trans_env: Create 'trans_env' object to analyze the association between...
In microeco: Microbial Community Ecology Data Analysis
trans_env 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
Public methods
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Method new()
Usage
trans_env$new(
dataset = NULL,
env_cols = NULL,
add_data = NULL,
character2numeric = FALSE,
standardize = FALSE,
complete_na = FALSE
)
Arguments
datasetthe object of microtable Class.
env_colsdefault NULL; either numeric vector or character vector to select columns in microtable$sample_table, i.e. dataset$sample_table.
This parameter should be used in the case that all the required environmental data is in sample_table of your microtable object.
Otherwise, please use add_data parameter.
add_datadefault NULL; data.frame format; provide the environmental data in the format data.frame; rownames should be sample names.
This parameter should be used when the microtable$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.
character2numericdefault FALSE; whether convert the characters or factors to numeric values.
standardizedefault FALSE; whether scale environmental variables to zero mean and unit variance.
complete_nadefault 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", "lme")[1],
...
)
Arguments
groupdefault NULL; a colname of sample_table used to compare values across groups.
by_groupdefault NULL; perform differential test among groups (group parameter) within each group (by_group parameter).
methoddefault "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
- 'lme'
lme: Linear Mixed Effect Model based on the lmerTest package
...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 = "KW_dunn")
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$cal_autocor(
group = NULL,
ggpairs = TRUE,
color_values = RColorBrewer::brewer.pal(8, "Dark2"),
alpha = 0.8,
...
)
Arguments
groupdefault NULL; a colname of sample_table; used to perform calculations for different groups.
ggpairsdefault TRUE; whether use GGally::ggpairs function to plot the correlation results.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); colors palette.
alphadefault 0.8; the alpha value to add transparency in colors; useful when group is not NULL.
...parameters passed to GGally::ggpairs when ggpairs = TRUE or
passed to cal_cor of trans_env class when ggpairs = FALSE.
Returns
ggmatrix when ggpairs = TRUE or data.frame object when ggpairs = FALSE.
Examples
\dontrun{
# Spearman correlation
t1$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$cal_ordination(
method = c("RDA", "dbRDA", "CCA")[1],
feature_sel = FALSE,
taxa_level = NULL,
taxa_filter_thres = NULL,
use_measure = NULL,
add_matrix = NULL,
...
)
Arguments
methoddefault c("RDA", "dbRDA", "CCA")[1]; the ordination method.
feature_seldefault FALSE; whether perform the feature selection based on forward selection method.
taxa_leveldefault NULL; If use RDA or CCA, provide the taxonomic rank, such as "Phylum" or "Genus";
If use otu_table; please set taxa_level = "OTU".
taxa_filter_thresdefault NULL; relative abundance threshold used to filter taxa when method is "RDA" or "CCA".
use_measuredefault NULL; a name of beta diversity matrix; only available when parameter method = "dbRDA";
If not provided, use the first beta diversity matrix in the microtable$beta_diversity automatically.
add_matrixdefault 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_taxadefault 10; taxa number shown in the plot.
adjust_arrow_lengthdefault FALSE; whether adjust the arrow length to be clearer.
min_perc_envdefault 0.1; used for scaling up the minimum of env arrow; multiply by the maximum distance between samples and origin.
max_perc_envdefault 0.8; used for scaling up the maximum of env arrow; multiply by the maximum distance between samples and origin.
min_perc_taxdefault 0.1; used for scaling up the minimum of tax arrow; multiply by the maximum distance between samples and origin.
max_perc_taxdefault 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_colordefault NULL; a colname of sample_table to assign colors to different groups.
plot_shapedefault NULL; a colname of sample_table to assign shapes to different groups.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.
shape_valuesdefault 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_colordefault "black"; environmental variable text color.
env_arrow_colordefault "grey30"; environmental variable arrow color.
taxa_text_colordefault "firebrick1"; taxa text color.
taxa_arrow_colordefault "firebrick1"; taxa arrow color.
env_text_sizedefault 3.7; environmental variable text size.
taxa_text_sizedefault 3; taxa text size.
taxa_text_italicdefault TRUE; "italic"; whether use "italic" style for the taxa text.
plot_typedefault "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_sizedefault 3; point size in plot when "point" is in plot_type.
point_alphadefault .8; point transparency in plot when "point" is in plot_type.
centroid_segment_alphadefault 0.6; segment transparency in plot when "centroid" is in plot_type.
centroid_segment_sizedefault 1; segment size in plot when "centroid" is in plot_type.
centroid_segment_linetypedefault 3; an integer; the line type related with centroid in plot when "centroid" is in plot_type.
ellipse_chull_filldefault TRUE; whether fill colors to the area of ellipse or chull.
ellipse_chull_alphadefault 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in plot_type.
ellipse_leveldefault .9; confidence level of ellipse when "ellipse" is in plot_type.
ellipse_typedefault "t"; ellipse type when "ellipse" is in plot_type; see type in stat_ellipse.
add_sample_labeldefault NULL; the column name in sample table, if provided, show the point name in plot.
env_nudge_xdefault 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_ydefault 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_xdefault 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_ydefault 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_manteldefault FALSE; whether use partial mantel test; If TRUE, use other all measurements as the zdis in each calculation.
add_matrixdefault NULL; additional distance matrix provided when the beta diversity matrix in the dataset is not used.
use_measuredefault NULL; a name of beta diversity matrix. If necessary and not provided, use the first beta diversity matrix.
methoddefault "pearson"; one of "pearson", "spearman" and "kendall"; correlation method; see method parameter in vegan::mantel function.
p_adjust_methoddefault "fdr"; p.adjust method; see method parameter of p.adjust function for available options.
by_groupdefault 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", "Type", "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_datadefault "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_methoddefault "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_tabledefault NULL; additional data table to be used. Samples must be rows.
filter_thresdefault 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_numdefault NULL; integer; a number used to select high abundant taxa; only useful when use_data parameter is a taxonomic level, e.g., "Genus".
other_taxadefault 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_methoddefault "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_typedefault "All"; "All", "Type", "Taxa" or "Env"; P value adjustment type.
"Env": adjustment for each environmental variable separately;
"Taxa": adjustment for each taxon separately;
"Type": adjustment according to the groups provided. If by_group is NULL, adjustment is performed for all the data together.
If by_group is provided, for each group in it separately.
These three options are the first three colnames of return table res_cor.
"All": adjustment for all the data together no matter whether by_group is provided. If by_group is NULL, it is same with the "Type" option.
by_groupdefault NULL; one column name or number in sample_table; calculate correlations for different groups separately.
group_usedefault NULL; numeric or character vector to select one column in sample_table for selecting samples; together with group_select.
group_selectdefault NULL; the group name used; remain samples within the group.
taxa_name_fulldefault TRUE; Whether use the complete taxonomic name of taxa.
tmp_input_maaslin2default "tmp_input"; the temporary folder used to save the input files for Maaslin2.
tmp_output_maaslin2default "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])
t1$cal_cor(use_data = "other", p_adjust_type = "Env", other_taxa = t2$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,
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_vectordefault c("#053061", "white", "#A50026"); colors with only three values representing low, middle and high values.
color_palettedefault NULL; a customized palette with more color values to be used instead of the parameter color_vector.
pheatmapdefault FALSE; whether use pheatmap package to plot the heatmap.
filter_featuredefault 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_envdefault 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_italicdefault FALSE; whether use italic type for y lab text.
keep_full_namedefault FALSE; whether use the complete taxonomic name.
keep_prefixdefault TRUE; whether retain the taxonomic prefix.
text_y_orderdefault NULL; character vector; provide customized text order for y axis; shown in the plot from the top down.
text_x_orderdefault NULL; character vector; provide customized text order for x axis.
xtext_angledefault 30; number ranging from 0 to 90; used to adjust x axis text angle.
xtext_sizedefault 10; x axis text size.
font_familydefault NULL; font family used in ggplot2; only available when pheatmap = FALSE.
cluster_ggplotdefault "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_rowsdefault 0.2, the dendrogram plot height for rows; available when cluster_ggplot is not "none".
cluster_height_colsdefault 0.2, the dendrogram plot height for columns; available when cluster_ggplot is not "none".
text_y_positiondefault "right"; "left" or "right"; the y axis text position for ggplot2 based heatmap.
mylabels_xdefault NULL; provide x axis text labels additionally; only available when pheatmap = TRUE.
na.valuedefault "grey50"; the color for the missing values when pheatmap = FALSE.
transdefault "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
xdefault 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.
ydefault 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.
groupdefault 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_orderdefault 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_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.
shape_valuesdefault NULL; a numeric vector for point shape types of groups when group is not NULL, see ggplot2 tutorial.
typedefault c("cor", "lm")[1]; "cor": correlation; "lm" for regression.
cor_methoddefault "pearson"; one of "pearson", "kendall" and "spearman"; correlation method.
label_sepdefault ";"; the separator string between different label parts.
label.x.npcdefault "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.npcdefault "top"; same usage with label.x.npc; also see label.y.npc parameter of ggpubr::stat_cor function.
label.xdefault NULL; x axis absolute position for adding the statistic label.
label.ydefault NULL; x axis absolute position for adding the statistic label.
x_axis_titledefault ""; the title of x axis.
y_axis_titledefault ""; the title of y axis.
point_sizedefault 5; point size value.
point_alphadefault 0.6; alpha value for the point color transparency.
line_sizedefault 0.8; line size value.
line_alphadefault 1; alpha value for the line color transparency.
line_colordefault "black"; fitted line color; only available when group = NULL.
line_sedefault TRUE; Whether show the confidence interval for the fitting.
line_se_colordefault "grey70"; the color to fill the confidence interval when line_se = TRUE.
pvalue_trimdefault 4; trim the decimal places of p value.
cor_coef_trimdefault 3; trim the decimal places of correlation coefficient.
lm_equationdefault TRUE; whether include the equation in the label when type = "lm".
lm_fir_trimdefault 2; trim the decimal places of first coefficient in regression.
lm_sec_trimdefault 2; trim the decimal places of second coefficient in regression.
lm_squ_trimdefault 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
deepWhether 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$cal_diff(group = "Group", method = "KW_dunn")
t1$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$Taxa[1:40])
t1$cal_cor(use_data = "other", p_adjust_type = "Env", 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$data_env)], y = "pH")
microeco documentation built on Nov. 18, 2023, 9:06 a.m.
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| trans_env | R Documentation |
Create trans_env object to analyze the association between environmental factor and microbial community.
Description
This class is a wrapper for a series of operations associated with environmental measurements, including redundancy analysis, mantel test, correlation analysis and linear fitting.
Methods
Public methods
Method new()
Usage
trans_env$new( dataset = NULL, env_cols = NULL, add_data = NULL, character2numeric = FALSE, standardize = FALSE, complete_na = FALSE )
Arguments
datasetthe object of
microtableClass.env_colsdefault 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 insample_tableof yourmicrotableobject. Otherwise, please useadd_dataparameter.add_datadefault NULL;
data.frameformat; provide the environmental data in the formatdata.frame; rownames should be sample names. This parameter should be used when themicrotable$sample_tableobject does not have environmental data. Under this circumstance, theenv_colsparameter can not be used because no data can be selected.character2numericdefault FALSE; whether convert the characters or factors to numeric values.
standardizedefault FALSE; whether scale environmental variables to zero mean and unit variance.
complete_nadefault FALSE; Whether fill the NA (missing value) in the environmental data; If TRUE, the function can run the interpolation with the
micepackage.
Returns
data_env stored in the object.
Examples
data(dataset) data(env_data_16S) t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
Method cal_diff()
Differential test of environmental variables across groups.
Usage
trans_env$cal_diff(
group = NULL,
by_group = NULL,
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lme")[1],
...
)Arguments
groupdefault NULL; a colname of
sample_tableused to compare values across groups.by_groupdefault NULL; perform differential test among groups (
groupparameter) within each group (by_groupparameter).methoddefault "KW"; see the following available options:
- 'KW'
KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)
- 'KW_dunn'
Dunn's Kruskal-Wallis Multiple Comparisons, see
dunnTestfunction inFSApackage- 'wilcox'
Wilcoxon Rank Sum and Signed Rank Tests for all paired groups
- 't.test'
Student's t-Test for all paired groups
- 'anova'
Duncan's new multiple range test for one-way anova; see
duncan.testfunction ofagricolaepackage. For multi-factor anova, seeaov- 'scheirerRayHare'
Scheirer Ray Hare test for nonparametric test used for a two-way factorial experiment; see
scheirerRayHarefunction ofrcompanionpackage- 'lme'
lme: Linear Mixed Effect Model based on the
lmerTestpackage
...parameters passed to
cal_difffunction oftrans_alphaclass.
Returns
res_diff stored in the object.
In the data frame, 'Group' column means that the group has the maximum median or mean value across the test groups;
For non-parametric methods, median value; For t.test, mean value.
Examples
\donttest{
t1$cal_diff(group = "Group", method = "KW")
t1$cal_diff(group = "Group", method = "KW_dunn")
t1$cal_diff(group = "Group", method = "anova")
}
Method plot_diff()
Plot environmental variables across groups and add the significance label.
Usage
trans_env$plot_diff(...)
Arguments
...parameters passed to
plot_alphaintrans_alphaclass. Please seeplot_alphafunction oftrans_alphafor all the available parameters.
Method cal_autocor()
Calculate the autocorrelations among environmental variables.
Usage
trans_env$cal_autocor( group = NULL, ggpairs = TRUE, color_values = RColorBrewer::brewer.pal(8, "Dark2"), alpha = 0.8, ... )
Arguments
groupdefault NULL; a colname of sample_table; used to perform calculations for different groups.
ggpairsdefault TRUE; whether use
GGally::ggpairsfunction to plot the correlation results.color_valuesdefault
RColorBrewer::brewer.pal(8, "Dark2"); colors palette.alphadefault 0.8; the alpha value to add transparency in colors; useful when group is not NULL.
...parameters passed to
GGally::ggpairswhenggpairs = TRUEor passed tocal_coroftrans_envclass whenggpairs = 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
methoddefault c("RDA", "dbRDA", "CCA")[1]; the ordination method.
feature_seldefault FALSE; whether perform the feature selection based on forward selection method.
taxa_leveldefault 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_thresdefault NULL; relative abundance threshold used to filter taxa when method is "RDA" or "CCA".
use_measuredefault NULL; a name of beta diversity matrix; only available when parameter
method = "dbRDA"; If not provided, use the first beta diversity matrix in themicrotable$beta_diversityautomatically.add_matrixdefault NULL; additional distance matrix provided, when the user does not want to use the beta diversity matrix within the dataset; only available when method = "dbRDA".
...paremeters passed to
dbrda,rdaorccafunction according to themethodparameter.
Returns
res_ordination and res_ordination_R2 stored in the object.
Examples
\donttest{
t1$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$cal_ordination_anova(...)
Arguments
...parameters passed to
anovafunction.
Returns
res_ordination_terms and res_ordination_axis stored in the object.
Examples
\donttest{
t1$cal_ordination_anova()
}
Method cal_ordination_envfit()
Fit each environmental vector onto the ordination to obtain the contribution of each variable.
Usage
trans_env$cal_ordination_envfit(...)
Arguments
...the parameters passed to
vegan::envfitfunction.
Returns
res_ordination_envfit stored in the object.
Examples
\donttest{
t1$cal_ordination_envfit()
}
Method trans_ordination()
Transform ordination results for the following plot.
Usage
trans_env$trans_ordination( show_taxa = 10, adjust_arrow_length = FALSE, min_perc_env = 0.1, max_perc_env = 0.8, min_perc_tax = 0.1, max_perc_tax = 0.8 )
Arguments
show_taxadefault 10; taxa number shown in the plot.
adjust_arrow_lengthdefault FALSE; whether adjust the arrow length to be clearer.
min_perc_envdefault 0.1; used for scaling up the minimum of env arrow; multiply by the maximum distance between samples and origin.
max_perc_envdefault 0.8; used for scaling up the maximum of env arrow; multiply by the maximum distance between samples and origin.
min_perc_taxdefault 0.1; used for scaling up the minimum of tax arrow; multiply by the maximum distance between samples and origin.
max_perc_taxdefault 0.8; used for scaling up the maximum of tax arrow; multiply by the maximum distance between samples and origin.
Returns
res_ordination_trans stored in the object.
Examples
\donttest{
t1$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_colordefault NULL; a colname of
sample_tableto assign colors to different groups.plot_shapedefault NULL; a colname of
sample_tableto assign shapes to different groups.color_valuesdefault
RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.shape_valuesdefault 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_colordefault "black"; environmental variable text color.
env_arrow_colordefault "grey30"; environmental variable arrow color.
taxa_text_colordefault "firebrick1"; taxa text color.
taxa_arrow_colordefault "firebrick1"; taxa arrow color.
env_text_sizedefault 3.7; environmental variable text size.
taxa_text_sizedefault 3; taxa text size.
taxa_text_italicdefault TRUE; "italic"; whether use "italic" style for the taxa text.
plot_typedefault "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_sizedefault 3; point size in plot when "point" is in
plot_type.point_alphadefault .8; point transparency in plot when "point" is in
plot_type.centroid_segment_alphadefault 0.6; segment transparency in plot when "centroid" is in
plot_type.centroid_segment_sizedefault 1; segment size in plot when "centroid" is in
plot_type.centroid_segment_linetypedefault 3; an integer; the line type related with centroid in plot when "centroid" is in
plot_type.ellipse_chull_filldefault TRUE; whether fill colors to the area of ellipse or chull.
ellipse_chull_alphadefault 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in
plot_type.ellipse_leveldefault .9; confidence level of ellipse when "ellipse" is in
plot_type.ellipse_typedefault "t"; ellipse type when "ellipse" is in
plot_type; see type instat_ellipse.add_sample_labeldefault NULL; the column name in sample table, if provided, show the point name in plot.
env_nudge_xdefault NULL; numeric vector to adjust the env text x axis position; passed to nudge_x parameter of
ggrepel::geom_text_repelfunction; default NULL represents automatic adjustment; the length must be same with the row number ofobject$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_x should be something likec(0.1, 0, -0.2, 0, 0). Note that this parameter and env_nudge_y is generally used when the automatic text adjustment is not very well.env_nudge_ydefault NULL; numeric vector to adjust the env text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of
object$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_y should be something likec(0.1, 0, -0.2, 0, 0).taxa_nudge_xdefault NULL; numeric vector to adjust the taxa text x axis position; passed to nudge_x parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of
object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_x should be something likec(0.3, -0.2, 0).taxa_nudge_ydefault NULL; numeric vector to adjust the taxa text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of
object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_y should be something likec(-0.2, 0, 0.4)....paremeters passed to
geom_pointfor controlling sample points.
Returns
ggplot object.
Examples
\donttest{
t1$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$cal_mantel( partial_mantel = FALSE, add_matrix = NULL, use_measure = NULL, method = "pearson", p_adjust_method = "fdr", by_group = NULL, ... )
Arguments
partial_manteldefault FALSE; whether use partial mantel test; If TRUE, use other all measurements as the zdis in each calculation.
add_matrixdefault NULL; additional distance matrix provided when the beta diversity matrix in the dataset is not used.
use_measuredefault NULL; a name of beta diversity matrix. If necessary and not provided, use the first beta diversity matrix.
methoddefault "pearson"; one of "pearson", "spearman" and "kendall"; correlation method; see method parameter in
vegan::mantelfunction.p_adjust_methoddefault "fdr"; p.adjust method; see method parameter of
p.adjustfunction for available options.by_groupdefault NULL; one column name or number in sample_table; used to perform mantel test for different groups separately.
...paremeters passed to
mantelof vegan package.
Returns
res_mantel in object.
Examples
\donttest{
t1$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", "Type", "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_datadefault "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 withother_taxaparameter which is necessary.cor_methoddefault "pearson"; "pearson", "spearman", "kendall" or "maaslin2"; correlation method. "pearson", "spearman" or "kendall" all refer to the correlation analysis based on the
cor.testfunction in R. "maaslin2" is the method inMaaslin2package for finding associations between metadata and potentially high-dimensional microbial multi-omics data.add_abund_tabledefault NULL; additional data table to be used. Samples must be rows.
filter_thresdefault 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_numdefault NULL; integer; a number used to select high abundant taxa; only useful when
use_dataparameter is a taxonomic level, e.g., "Genus".other_taxadefault 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_methoddefault "fdr"; p.adjust method; see method parameter of
p.adjustfunction for available options.p_adjust_method = "none"can disable the p value adjustment.p_adjust_typedefault "All"; "All", "Type", "Taxa" or "Env"; P value adjustment type. "Env": adjustment for each environmental variable separately; "Taxa": adjustment for each taxon separately; "Type": adjustment according to the groups provided. If
by_groupis NULL, adjustment is performed for all the data together. Ifby_groupis provided, for each group in it separately. These three options are the first three colnames of return tableres_cor. "All": adjustment for all the data together no matter whetherby_groupis provided. Ifby_groupis NULL, it is same with the "Type" option.by_groupdefault NULL; one column name or number in sample_table; calculate correlations for different groups separately.
group_usedefault NULL; numeric or character vector to select one column in sample_table for selecting samples; together with group_select.
group_selectdefault NULL; the group name used; remain samples within the group.
taxa_name_fulldefault TRUE; Whether use the complete taxonomic name of taxa.
tmp_input_maaslin2default "tmp_input"; the temporary folder used to save the input files for Maaslin2.
tmp_output_maaslin2default "tmp_output"; the temporary folder used to save the output files of Maaslin2.
...parameters passed to
Maaslin2function ofMaaslin2package.
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$Taxa[1:40])
t1$cal_cor(use_data = "other", p_adjust_type = "Env", 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,
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_vectordefault
c("#053061", "white", "#A50026"); colors with only three values representing low, middle and high values.color_palettedefault NULL; a customized palette with more color values to be used instead of the parameter
color_vector.pheatmapdefault FALSE; whether use pheatmap package to plot the heatmap.
filter_featuredefault NULL; character vector; used to filter features that only have labels in the
filter_featurevector. For example,filter_feature = ""can be used to remove features that only have "", no any "*".filter_envdefault NULL; character vector; used to filter environmental variables that only have labels in the
filter_envvector. For example,filter_env = ""can be used to remove features that only have "", no any "*".ylab_type_italicdefault FALSE; whether use italic type for y lab text.
keep_full_namedefault FALSE; whether use the complete taxonomic name.
keep_prefixdefault TRUE; whether retain the taxonomic prefix.
text_y_orderdefault NULL; character vector; provide customized text order for y axis; shown in the plot from the top down.
text_x_orderdefault NULL; character vector; provide customized text order for x axis.
xtext_angledefault 30; number ranging from 0 to 90; used to adjust x axis text angle.
xtext_sizedefault 10; x axis text size.
font_familydefault NULL; font family used in
ggplot2; only available whenpheatmap = FALSE.cluster_ggplotdefault "none"; add clustering dendrogram for
ggplot2based 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 whenpheatmap = FALSE.cluster_height_rowsdefault 0.2, the dendrogram plot height for rows; available when
cluster_ggplotis not "none".cluster_height_colsdefault 0.2, the dendrogram plot height for columns; available when
cluster_ggplotis not "none".text_y_positiondefault "right"; "left" or "right"; the y axis text position for ggplot2 based heatmap.
mylabels_xdefault NULL; provide x axis text labels additionally; only available when
pheatmap = TRUE.na.valuedefault "grey50"; the color for the missing values when
pheatmap = FALSE.transdefault "identity"; the transformation for continuous scales in the legend when
pheatmap = FALSE; see thetransitem inggplot2::scale_colour_gradientn....paremeters passed to
ggplot2::geom_tileorpheatmap::pheatmap, depending on the parameterpheatmapis 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
xdefault 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_envin the object. If x is a distance matrix, it will be transformed to be a vector.ydefault 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_envin the object. If y is a distance matrix, it will be transformed to be a vector.groupdefault NULL; a character vector; if length is 1, must be a colname of
sample_tablein 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_orderdefault 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_tableis factor. If group_order is provided, disable the group orders or factor levels in thegroupcolumn ofsample_table.color_valuesdefault
RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.shape_valuesdefault NULL; a numeric vector for point shape types of groups when group is not NULL, see ggplot2 tutorial.
typedefault c("cor", "lm")[1]; "cor": correlation; "lm" for regression.
cor_methoddefault "pearson"; one of "pearson", "kendall" and "spearman"; correlation method.
label_sepdefault ";"; the separator string between different label parts.
label.x.npcdefault "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.npcdefault "top"; same usage with label.x.npc; also see
label.y.npcparameter ofggpubr::stat_corfunction.label.xdefault NULL; x axis absolute position for adding the statistic label.
label.ydefault NULL; x axis absolute position for adding the statistic label.
x_axis_titledefault ""; the title of x axis.
y_axis_titledefault ""; the title of y axis.
point_sizedefault 5; point size value.
point_alphadefault 0.6; alpha value for the point color transparency.
line_sizedefault 0.8; line size value.
line_alphadefault 1; alpha value for the line color transparency.
line_colordefault "black"; fitted line color; only available when
group = NULL.line_sedefault TRUE; Whether show the confidence interval for the fitting.
line_se_colordefault "grey70"; the color to fill the confidence interval when
line_se = TRUE.pvalue_trimdefault 4; trim the decimal places of p value.
cor_coef_trimdefault 3; trim the decimal places of correlation coefficient.
lm_equationdefault TRUE; whether include the equation in the label when
type = "lm".lm_fir_trimdefault 2; trim the decimal places of first coefficient in regression.
lm_sec_trimdefault 2; trim the decimal places of second coefficient in regression.
lm_squ_trimdefault 2; trim the decimal places of R square in regression.
...other arguments passed to
geom_textorgeom_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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_env$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 = "KW_dunn")
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])
t1$cal_cor(use_data = "other", p_adjust_type = "Env", other_taxa = t2$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")
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