microeco (version 0.6.5)

trans_env: Create trans_env object for the analysis of the effects of environmental factors on communities.

Description

This class is a wrapper for a series of operations associated with environmental measurements, including redundancy analysis, mantel test and correlation analysis based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.

Methods

Public methods

• trans_env$new()

• trans_env$cal_rda()

• trans_env$cal_rda_envsquare()

• trans_env$trans_rda()

• trans_env$plot_rda()

• trans_env$cal_mantel()

• trans_env$cal_cor()

• trans_env$plot_cor()

• trans_env$plot_scatterfit()

• trans_env$print()

• trans_env$clone()

Method new()

Usage

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

Arguments

dataset

the object of microtable Class.

env_cols

default NULL; a vector to select columns in sample_table, when the environmental data is in sample_table. Either numeric vector or character vector of colnames.

add_data

default NULL; data.frame format; provide the environmental data frame individually; rownames should be sample names.

character2numeric

default TRUE; whether transform the characters or factors to numeric attributes.

complete_na

default FALSE; Whether fill the NA (missing value) in the environmental data.

Returns

env_data in trans_env object.

Examples

data(dataset)
data(env_data_16S)
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S)

Method cal_rda()

Redundancy analysis (RDA) based on the rda function in vegan package.

Usage

trans_env$cal_rda(
  use_dbrda = TRUE,
  add_matrix = NULL,
  use_measure = NULL,
  feature_sel = FALSE,
  taxa_level = NULL,
  taxa_filter_thres = NULL
)

Arguments

use_dbrda

default TRUE; whether use db-RDA, if FALSE, use RDA.

add_matrix

default NULL; additional distance matrix provided, if you do not want to use the beta diversity matrix within the dataset.

use_measure

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

feature_sel

default FALSE; whether perform the feature selection.

taxa_level

default NULL; If use RDA, provide the taxonomic rank.

taxa_filter_thres

default NULL; If want to filter taxa, provide the relative abundance threshold.

Returns

res_rda, res_rda_R2, res_rda_terms and res_rda_axis in object.

Examples

\donttest{
t1$cal_rda(use_dbrda = TRUE, use_measure = "bray")
}

Method cal_rda_envsquare()

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

Usage

trans_env$cal_rda_envsquare(...)

Arguments

...

the parameters passing to vegan::envfit function.

Returns

res_rda_envsquare in object.

Examples

\donttest{
t1$cal_rda_envsquare()
}

Method trans_rda()

transform RDA result for the following plotting.

Usage

trans_env$trans_rda(
  show_taxa = 10,
  adjust_arrow_length = FALSE,
  min_perc_env = 1,
  max_perc_env = 100,
  min_perc_tax = 1,
  max_perc_tax = 100
)

Arguments

show_taxa

default 10; taxa number shown in the plot.

adjust_arrow_length

default FALSE; whether adjust the arrow length to be clear

min_perc_env

default 1; minimum scale value for env arrow, relatively.

max_perc_env

default 100; maximum scale value for env arrow, relatively.

min_perc_tax

default 1; minimum scale value for tax arrow, relatively.

max_perc_tax

default 100; maximum scale value for tax arrow, relatively.

Returns

res_rda_trans in object.

Examples

\donttest{
t1$trans_rda(adjust_arrow_length = TRUE, max_perc_env = 10)
}

Method plot_rda()

plot RDA result.

Usage

trans_env$plot_rda(
  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),
  taxa_text_color = "firebrick1",
  taxa_text_type = "italic"
)

Arguments

plot_color

default NULL; group used for color.

plot_shape

default NULL; group used for shape.

color_values

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

shape_values

default see the function; vector used in the shape, see ggplot2 tutorial.

taxa_text_color

default "firebrick1"; taxa text colors.

taxa_text_type

default "italic"; taxa text style; better to use "italic" for Genus, use "normal" for others.

Returns

ggplot object.

Examples

\donttest{
t1$plot_rda(plot_color = "Group")
}

Method cal_mantel()

Mantel test between beta diversity matrix and environmental data.

Usage

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

Arguments

select_env_data

default NULL; numeric or character vector to select columns in env_data; if not provided, automatically select the columns with numeric attributes.

partial_mantel

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

add_matrix

default NULL; additional distance matrix provided, if you donot want to use the beta diversity matrix in the dataset.

use_measure

default NULL; 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.

...

paremeters pass to mantel.

Returns

res_mantel in object.

Examples

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

Method cal_cor()

Calculating the correlations between taxa abundance and environmental variables. Indeed, it can also be used for calculating other correlation between any two variables from two tables.

Usage

trans_env$cal_cor(
  use_data = c("Genus", "all", "other")[1],
  select_env_data = NULL,
  cor_method = c("pearson", "spearman", "kendall")[1],
  p_adjust_method = "fdr",
  p_adjust_type = c("Type", "Taxa", "Env")[3],
  add_abund_table = NULL,
  by_group = NULL,
  use_taxa_num = NULL,
  other_taxa = NULL,
  group_use = NULL,
  group_select = NULL,
  taxa_name_full = TRUE
)

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 taxa abundance table; "other": provide additional taxa name with other_taxa parameter which is necessary.

select_env_data

default NULL; numeric or character vector to select columns in env_data; if not provided, automatically select the columns with numeric attributes.

cor_method

default "pearson"; "pearson", "spearman" or "kendall"; correlation method.

p_adjust_method

default "fdr"; p.adjust method.

p_adjust_type

default "Env"; "Type", "Taxa" or "Env"; p.adjust type; Env: environmental data; Taxa: taxa data; Type: group used.

add_abund_table

default NULL; additional data table to be used. Samples must be rows.

by_group

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

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; provide additional taxa, see use_data parameter.

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; will retain samples within the group.

taxa_name_full

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

Returns

res_cor in 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_rf$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,
  ylab_type_italic = FALSE,
  keep_full_name = FALSE,
  keep_prefix = TRUE,
  plot_x_size = 9,
  mylabels_x = NULL,
  font_family = NULL,
  ...
)

Arguments

color_vector

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

color_palette

default NULL; a customized palette with more color values; if provided, use it instead of color_vector.

pheatmap

default FALSE; whether use heatmap with clustering plot.

filter_feature

default NULL; character vector; used to filter features that only have significance labels in the filter_feature vector. For example, filter_feature = "" can be used to filter 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.

plot_x_size

default 9; x axis text size.

mylabels_x

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

font_family

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

...

paremeters pass to ggplot2::geom_tile or pheatmap, depending on the pheatmap = FALSE or TRUE.

Returns

plot.

Examples

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

Method plot_scatterfit()

Scatter plot and add fitted line. 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 env_data inside.

Usage

trans_env$plot_scatterfit(
  x = NULL,
  y = NULL,
  use_cor = TRUE,
  cor_method = "pearson",
  add_line = TRUE,
  use_se = TRUE,
  text_x_pos = NULL,
  text_y_pos = NULL,
  x_axis_title = "",
  y_axis_title = "",
  pvalue_trim = 4,
  cor_coef_trim = 3,
  lm_fir_trim = 2,
  lm_sec_trim = 2,
  lm_squ_trim = 2,
  ...
)

Arguments

x

default NULL; a single numeric or character value or 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 env_data inside. If x is a distance matrix, it will be transformed to be a vector.

y

default NULL; a single numeric or character value or 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 env_data inside. If y is a distance matrix, it will be transformed to be a vector.

use_cor

default TRUE; TRUE for correlation; FALSE for regression.

cor_method

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

add_line

default TRUE; whether add the fitted line in the plot.

use_se

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

text_x_pos

default NULL; the central x axis position of the fitting text.

text_y_pos

default NULL; the central y axis position of the fitting text.

x_axis_title

default ""; the title of x axis.

y_axis_title

default ""; the title of y axis.

pvalue_trim

default 4; trim the decimal places of p value.

cor_coef_trim

default 3; trim the decimal places of correlation coefficient.

lm_fir_trim

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

lm_sec_trim

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

lm_squ_trim

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

...

the parameters passing to ggplot2::geom_point function.

Returns

plot.

Examples

\donttest{
t1$plot_scatterfit(x = 1, y = 2, alpha = .5)
}

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

# NOT RUN {
## ------------------------------------------------
## Method `trans_env$new`
## ------------------------------------------------

data(dataset)
data(env_data_16S)
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S)

## ------------------------------------------------
## Method `trans_env$cal_rda`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_rda(use_dbrda = TRUE, use_measure = "bray")
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$cal_rda_envsquare`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_rda_envsquare()
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$trans_rda`
## ------------------------------------------------

# }
# NOT RUN {
t1$trans_rda(adjust_arrow_length = TRUE, max_perc_env = 10)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$plot_rda`
## ------------------------------------------------

# }
# NOT RUN {
t1$plot_rda(plot_color = "Group")
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$cal_mantel`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_mantel(use_measure = "bray")
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$cal_cor`
## ------------------------------------------------

# }
# NOT RUN {
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_rf$Taxa[1:40])
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$plot_cor`
## ------------------------------------------------

# }
# NOT RUN {
t1$plot_cor(pheatmap = FALSE)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_env$plot_scatterfit`
## ------------------------------------------------

# }
# NOT RUN {
t1$plot_scatterfit(x = 1, y = 2, alpha = .5)
# }