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microeco (version 0.3.3)

trans_network: Create trans_network object for co-occurrence network analysis.

Description

This class is a wrapper for a series of network analysis related methods, including the correlation based <doi:10.1186/1471-2105-13-113>, SpiecEasi <doi:10.1371/journal.pcbi.1004226>, and Probabilistic Graphical Models based <doi:10.1016/j.cels.2019.08.002> network construction approaches, network and node attributes analysis and other network operations.

Arguments

Methods

Public methods

Method new()

Usage

trans_network$new(
  dataset = NULL,
  cor_method = c("pearson", "spearman", "kendall")[1],
  cal_cor = c("base", "WGCNA", "SparCC", NA)[1],
  taxa_level = "OTU",
  filter_thres = 0,
  nThreads = 1,
  SparCC_simu_num = 100,
  env_cols = NULL,
  add_data = NULL
)

Arguments

dataset

the object of microtable Class.

cor_method

default "pearson"; "pearson", "spearman" or "kendall"; correlation algorithm, only use for correlation based network.

cal_cor

default "base"; "base", "WGCNA", "SparCC" or NA; correlation method; NA represent do not calculate correlations, used for non-correlation based network.

taxa_level

default "OTU"; taxonomic rank.

filter_thres

default 0; the relative abundance threshold.

nThreads

default 1; the thread number used for "WGCNA" and SparCC.

SparCC_simu_num

default 100; SparCC simulation number for bootstrap.

env_cols

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

add_data

default NULL; provide physicochemical table additionally.

Returns

res_cor_p list.

Examples

\donttest{
data(dataset)
# correlation network
t1 <- trans_network$new(dataset = dataset, cal_cor = "base", 
  taxa_level = "OTU", filter_thres = 0.001)
}

Method cal_network()

Calculate network either based on the correlation method or based on SpiecEasi or based on the Probabilistic Graphical Models (PGM) in julia FlashWeave; see Deng et al. (2012) <doi:10.1186/1471-2105-13-113> for correlation based method; see Kurtz et al. (2015) <doi:doi:10.1371/journal.pcbi.1004226> for SpiecEasi method; see Tackmann et al. (2019) <doi:10.1016/j.cels.2019.08.002> for PGM based method.

Usage

trans_network$cal_network(
  network_method = c("COR", "SpiecEasi", "PGM")[1],
  p_thres = 0.01,
  COR_weight = TRUE,
  COR_p_adjust = "fdr",
  COR_cut = 0.6,
  COR_low_threshold = 0.4,
  COR_optimization = FALSE,
  PGM_meta_data = FALSE,
  PGM_sensitive = "true",
  PGM_heterogeneous = "true",
  SpiecEasi_method = "mb",
  with_module = TRUE,
  add_taxa_name = "Phylum",
  usename_rawtaxa_when_taxalevel_notOTU = FALSE,
  ...
)

Arguments

network_method

default "COR"; "COR", "SpiecEasi" or "PGM"; COR: correlation based method; PGM: Probabilistic Graphical Models based method.

p_thres

default .01; the p value threshold.

COR_weight

default TRUE; whether use correlation coefficient as the weight of edges.

COR_p_adjust

default "fdr"; p.adjust method, see p.adjust.methods.

COR_cut

default .6; correlation coefficient threshold.

COR_low_threshold

default .4; the lowest correlation coefficient threshold, use with COR_optimization = TRUE.

COR_optimization

default FALSE; whether use random matrix theory to optimize the choice of correlation coefficient, see https://doi.org/10.1186/1471-2105-13-113

PGM_meta_data

default FALSE; whether use env data for the optimization, If TRUE, will automatically find the env_data in the object.

PGM_sensitive

default "true"; whether use sensitive type in the PGM model.

PGM_heterogeneous

default "true"; whether use heterogeneous type in the PGM model.

SpiecEasi_method

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

with_module

default TRUE; whether calculate modules.

add_taxa_name

default "Phylum"; add taxonomic rank name to the result.

usename_rawtaxa_when_taxalevel_notOTU

default FALSE; whether replace the name of nodes using the taxonomic information.

...

paremeters pass to spiec.easi in package SpiecEasi.

Returns

res_network in object.

Examples

\donttest{
t1$cal_network(p_thres = 0.01, COR_cut = 0.6)
}

Method save_network()

Save network as gexf style, which can be opened by Gephi <https://gephi.org/>.

Usage

trans_network$save_network(filepath = "network.gexf")

Arguments

filepath

default "network.gexf"; file path.

Returns

None.

Method cal_network_attr()

Calculate network properties.

Usage

trans_network$cal_network_attr()

Returns

res_network_attr in object.

Examples

\donttest{
t1$cal_network_attr()
}

Method cal_node_type()

Calculate node properties.

Usage

trans_network$cal_node_type()

Returns

res_node_type in object.

Examples

\donttest{
t1$cal_node_type()
}

Method cal_eigen()

Calculate eigengenes of modules, i.e. the first principal component based on PCA analysis, and the percentage of variance.

Usage

trans_network$cal_eigen()

Returns

res_eigen and res_eigen_expla in object.

Examples

\donttest{
t1$cal_eigen()
}

Method plot_taxa_roles()

Plot the classification and importance of nodes.

Usage

trans_network$plot_taxa_roles(
  use_type = c(1, 2)[1],
  roles_colors = NULL,
  plot_module = FALSE,
  use_level = "Phylum",
  show_value = c("z", "p"),
  show_number = 1:10,
  plot_color = "Phylum",
  plot_shape = "taxa_roles",
  plot_size = NULL,
  color_values = RColorBrewer::brewer.pal(12, "Paired"),
  shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14)
)

Arguments

use_type

default 1; 1 or 2; 1 represent the traditional taxa roles plot; 2 represent the plot with taxa names as x axis.

roles_colors

default NULL; for use_type 1; colors for each group.

plot_module

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

use_level

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

show_value

default c("z", "p"); for use_type 2; used variable in y axis.

show_number

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

plot_color

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

plot_shape

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

plot_size

default NULL; for use_type 2; used variable for shape.

color_values

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

shape_values

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

Returns

ggplot.

Examples

\donttest{
t1$plot_taxa_roles()
}

Method cal_sum_links()

This function is used to sum the links number from one taxa to another or in the same taxa, for example, at Phylum level. This is very useful to fast see how many nodes are connected between different taxa or within the taxa.

Usage

trans_network$cal_sum_links(taxa_level = "Phylum")

Arguments

taxa_level

default "Phylum"; taxonomic rank.

Returns

res_sum_links_pos and res_sum_links_neg in object.

Examples

\donttest{
t1$cal_sum_links(taxa_level = "Phylum")
}

Method plot_sum_links()

Plot the summed linkages among taxa using chorddiag package <https://github.com/mattflor/chorddiag>.

Usage

trans_network$plot_sum_links(
  plot_pos = TRUE,
  plot_num = NULL,
  color_values = NULL
)

Arguments

plot_pos

default TRUE; plot the summed positive or negative linkages.

plot_num

default NULL; number of taxa presented in the plot.

color_values

default NULL; If not provided, use default.

Returns

chorddiag plot

Method subset_network()

Subset of the network.

Usage

trans_network$subset_network(node = NULL, rm_single = TRUE)

Arguments

node

default NULL; provide the names of the nodes that you want to use in the sub-network.

rm_single

default TRUE; whether remove the nodes without any edge in the sub-network.

Returns

a new network

Examples

\donttest{
t1$subset_network(node = t1$res_node_type %>% .[.$module == "M1", ] %>% 
  rownames, rm_single = TRUE)
# return a sub network that contains all nodes of module M1
}

Method print()

Print the trans_network object.

Usage

trans_network$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

trans_network$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$new`
## ------------------------------------------------

# }
# NOT RUN {
data(dataset)
# correlation network
t1 <- trans_network$new(dataset = dataset, cal_cor = "base", 
  taxa_level = "OTU", filter_thres = 0.001)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$cal_network`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_network(p_thres = 0.01, COR_cut = 0.6)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$cal_network_attr`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_network_attr()
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$cal_node_type`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_node_type()
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$cal_eigen`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_eigen()
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$plot_taxa_roles`
## ------------------------------------------------

# }
# NOT RUN {
t1$plot_taxa_roles()
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$cal_sum_links`
## ------------------------------------------------

# }
# NOT RUN {
t1$cal_sum_links(taxa_level = "Phylum")
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_network$subset_network`
## ------------------------------------------------

# }
# NOT RUN {
t1$subset_network(node = t1$res_node_type %>% .[.$module == "M1", ] %>% 
  rownames, rm_single = TRUE)
# return a sub network that contains all nodes of module M1
# }

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