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

Method `cal_autocor()`

Calculate the autocorrelations among environmental variables.

Usage

trans_env$cal_autocor(
  group = NULL,
  ggpairs = TRUE,
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  alpha = 0.8,
  ...
)

Arguments

group: default NULL; a colname of sample_table; used to perform calculations for different groups.
ggpairs: default TRUE; whether use GGally::ggpairs function to plot the correlation results.
color_values: default RColorBrewer::brewer.pal(8, "Dark2"); colors palette.
alpha: 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.

Returns

ggmatrix when ggpairs = TRUE or data.frame object when ggpairs = FALSE.

Examples

\dontrun{
# Spearman correlation
t1$cal_autocor(upper = list(continuous = GGally::wrap("cor", method= "spearman")))
}

Method `cal_ordination()`

Redundancy analysis (RDA) and Correspondence Analysis (CCA) based on the vegan package.

Usage

trans_env$cal_ordination(
  method = c("RDA", "dbRDA", "CCA")[1],
  feature_sel = FALSE,
  taxa_level = NULL,
  taxa_filter_thres = NULL,
  use_measure = NULL,
  add_matrix = NULL,
  ...
)

Arguments

method: default c("RDA", "dbRDA", "CCA")[1]; the ordination method.
feature_sel: default FALSE; whether perform the feature selection based on forward selection method.
taxa_level: 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".
taxa_filter_thres: default NULL; relative abundance threshold used to filter taxa when method is "RDA" or "CCA".
use_measure: 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.
add_matrix: 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.

Returns

res_ordination and res_ordination_R2 stored in the object.

Examples

\donttest{
t1$cal_ordination(method = "dbRDA", use_measure = "bray")
t1$cal_ordination(method = "RDA", taxa_level = "Genus")
t1$cal_ordination(method = "CCA", taxa_level = "Genus")
}

Method `cal_ordination_anova()`

Use anova to test the significance of the terms and axis in ordination.

Usage

trans_env$cal_ordination_anova(...)

Arguments

...: parameters passed to anova function.

Returns

res_ordination_terms and res_ordination_axis stored in the object.

Examples

\donttest{
t1$cal_ordination_anova()
}

Method `cal_ordination_envfit()`

Fit each environmental vector onto the ordination to obtain the contribution of each variable.

Usage

trans_env$cal_ordination_envfit(...)

Arguments

...: the parameters passed to vegan::envfit function.

Returns

res_ordination_envfit stored in the object.

Examples

\donttest{
t1$cal_ordination_envfit()
}

Method `trans_ordination()`

Transform ordination results for the following plot.

Usage

trans_env$trans_ordination(
  show_taxa = 10,
  adjust_arrow_length = FALSE,
  min_perc_env = 0.1,
  max_perc_env = 0.8,
  min_perc_tax = 0.1,
  max_perc_tax = 0.8
)

Arguments

show_taxa: default 10; taxa number shown in the plot.
adjust_arrow_length: default FALSE; whether adjust the arrow length to be clearer.
min_perc_env: default 0.1; used for scaling up the minimum of env arrow; multiply by the maximum distance between samples and origin.
max_perc_env: default 0.8; used for scaling up the maximum of env arrow; multiply by the maximum distance between samples and origin.
min_perc_tax: default 0.1; used for scaling up the minimum of tax arrow; multiply by the maximum distance between samples and origin.
max_perc_tax: default 0.8; used for scaling up the maximum of tax arrow; multiply by the maximum distance between samples and origin.

Returns

res_ordination_trans stored in the object.

Examples

\donttest{
t1$trans_ordination(adjust_arrow_length = TRUE, min_perc_env = 0.1, max_perc_env = 1)
}

Method `plot_ordination()`

plot ordination result.

Usage

trans_env$plot_ordination(
  plot_color = NULL,
  plot_shape = NULL,
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14),
  env_text_color = "black",
  env_arrow_color = "grey30",
  taxa_text_color = "firebrick1",
  taxa_arrow_color = "firebrick1",
  env_text_size = 3.7,
  taxa_text_size = 3,
  taxa_text_italic = TRUE,
  plot_type = "point",
  point_size = 3,
  point_alpha = 0.8,
  centroid_segment_alpha = 0.6,
  centroid_segment_size = 1,
  centroid_segment_linetype = 3,
  ellipse_chull_fill = TRUE,
  ellipse_chull_alpha = 0.1,
  ellipse_level = 0.9,
  ellipse_type = "t",
  add_sample_label = NULL,
  env_nudge_x = NULL,
  env_nudge_y = NULL,
  taxa_nudge_x = NULL,
  taxa_nudge_y = NULL,
  ...
)

Arguments

plot_color

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

plot_shape

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

color_values

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

shape_values

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.

env_text_color

default "black"; environmental variable text color.

env_arrow_color

default "grey30"; environmental variable arrow color.

taxa_text_color

default "firebrick1"; taxa text color.

taxa_arrow_color

default "firebrick1"; taxa arrow color.

env_text_size

default 3.7; environmental variable text size.

taxa_text_size

default 3; taxa text size.

taxa_text_italic

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

plot_type

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

point_size

default 3; point size in plot when "point" is in plot_type.

point_alpha

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

centroid_segment_alpha

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

centroid_segment_size

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

centroid_segment_linetype

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

ellipse_chull_fill

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

ellipse_chull_alpha

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

ellipse_level

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

ellipse_type

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

add_sample_label

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

env_nudge_x

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.

env_nudge_y

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).

taxa_nudge_x

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).

taxa_nudge_y

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.

Returns

ggplot object.

Examples

\donttest{
t1$cal_ordination(method = "RDA")
t1$trans_ordination(adjust_arrow_length = TRUE, max_perc_env = 1.5)
t1$plot_ordination(plot_color = "Group")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "chull"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"), 
	  centroid_segment_linetype = 1)
t1$plot_ordination(plot_color = "Group", env_nudge_x = c(0.4, 0, 0, 0, 0, -0.2, 0, 0), 
	  env_nudge_y = c(0.6, 0, 0.2, 0.5, 0, 0.1, 0, 0.2))
}

Method `cal_mantel()`

Mantel test between beta diversity matrix and environmental data.

Usage

trans_env$cal_mantel(
  partial_mantel = FALSE,
  add_matrix = NULL,
  use_measure = NULL,
  method = "pearson",
  p_adjust_method = "fdr",
  by_group = NULL,
  ...
)

Arguments

partial_mantel: default FALSE; whether use partial mantel test; If TRUE, use other all measurements as the zdis in each calculation.
add_matrix: default NULL; additional distance matrix provided when the beta diversity matrix in the dataset is not used.
use_measure: default NULL; a name of beta diversity matrix. If necessary and not provided, use the first beta diversity matrix.
method: default "pearson"; one of "pearson", "spearman" and "kendall"; correlation method; see method parameter in vegan::mantel function.
p_adjust_method: default "fdr"; p.adjust method; see method parameter of p.adjust function for available options.
by_group: 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.

Returns

res_mantel in object.

Examples

\donttest{
t1$cal_mantel(use_measure = "bray")
t1$cal_mantel(partial_mantel = TRUE, use_measure = "bray")
}

Method `cal_cor()`

Calculate the correlations between taxonomic abundance and environmental variables. Actually, it can also be applied to other correlation between any two variables from two tables.

Usage

trans_env$cal_cor(
  use_data = c("Genus", "all", "other")[1],
  cor_method = c("pearson", "spearman", "kendall", "maaslin2")[1],
  add_abund_table = NULL,
  filter_thres = 0,
  use_taxa_num = NULL,
  other_taxa = NULL,
  p_adjust_method = "fdr",
  p_adjust_type = c("All", "Taxa", "Env")[1],
  by_group = NULL,
  group_use = NULL,
  group_select = NULL,
  taxa_name_full = TRUE,
  tmp_input_maaslin2 = "tmp_input",
  tmp_output_maaslin2 = "tmp_output",
  ...
)

Arguments

use_data: default "Genus"; "Genus", "all" or "other"; "Genus" or other taxonomic name: use genus or other taxonomic abundance table in taxa_abund; "all": use all merged taxonomic abundance table; "other": provide additional taxa name with other_taxa parameter which is necessary.
cor_method: 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.
add_abund_table: default NULL; additional data table to be used. Row names must be sample names.
filter_thres: 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.
use_taxa_num: default NULL; integer; a number used to select high abundant taxa; only useful when use_data parameter is a taxonomic level, e.g., "Genus".
other_taxa: default NULL; character vector containing a series of feature names; used 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 see the example.
p_adjust_method: 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.
p_adjust_type: 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.
by_group: default NULL; one column name or number in sample_table; calculate correlations for different groups separately.
group_use: default NULL; numeric or character vector to select one column in sample_table for selecting samples; together with group_select.
group_select: default NULL; the group name used; remain samples within the group.
taxa_name_full: default TRUE; Whether use the complete taxonomic name of taxa.
tmp_input_maaslin2: default "tmp_input"; the temporary folder used to save the input files for Maaslin2.
tmp_output_maaslin2: default "tmp_output"; the temporary folder used to save the output files of Maaslin2.
...: parameters passed to Maaslin2 function of Maaslin2 package.

Returns

res_cor stored in the object.

Examples

\donttest{
t2 <- trans_diff$new(dataset = dataset, method = "rf", group = "Group", rf_taxa_level = "Genus")
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
t1$cal_cor(use_data = "other", p_adjust_method = "fdr", other_taxa = t2$res_diff$Taxa[1:40])
}

Method `plot_cor()`

Plot correlation heatmap.

Usage

trans_env$plot_cor(
  color_vector = c("#053061", "white", "#A50026"),
  color_palette = NULL,
  pheatmap = FALSE,
  filter_feature = NULL,
  filter_env = NULL,
  ylab_type_italic = FALSE,
  keep_full_name = FALSE,
  keep_prefix = TRUE,
  text_y_order = NULL,
  text_x_order = NULL,
  xtext_angle = 30,
  xtext_size = 10,
  xtext_color = "black",
  ytext_size = NULL,
  ytext_color = "black",
  sig_label_size = 4,
  font_family = NULL,
  cluster_ggplot = "none",
  cluster_height_rows = 0.2,
  cluster_height_cols = 0.2,
  text_y_position = "right",
  mylabels_x = NULL,
  na.value = "grey50",
  trans = "identity",
  ...
)

Arguments

color_vector: default c("#053061", "white", "#A50026"); colors with only three values representing low, middle and high values.
color_palette: default NULL; a customized palette with more color values to be used instead of the parameter color_vector.
pheatmap: default FALSE; whether use pheatmap package to plot the heatmap.
filter_feature: 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 "*".
filter_env: 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 "*".
ylab_type_italic: default FALSE; whether use italic type for y lab text.
keep_full_name: default FALSE; whether use the complete taxonomic name.
keep_prefix: default TRUE; whether retain the taxonomic prefix.
text_y_order: default NULL; character vector; provide customized text order for y axis; shown in the plot from the top down.
text_x_order: default NULL; character vector; provide customized text order for x axis.
xtext_angle: default 30; number ranging from 0 to 90; used to adjust x axis text angle.
xtext_size: default 10; x axis text size.
xtext_color: default "black"; x axis text color.
ytext_size: default NULL; y axis text size. NULL means default ggplot2 value.
ytext_color: default "black"; y axis text color.
sig_label_size: default 4; the size of significance label shown in the cell.
font_family: default NULL; font family used in ggplot2; only available when pheatmap = FALSE.
cluster_ggplot: 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.
cluster_height_rows: default 0.2, the dendrogram plot height for rows; available when cluster_ggplot is not "none".
cluster_height_cols: default 0.2, the dendrogram plot height for columns; available when cluster_ggplot is not "none".
text_y_position: default "right"; "left" or "right"; the y axis text position for ggplot2 based heatmap.
mylabels_x: default NULL; provide x axis text labels additionally; only available when pheatmap = TRUE.
na.value: default "grey50"; the color for the missing values when pheatmap = FALSE.
trans: 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.

Returns

plot.

Examples

\donttest{
t1$plot_cor(pheatmap = FALSE)
}

Method `plot_scatterfit()`

Scatter plot with fitted line based on the correlation or regression.
The most important thing is to make sure that the input x and y have correponding sample orders. If one of x and y is a matrix, the other will be also transformed to matrix with Euclidean distance. Then, both of them are transformed to be vectors. If x or y is a vector with a single value, x or y will be assigned according to the column selection of the data_env in the object.

Usage

trans_env$plot_scatterfit(
  x = NULL,
  y = NULL,
  group = NULL,
  group_order = NULL,
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  shape_values = NULL,
  type = c("cor", "lm")[1],
  cor_method = "pearson",
  label_sep = ";",
  label.x.npc = "left",
  label.y.npc = "top",
  label.x = NULL,
  label.y = NULL,
  x_axis_title = "",
  y_axis_title = "",
  point_size = 5,
  point_alpha = 0.6,
  line_size = 0.8,
  line_alpha = 1,
  line_color = "black",
  line_se = TRUE,
  line_se_color = "grey70",
  pvalue_trim = 4,
  cor_coef_trim = 3,
  lm_equation = TRUE,
  lm_fir_trim = 2,
  lm_sec_trim = 2,
  lm_squ_trim = 2,
  ...
)

Arguments

x

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.

y

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.

group

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).

group_order

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.

color_values

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

shape_values

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

type

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

cor_method

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

label_sep

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

label.x.npc

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.

label.y.npc

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

label.x

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

label.y

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

x_axis_title

default ""; the title of x axis.

y_axis_title

default ""; the title of y axis.

point_size

default 5; point size value.

point_alpha

default 0.6; alpha value for the point color transparency.

line_size

default 0.8; line size value.

line_alpha

default 1; alpha value for the line color transparency.

line_color

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

line_se

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

line_se_color

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

pvalue_trim

default 4; trim the decimal places of p value.

cor_coef_trim

default 3; trim the decimal places of correlation coefficient.

lm_equation

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

lm_fir_trim

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

lm_sec_trim

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

lm_squ_trim

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

...

other arguments passed to geom_text or geom_label.

Returns

ggplot.

Examples

\donttest{
t1$plot_scatterfit(x = 1, y = 2, type = "cor")
t1$plot_scatterfit(x = 1, y = 2, type = "lm", point_alpha = .3)
t1$plot_scatterfit(x = "pH", y = "TOC", type = "lm", group = "Group", line_se = FALSE)
t1$plot_scatterfit(x = 
	 dataset$beta_diversity$bray[rownames(t1$data_env), rownames(t1$data_env)], y = "pH")
}

Method `print()`

Print the trans_env object.

Usage

trans_env$print()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

trans_env$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `trans_env$new`
## ------------------------------------------------

data(dataset)
data(env_data_16S)
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])

## ------------------------------------------------
## Method `trans_env$cal_diff`
## ------------------------------------------------


t1$cal_diff(group = "Group", method = "KW")
t1$cal_diff(group = "Group", method = "anova")


## ------------------------------------------------
## Method `trans_env$cal_autocor`
## ------------------------------------------------

## Not run: 
# Spearman correlation
t1$cal_autocor(upper = list(continuous = GGally::wrap("cor", method= "spearman")))

## End(Not run)

## ------------------------------------------------
## Method `trans_env$cal_ordination`
## ------------------------------------------------


t1$cal_ordination(method = "dbRDA", use_measure = "bray")
t1$cal_ordination(method = "RDA", taxa_level = "Genus")
t1$cal_ordination(method = "CCA", taxa_level = "Genus")


## ------------------------------------------------
## Method `trans_env$cal_ordination_anova`
## ------------------------------------------------


t1$cal_ordination_anova()


## ------------------------------------------------
## Method `trans_env$cal_ordination_envfit`
## ------------------------------------------------


t1$cal_ordination_envfit()


## ------------------------------------------------
## Method `trans_env$trans_ordination`
## ------------------------------------------------


t1$trans_ordination(adjust_arrow_length = TRUE, min_perc_env = 0.1, max_perc_env = 1)


## ------------------------------------------------
## Method `trans_env$plot_ordination`
## ------------------------------------------------


t1$cal_ordination(method = "RDA")
t1$trans_ordination(adjust_arrow_length = TRUE, max_perc_env = 1.5)
t1$plot_ordination(plot_color = "Group")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "chull"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"), 
	  centroid_segment_linetype = 1)
t1$plot_ordination(plot_color = "Group", env_nudge_x = c(0.4, 0, 0, 0, 0, -0.2, 0, 0), 
	  env_nudge_y = c(0.6, 0, 0.2, 0.5, 0, 0.1, 0, 0.2))


## ------------------------------------------------
## Method `trans_env$cal_mantel`
## ------------------------------------------------


t1$cal_mantel(use_measure = "bray")
t1$cal_mantel(partial_mantel = TRUE, use_measure = "bray")


## ------------------------------------------------
## Method `trans_env$cal_cor`
## ------------------------------------------------


t2 <- trans_diff$new(dataset = dataset, method = "rf", group = "Group", rf_taxa_level = "Genus")
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
t1$cal_cor(use_data = "other", p_adjust_method = "fdr", other_taxa = t2$res_diff$Taxa[1:40])


## ------------------------------------------------
## Method `trans_env$plot_cor`
## ------------------------------------------------


t1$plot_cor(pheatmap = FALSE)


## ------------------------------------------------
## Method `trans_env$plot_scatterfit`
## ------------------------------------------------


t1$plot_scatterfit(x = 1, y = 2, type = "cor")
t1$plot_scatterfit(x = 1, y = 2, type = "lm", point_alpha = .3)
t1$plot_scatterfit(x = "pH", y = "TOC", type = "lm", group = "Group", line_se = FALSE)
t1$plot_scatterfit(x = 
	 dataset$beta_diversity$bray[rownames(t1$data_env), rownames(t1$data_env)], y = "pH")

[Package microeco version 1.8.0 Index]

Create trans_env object to analyze the association between environmental factor and microbial community.

Description

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method cal_diff()

Usage

Arguments

Returns

Examples

Method plot_diff()

Usage

Arguments

Method cal_autocor()

Usage

Arguments

Returns

Examples

Method cal_ordination()

Usage

Arguments

Returns

Examples

Method cal_ordination_anova()

Usage

Arguments

Returns

Examples

Method cal_ordination_envfit()

Usage

Arguments

Returns

Examples

Method trans_ordination()

Usage

Arguments

Returns

Examples

Method plot_ordination()

Usage

Arguments

Returns

Examples

Method cal_mantel()

Usage

Arguments

Returns

Examples

Method cal_cor()

Usage

Arguments

Returns

Examples

Method plot_cor()

Usage

Arguments

Returns

Examples

Method plot_scatterfit()

Usage

Arguments

Returns

Examples

Method print()

Usage

Method clone()

Usage

Arguments

Examples

Create `trans_env` object to analyze the association between environmental factor and microbial community.

Method `new()`

Method `cal_diff()`

Method `plot_diff()`

Method `cal_autocor()`

Method `cal_ordination()`

Method `cal_ordination_anova()`

Method `cal_ordination_envfit()`

Method `trans_ordination()`

Method `plot_ordination()`

Method `cal_mantel()`

Method `cal_cor()`

Method `plot_cor()`

Method `plot_scatterfit()`

Method `print()`

Method `clone()`