trans_network {microeco} | R Documentation |
This class is a wrapper for a series of network analysis related methods, including the correlation based <doi:10.1186/1471-2105-13-113> 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.
new()
trans_network$new( dataset = NULL, cor_method = c("pearson", "spearman", "kendall")[1], cal_cor = c("base", "WGCNA", "SparCC", NA)[1], taxa_level = "OTU", filter_thres = 0, nThreads = 1, SparCC_simu_num = 100, env_cols = NULL, add_data = NULL )
dataset
the object of microtable
Class.
cor_method
default "pearson"; "pearson", "spearman" or "kendall"; correlation algorithm.
cal_cor
default "base"; "base", "WGCNA", "SparCC" or NA; correlation method; NA represent do not calculate.
taxa_level
default "OTU"; taxonomic rank.
filter_thres
default 0; the relative abundance threshold.
nThreads
default 1; the thread number used for "WGCNA" and SparCC.
SparCC_simu_num
default 100; SparCC simulation number for bootstrap.
env_cols
default NULL; number or name vector to select the physicochemical data in dataset$sample_table.
add_data
default NULL; provide physicochemical table additionally.
res_cor_p list.
\donttest{ data(dataset) # correlation network t1 <- trans_network$new(dataset = dataset, cal_cor = "base", taxa_level = "OTU", filter_thres = 0.001) t1 <- trans_network$new(dataset = dataset, cal_cor = "SparCC", taxa_level = "OTU", filter_thres = 0.001) t1 <- trans_network$new(dataset = dataset, cal_cor = "WGCNA", taxa_level = "OTU", filter_thres = 0.0001) # PGM network t1 <- trans_network$new(dataset = dataset, cal_cor = NA, taxa_level = "OTU", filter_thres = 0.0001) }
replace_name()
Replace names in res_cor_p of trans_network object.
trans_network$replace_name()
a new res_cor_p in trans_network object.
\donttest{ t1$replace_name() }
cal_network()
Calculate network either based on the correlation method or based on the Probabilistic Graphical Models (PGM) in julia FlashWeave; see Deng et al. (2012) <10.1186/1471-2105-13-113> for correlation based method; see Tackmann et al. (2019) <doi:10.1016/j.cels.2019.08.002> for PGM based method.
trans_network$cal_network( network_method = c("COR", "PGM")[1], p_thres = 0.01, COR_weight = TRUE, COR_p_adjust = "fdr", COR_cut = 0.6, COR_low_threshold = 0.4, COR_optimization = FALSE, PGM_meta_data = FALSE, PGM_sensitive = "true", PGM_heterogeneous = "true", with_module = TRUE, add_taxa_name = "Phylum", usename_rawtaxa_when_taxalevel_notOTU = FALSE )
network_method
default "COR"; "COR" or "PGM"; COR: correlation based method; PGM: Probabilistic Graphical Models based method.
p_thres
default .01; the p value threshold.
COR_weight
default TRUE; whether use correlation coefficient as the weight of edges.
COR_p_adjust
default "fdr"; p.adjust method, see p.adjust.methods.
COR_cut
default .6; correlation coefficient threshold.
COR_low_threshold
default .4; the lowest correlation coefficient threshold, use with COR_optimization = TRUE.
COR_optimization
default FALSE; whether use random matrix theory to optimize the choice of correlation coefficient, see https://doi.org/10.1186/1471-2105-13-113
PGM_meta_data
default FALSE; whether use env data for the optimization, If TRUE, will automatically find the env_data in the object.
PGM_sensitive
default "true"; whether use sensitive type in the PGM model.
PGM_heterogeneous
default "true"; whether use heterogeneous type in the PGM model.
with_module
default TRUE; whether calculate modules.
add_taxa_name
default "Phylum"; add taxonomic rank name to the result.
usename_rawtaxa_when_taxalevel_notOTU
default FALSE; whether replace the name of nodes using the taxonomic information.
res_network in object.
\donttest{ t1$cal_network(p_thres = 0.01, COR_cut = 0.6) }
save_network()
Save network as gexf style, which can be opened by Gephi <https://gephi.org/>.
trans_network$save_network(filepath = "network.gexf")
filepath
default "network.gexf"; file path.
None.
\donttest{ t1$save_network(filepath = "network.gexf") }
cal_network_attr()
Calculate network properties.
trans_network$cal_network_attr()
res_network_attr in object.
\donttest{ t1$cal_network_attr() }
cal_node_type()
Calculate node properties.
trans_network$cal_node_type()
res_node_type in object.
\donttest{ t1$cal_node_type() }
cal_eigen()
Calculate eigengenes of modules, i.e. the first principal component based on PCA analysis, and the percentage of variance.
trans_network$cal_eigen()
res_eigen and res_eigen_expla in object.
\donttest{ t1$cal_eigen() }
plot_taxa_roles()
Plot the classification and importance of nodes.
trans_network$plot_taxa_roles( use_type = c(1, 2)[1], roles_colors = NULL, plot_module = FALSE, use_level = "Phylum", show_value = c("z", "p"), show_number = 1:10, plot_color = "Phylum", plot_shape = "taxa_roles", plot_size = NULL, color_values = RColorBrewer::brewer.pal(12, "Paired"), shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14) )
use_type
default 1; 1 or 2; 1 represent the traditional taxa roles plot; 2 represent the plot with taxa names as x axis.
roles_colors
default NULL; for use_type 1; colors for each group.
plot_module
default FALSE; for use_type 1; whether plot the modules information.
use_level
default "Phylum"; for use_type 2; used taxonomic level in x axis.
show_value
default c("z", "p"); for use_type 2; used variable in y axis.
show_number
default 1:10; for use_type 2; showed number in x axis, sorting according to the nodes number.
plot_color
default "Phylum"; for use_type 2; used variable for color.
plot_shape
default "taxa_roles"; for use_type 2; used variable for shape.
plot_size
default NULL; for use_type 2; used variable for shape.
color_values
default RColorBrewer::brewer.pal(12, "Paired"); for use_type 2; color vector
shape_values
default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); for use_type 2; shape vector, see ggplot2 tutorial for the shape meaning.
ggplot.
\donttest{ t1$plot_taxa_roles() }
cal_sum_links()
Sum the linkages among taxa.
trans_network$cal_sum_links(taxa_level = "Phylum")
taxa_level
default "Phylum"; taxonomic rank.
res_sum_links_pos and res_sum_links_neg in object.
\donttest{ t1$cal_sum_links(taxa_level = "Phylum") }
plot_sum_links()
Plot the summed linkages among taxa using chorddiag package <https://github.com/mattflor/chorddiag>.
trans_network$plot_sum_links( plot_pos = TRUE, plot_num = NULL, color_values = NULL )
plot_pos
default TRUE; plot the summed positive or negative linkages.
plot_num
default NULL; number of taxa presented in the plot.
color_values
default NULL; If not provided, use default.
chorddiag plot
\donttest{ t1$plot_sum_links(plot_pos = TRUE, plot_num = 10) }
subset_network()
Subset of the network.
trans_network$subset_network(node = NULL, rm_single = TRUE)
node
default NULL; provide the names of the nodes that you want to use in the sub-network.
rm_single
default TRUE; whether remove the nodes without any edge in the sub-network.
a new network
\donttest{ t1$subset_network(node = t1$res_node_type %>% .[.$module == "M1", ] %>% rownames, rm_single = TRUE) # return a sub network that contains all nodes of module M1 }
print()
Print the trans_network object.
trans_network$print()
clone()
The objects of this class are cloneable with this method.
trans_network$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------ ## Method `trans_network$new` ## ------------------------------------------------ data(dataset) # correlation network t1 <- trans_network$new(dataset = dataset, cal_cor = "base", taxa_level = "OTU", filter_thres = 0.001) t1 <- trans_network$new(dataset = dataset, cal_cor = "SparCC", taxa_level = "OTU", filter_thres = 0.001) t1 <- trans_network$new(dataset = dataset, cal_cor = "WGCNA", taxa_level = "OTU", filter_thres = 0.0001) # PGM network t1 <- trans_network$new(dataset = dataset, cal_cor = NA, taxa_level = "OTU", filter_thres = 0.0001) ## ------------------------------------------------ ## Method `trans_network$replace_name` ## ------------------------------------------------ t1$replace_name() ## ------------------------------------------------ ## Method `trans_network$cal_network` ## ------------------------------------------------ t1$cal_network(p_thres = 0.01, COR_cut = 0.6) ## ------------------------------------------------ ## Method `trans_network$save_network` ## ------------------------------------------------ t1$save_network(filepath = "network.gexf") ## ------------------------------------------------ ## Method `trans_network$cal_network_attr` ## ------------------------------------------------ t1$cal_network_attr() ## ------------------------------------------------ ## Method `trans_network$cal_node_type` ## ------------------------------------------------ t1$cal_node_type() ## ------------------------------------------------ ## Method `trans_network$cal_eigen` ## ------------------------------------------------ t1$cal_eigen() ## ------------------------------------------------ ## Method `trans_network$plot_taxa_roles` ## ------------------------------------------------ t1$plot_taxa_roles() ## ------------------------------------------------ ## Method `trans_network$cal_sum_links` ## ------------------------------------------------ t1$cal_sum_links(taxa_level = "Phylum") ## ------------------------------------------------ ## Method `trans_network$plot_sum_links` ## ------------------------------------------------ t1$plot_sum_links(plot_pos = TRUE, plot_num = 10) ## ------------------------------------------------ ## Method `trans_network$subset_network` ## ------------------------------------------------ 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