Package 'MetaNet' reference manual

Title:	Network Analysis for Omics Data
Description:	Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.
Authors:	Chen Peng [aut, cre]
Maintainer:	Chen Peng <[email protected]>
License:	GPL-3
Version:	0.2.2
Built:	2025-03-05 13:57:53 UTC
Source:	https://github.com/asa12138/metanet

Use dataframe to annotate edges of an igraph

Description

Use dataframe to annotate edges of an igraph

Usage

anno_edge(go, anno_tab, verbose = TRUE)
anno_edge(go, anno_tab, verbose = TRUE)

Arguments

`go`	metanet an igraph object
`anno_tab`	a dataframe using to annotate (with rowname or a name column)
`verbose`	logical

Value

a annotated igraph object

Examples

data("c_net")
anno <- data.frame("from" = "s__Pelomonas_puraquae", "to" = "s__un_g__Rhizobium", new_atr = "new")
anno_edge(co_net, anno) -> anno_net
data("c_net")
anno <- data.frame("from" = "s__Pelomonas_puraquae", "to" = "s__un_g__Rhizobium", new_atr = "new")
anno_edge(co_net, anno) -> anno_net

Use data.frame to annotate vertexes of metanet

Description

Use data.frame to annotate vertexes of metanet

Usage

anno_vertex(go, anno_tab, verbose = TRUE)
anno_vertex(go, anno_tab, verbose = TRUE)

Arguments

`go`	metanet object
`anno_tab`	a dataframe using to annotate (with rowname or a "name" column)
`verbose`	logical

Value

a annotated metanet object

Examples

data("c_net")
data("otutab", package = "pcutils")
anno_vertex(co_net, taxonomy)
data("c_net")
data("otutab", package = "pcutils")
anno_vertex(co_net, taxonomy)

Edgelist

Description

Edgelist for c_net_from_edgelist()

Edgelist

Description

Edgelist for c_net_from_edgelist()

Layout as a arc

Description

Layout as a arc

Usage

as_arc(angle = 0, arc = pi)
as_arc(angle = 0, arc = pi)

Arguments

`angle`	anticlockwise rotation angle
`arc`	the radian of arc

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Examples

as_arc()(co_net)
c_net_plot(co_net, coors = as_arc(pi / 2), rescale = FALSE)
as_arc()(co_net)
c_net_plot(co_net, coors = as_arc(pi / 2), rescale = FALSE)

Layout as a circle_tree

Description

Layout as a circle_tree

Usage

as_circle_tree()
as_circle_tree()

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Layout as a line

Description

Layout as a line

Usage

as_line(angle = 0)
as_line(angle = 0)

Arguments

angle

anticlockwise rotation angle

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Examples

as_line()(co_net)
c_net_plot(co_net, coors = as_line(pi / 2))
as_line()(co_net)
c_net_plot(co_net, coors = as_line(pi / 2))

Layout as a polyarc

Description

Layout as a polyarc

Usage

as_polyarc(n = 3, space = pi/3)
as_polyarc(n = 3, space = pi/3)

Arguments

`n`	how many arcs of this poly_arc
`space`	the space between each arc, default: pi/3

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Examples

as_polyarc()(co_net)
as_polyarc()(co_net)

Layout as a polycircle

Description

Layout as a polycircle

Usage

as_polycircle(n = 2)
as_polycircle(n = 2)

Arguments

`n`	how many circles of this polycircle

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Examples

as_polycircle()(co_net)
as_polycircle()(co_net)

Layout as a polygon

Description

Layout as a polygon

Usage

as_polygon(n = 3, line_curved = 0.5)
as_polygon(n = 3, line_curved = 0.5)

Arguments

`n`	how many edges of this polygon
`line_curved`	line_curved 0~0.5

Value

A two-column matrix, each row giving the coordinates of a vertex, according to the ids of the vertex ids.

Examples

as_polygon()(co_net)
as_polygon()(co_net)

Transfer an igraph object to a ggig

Description

Transfer an igraph object to a ggig

Usage

as.ggig(go, coors = NULL)
as.ggig(go, coors = NULL)

Arguments

`go`	igraph or meatnet
`coors`	coordinates for nodes,columns: name, X, Y

Value

ggig object

Examples

as.ggig(co_net, coors = c_net_layout(co_net)) -> ggig
plot(ggig)
as.ggig(multi1, coors = c_net_layout(multi1)) -> ggig
plot(ggig, labels_num = 0.3)
as.ggig(co_net, coors = c_net_layout(co_net)) -> ggig
plot(ggig)
as.ggig(multi1, coors = c_net_layout(multi1)) -> ggig
plot(ggig, labels_num = 0.3)

Annotate a metanet

Description

Annotate a metanet

Usage

c_net_annotate(go, anno_tab, mode = "v", verbose = TRUE)
c_net_annotate(go, anno_tab, mode = "v", verbose = TRUE)

Arguments

`go`	metanet object
`anno_tab`	a dataframe using to annotate (mode v, e), or a list (mode n)
`mode`	"v" for vertex, "e" for edge, "n" for network
`verbose`	logical

Value

a annotated metanet object

Examples

data("c_net")
anno <- data.frame("name" = "s__Pelomonas_puraquae", new_atr = "new")
co_net_new <- c_net_annotate(co_net, anno, mode = "v")
get_v(co_net_new, c("name", "new_atr"))

anno <- data.frame("from" = "s__Pelomonas_puraquae", "to" = "s__un_g__Rhizobium", new_atr = "new")
co_net_new <- c_net_annotate(co_net, anno, mode = "e")
get_e(co_net_new, c("from", "to", "new_atr"))

co_net_new <- c_net_annotate(co_net, list(new_atr = "new"), mode = "n")
get_n(co_net_new)
data("c_net")
anno <- data.frame("name" = "s__Pelomonas_puraquae", new_atr = "new")
co_net_new <- c_net_annotate(co_net, anno, mode = "v")
get_v(co_net_new, c("name", "new_atr"))

anno <- data.frame("from" = "s__Pelomonas_puraquae", "to" = "s__un_g__Rhizobium", new_atr = "new")
co_net_new <- c_net_annotate(co_net, anno, mode = "e")
get_e(co_net_new, c("from", "to", "new_atr"))

co_net_new <- c_net_annotate(co_net, list(new_atr = "new"), mode = "n")
get_n(co_net_new)

Construct a metanet from a corr object

Description

Construct a metanet from a corr object

Usage

c_net_build(
  corr,
  r_threshold = 0.6,
  p_threshold = 0.05,
  use_p_adj = TRUE,
  delete_single = TRUE
)
c_net_build(
  corr,
  r_threshold = 0.6,
  p_threshold = 0.05,
  use_p_adj = TRUE,
  delete_single = TRUE
)

Arguments

`corr`	corr object from `c_net_calculate()` or `read_corr()`.
`r_threshold`	r_threshold (default: >0.6).
`p_threshold`	p_threshold (default: <0.05).
`use_p_adj`	use the p.adjust instead of p.value (default: TRUE), if p.adjust not in the corr object, use p.value.
`delete_single`	should delete single vertexes?

Value

an metanet object

Examples

data("otutab", package = "pcutils")
t(otutab) -> totu
metadata[, 3:10] -> env
c_net_calculate(totu) -> corr
c_net_build(corr, r_threshold = 0.65) -> co_net

c_net_calculate(totu, env) -> corr2
c_net_build(corr2) -> co_net2
data("otutab", package = "pcutils")
t(otutab) -> totu
metadata[, 3:10] -> env
c_net_calculate(totu) -> corr
c_net_build(corr, r_threshold = 0.65) -> co_net

c_net_calculate(totu, env) -> corr2
c_net_build(corr2) -> co_net2

Calculate correlation for one or two t(otutab), or distance for one t(otutab).

Description

Calculate correlation for one or two t(otutab), or distance for one t(otutab).

Usage

c_net_calculate(
  totu,
  totu2 = NULL,
  method = "spearman",
  filename = FALSE,
  p.adjust.method = NULL,
  p.adjust.mode = "all",
  threads = 1,
  verbose = TRUE
)
c_net_calculate(
  totu,
  totu2 = NULL,
  method = "spearman",
  filename = FALSE,
  p.adjust.method = NULL,
  p.adjust.mode = "all",
  threads = 1,
  verbose = TRUE
)

Arguments

`totu`	t(otutab), row are samples, column are features.
`totu2`	t(otutab2) or NULL, row are samples, column are features.
`method`	"spearman" (default), "pearson", "sparcc", or distance index from `vegdist`.
`filename`	the prefix of saved .corr file or FALSE.
`p.adjust.method`	see `p.adjust`
`p.adjust.mode`	see `p.adjust.table`
`threads`	threads, default: 1.
`verbose`	verbose, default: TRUE.

Value

a corr object with 3 elements:

`r`	default: spearman correlation
`p.value`	default: p-value of spearman correlation
`p.adjust`	default p.adjust.method = NULL

Examples

data("otutab", package = "pcutils")
t(otutab) -> totu
c_net_calculate(totu) -> corr
metadata[, 3:10] -> env
c_net_calculate(totu, env) -> corr2
data("otutab", package = "pcutils")
t(otutab) -> totu
c_net_calculate(totu) -> corr
metadata[, 3:10] -> env
c_net_calculate(totu, env) -> corr2

Filter a network according to some attributes

Description

Filter a network according to some attributes

Usage

c_net_filter(go, ..., mode = "v")
c_net_filter(go, ..., mode = "v")

Arguments

`go`	metanet object
`...`	some attributes of vertex and edge
`mode`	"v" or "e"

Value

metanet

Examples

data("multi_net")
c_net_filter(multi1, v_group %in% c("omic1", "omic2"))
data("multi_net")
c_net_filter(multi1, v_group %in% c("omic1", "omic2"))

Construct a network from edge_list dataframe

Description

Construct a network from edge_list dataframe

Usage

c_net_from_edgelist(
  edgelist,
  vertex_df = NULL,
  direct = FALSE,
  e_type = NULL,
  e_class = NULL
)
c_net_from_edgelist(
  edgelist,
  vertex_df = NULL,
  direct = FALSE,
  e_type = NULL,
  e_class = NULL
)

Arguments

`edgelist`	first is source, second is target, others are annotation
`vertex_df`	vertex metadata data.frame
`direct`	logical
`e_type`	set e_type
`e_class`	set e_class

Value

metanet

Examples

data(edgelist)
edge_net <- c_net_from_edgelist(arc_count, vertex_df = arc_taxonomy)
edge_net <- c_net_set(edge_net, vertex_class = "Phylum", edge_width = "n")
c_net_plot(edge_net)
data(edgelist)
edge_net <- c_net_from_edgelist(arc_count, vertex_df = arc_taxonomy)
edge_net <- c_net_set(edge_net, vertex_class = "Phylum", edge_width = "n")
c_net_plot(edge_net)

Layout coordinates

Description

Layout coordinates

Usage

c_net_layout(
  go,
  method = igraph::nicely(),
  order_by = NULL,
  order_ls = NULL,
  seed = 1234,
  line_curved = 0.5,
  ...
)
c_net_layout(
  go,
  method = igraph::nicely(),
  order_by = NULL,
  order_ls = NULL,
  seed = 1234,
  line_curved = 0.5,
  ...
)

Arguments

`go`	igraph or metanet
`method`	(1) as_line(), as_arc(), as_polygon(), as_polyarc(), as_polycircle(), as_circle_tree(); (2) as_star(), as_tree(), in_circle(), nicely(), on_grid(), on_sphere(),randomly(), with_dh(), with_fr(), with_gem(), with_graphopt(), with_kk(),with_lgl(), with_mds(),. see `layout_`; (3) a character, "auto","backbone","centrality","circlepack","dendrogram", "eigen","focus","hive","igraph","linear","manual","matrix", "partition","pmds","stress","treemap","unrooted". see `create_layout`
`order_by`	order nodes according to a node attribute
`order_ls`	manual the discrete variable with a vector, or continuous variable with "desc" to decreasing
`seed`	random seed
`line_curved`	consider line curved, only for some layout methods like as_line(), as_polygon().default:0
`...`	add

Value

coors object: coordinates for nodes, columns: name, X, Y; curved for edges, columns: from, to, curved;

Examples

library(igraph)
c_net_layout(co_net) -> coors
c_net_plot(co_net, coors)
c_net_plot(co_net, c_net_layout(co_net, in_circle()), vertex.size = 2)
c_net_plot(co_net, c_net_layout(co_net, in_circle(), order_by = "v_class"), vertex.size = 2)
c_net_plot(co_net, c_net_layout(co_net, in_circle(), order_by = "size", order_ls = "desc"))
c_net_plot(co_net, c_net_layout(co_net, as_polygon(3)))
library(igraph)
c_net_layout(co_net) -> coors
c_net_plot(co_net, coors)
c_net_plot(co_net, c_net_layout(co_net, in_circle()), vertex.size = 2)
c_net_plot(co_net, c_net_layout(co_net, in_circle(), order_by = "v_class"), vertex.size = 2)
c_net_plot(co_net, c_net_layout(co_net, in_circle(), order_by = "size", order_ls = "desc"))
c_net_plot(co_net, c_net_layout(co_net, as_polygon(3)))

Load network file

Description

Load network file

Usage

c_net_load(filename, format = "data.frame")
c_net_load(filename, format = "data.frame")

Arguments

`filename`	filename
`format`	"data.frame","graphml"

Value

metanet

Plot a metanet

Description

Plot a metanet

Usage

c_net_plot(
  go,
  coors = NULL,
  ...,
  labels_num = NULL,
  vertex_size_range = NULL,
  edge_width_range = NULL,
  plot_module = FALSE,
  mark_module = FALSE,
  mark_color = NULL,
  mark_alpha = 0.3,
  module_label = FALSE,
  module_label_cex = 2,
  module_label_color = "black",
  module_label_just = c(0.5, 0.5),
  pie_value = NULL,
  pie_color = NULL,
  legend = TRUE,
  legend_number = FALSE,
  legend_cex = 1,
  legend_position = c(left_leg_x = -2, left_leg_y = 1, right_leg_x = 1.2, right_leg_y =
    1),
  group_legend_title = NULL,
  group_legend_order = NULL,
  color_legend = TRUE,
  color_legend_order = NULL,
  size_legend = FALSE,
  size_legend_title = "Node Size",
  edge_legend = TRUE,
  edge_legend_title = "Edge type",
  edge_legend_order = NULL,
  width_legend = FALSE,
  width_legend_title = "Edge width",
  lty_legend = FALSE,
  lty_legend_title = "Edge class",
  lty_legend_order = NULL,
  module_legend = FALSE,
  module_legend_title = "Module",
  module_legend_order = NULL,
  pie_legend = FALSE,
  pie_legend_title = "Pie part",
  pie_legend_order = NULL,
  params_list = NULL,
  seed = 1234
)
c_net_plot(
  go,
  coors = NULL,
  ...,
  labels_num = NULL,
  vertex_size_range = NULL,
  edge_width_range = NULL,
  plot_module = FALSE,
  mark_module = FALSE,
  mark_color = NULL,
  mark_alpha = 0.3,
  module_label = FALSE,
  module_label_cex = 2,
  module_label_color = "black",
  module_label_just = c(0.5, 0.5),
  pie_value = NULL,
  pie_color = NULL,
  legend = TRUE,
  legend_number = FALSE,
  legend_cex = 1,
  legend_position = c(left_leg_x = -2, left_leg_y = 1, right_leg_x = 1.2, right_leg_y =
    1),
  group_legend_title = NULL,
  group_legend_order = NULL,
  color_legend = TRUE,
  color_legend_order = NULL,
  size_legend = FALSE,
  size_legend_title = "Node Size",
  edge_legend = TRUE,
  edge_legend_title = "Edge type",
  edge_legend_order = NULL,
  width_legend = FALSE,
  width_legend_title = "Edge width",
  lty_legend = FALSE,
  lty_legend_title = "Edge class",
  lty_legend_order = NULL,
  module_legend = FALSE,
  module_legend_title = "Module",
  module_legend_order = NULL,
  pie_legend = FALSE,
  pie_legend_title = "Pie part",
  pie_legend_order = NULL,
  params_list = NULL,
  seed = 1234
)

Arguments

`go`	an igraph or metanet object
`coors`	the coordinates you saved
`...`	additional parameters for `igraph.plotting`
`labels_num`	show how many labels, >1 indicates number, <1 indicates fraction, "all" indicates all.
`vertex_size_range`	the vertex size range, e.g. c(1,10)
`edge_width_range`	the edge width range, e.g. c(1,10)
`plot_module`	logical, plot module?
`mark_module`	logical, mark the modules?
`mark_color`	mark color
`mark_alpha`	mark fill alpha, default 0.3
`module_label`	show module label?
`module_label_cex`	module label cex
`module_label_color`	module label color
`module_label_just`	module label just, default c(0.5,0.5)
`pie_value`	a dataframe using to plot pie (with rowname or a "name" column)
`pie_color`	color vector
`legend`	all legends
`legend_number`	legend with numbers
`legend_cex`	character expansion factor relative to current par("cex"), default: 1
`legend_position`	legend_position, default: c(left_leg_x=-1.9,left_leg_y=1,right_leg_x=1.2,right_leg_y=1)
`group_legend_title`	group_legend_title, length must same to the numbers of v_group
`group_legend_order`	group_legend_order vector
`color_legend`	logical
`color_legend_order`	color_legend_order vector
`size_legend`	logical
`size_legend_title`	size_legend_title
`edge_legend`	logical
`edge_legend_title`	edge_legend_title
`edge_legend_order`	edge_legend_order vector, e.g. c("positive","negative")
`width_legend`	logical
`width_legend_title`	width_legend_title
`lty_legend`	logical
`lty_legend_title`	lty_legend_title
`lty_legend_order`	lty_legend_order
`module_legend`	logical
`module_legend_title`	module_legend_title
`module_legend_order`	module_legend_order
`pie_legend`	logical
`pie_legend_title`	pie_legend_title
`pie_legend_order`	pie_legend_order
`params_list`	a list of parameters, e.g. list(edge_legend = TRUE, lty_legend = FALSE), when the parameter is duplicated, the format argument will be used rather than the argument in params_list.
`seed`	random seed, default:1234, make sure each plot is the same.

Value

a network plot

Examples

data("c_net")
c_net_plot(co_net)
c_net_plot(co_net2)
c_net_plot(multi1)
data("c_net")
c_net_plot(co_net)
c_net_plot(co_net2)
c_net_plot(multi1)

Save network file

Description

Save network file

Usage

c_net_save(go, filename = "net", format = "data.frame")
c_net_save(go, filename = "net", format = "data.frame")

Arguments

`go`	metanet network
`filename`	filename
`format`	"data.frame","graphml"

Value

No value

Set basic attributes from totu table

Description

Set basic attributes from totu table

Usage

c_net_set(
  go,
  ...,
  vertex_group = "v_group",
  vertex_class = "v_class",
  vertex_size = "size",
  edge_type = "e_type",
  edge_class = "e_class",
  edge_width = "width",
  node_break = 5,
  edge_break = 5,
  initialize = TRUE
)
c_net_set(
  go,
  ...,
  vertex_group = "v_group",
  vertex_class = "v_class",
  vertex_size = "size",
  edge_type = "e_type",
  edge_class = "e_class",
  edge_width = "width",
  node_break = 5,
  edge_break = 5,
  initialize = TRUE
)

Arguments

`go`	metanet an igraph object
`...`	some data.frames to annotate go
`vertex_group`	choose which column to be vertex_group (map to vertex_shape)
`vertex_class`	choose which column to be vertex_class (map to vertex_color)
`vertex_size`	choose which column to be vertex_size (map to vertex_size)
`edge_type`	choose which column to be edge_type (map to edge_color)
`edge_class`	choose which column to be edge_class (map to edge_linetype)
`edge_width`	choose which column to be edge_width (map to edge_width)
`node_break`	node_break if v_class is numeric, default: 5
`edge_break`	edge_break if e_type is numeric, default: 5
`initialize`	initialize, default: TRUE

Value

a metanet object

Examples

data("otutab", package = "pcutils")
t(otutab) -> totu
metadata[, 3:10] -> env

data("c_net")
co_net <- c_net_set(co_net, taxonomy, data.frame("Abundance" = colSums(totu)),
  vertex_class = "Phylum", vertex_size = "Abundance"
)
co_net2 <- c_net_set(co_net2, taxonomy, data.frame(name = colnames(env), env = colnames(env)),
  vertex_class = c("Phylum", "env")
)
co_net2 <- c_net_set(co_net2, data.frame("Abundance" = colSums(totu)), vertex_size = "Abundance")
data("otutab", package = "pcutils")
t(otutab) -> totu
metadata[, 3:10] -> env

data("c_net")
co_net <- c_net_set(co_net, taxonomy, data.frame("Abundance" = colSums(totu)),
  vertex_class = "Phylum", vertex_size = "Abundance"
)
co_net2 <- c_net_set(co_net2, taxonomy, data.frame(name = colnames(env), env = colnames(env)),
  vertex_class = c("Phylum", "env")
)
co_net2 <- c_net_set(co_net2, data.frame("Abundance" = colSums(totu)), vertex_size = "Abundance")

Evaluate the stability of a network

Description

$Vi=\frac{E-Ei}{E}$

E is the global efficiency and Ei is the global efficiency after the removal of the node i and its entire links.

Usage

c_net_stability(
  go_ls,
  mode = "robust_test",
  partial = 0.5,
  step = 10,
  reps = 9,
  threads = 1,
  verbose = TRUE,
  keystone = FALSE
)

robust_test(
  go_ls,
  partial = 0.5,
  step = 10,
  reps = 9,
  threads = 1,
  verbose = TRUE
)

vulnerability(go_ls, threads = 1, verbose = TRUE)

robustness(go_ls, keystone = FALSE, reps = 9, threads = 1, verbose = TRUE)
c_net_stability(
  go_ls,
  mode = "robust_test",
  partial = 0.5,
  step = 10,
  reps = 9,
  threads = 1,
  verbose = TRUE,
  keystone = FALSE
)

robust_test(
  go_ls,
  partial = 0.5,
  step = 10,
  reps = 9,
  threads = 1,
  verbose = TRUE
)

vulnerability(go_ls, threads = 1, verbose = TRUE)

robustness(go_ls, keystone = FALSE, reps = 9, threads = 1, verbose = TRUE)

Arguments

`go_ls`	an igraph object or igraph list.
`mode`	"robust_test", "vulnerability", "robustness"
`partial`	how much percent vertexes be removed in total (default: 0.5, only for robust_test)
`step`	how many nodes be removed each time? (default: 10, only for robust_test)
`reps`	simulation number (default: 9)
`threads`	threads
`verbose`	verbose
`keystone`	remove 70%% keystones instead of remove 50%% nodes (default: False, only for robustness)

Value

a data.frame

data.frame (robustness class)

a vector

Examples


data("c_net")
if (requireNamespace("ggpmisc")) {
  c_net_stability(co_net, mode = "robust_test", step = 20, reps = 9) -> robust_res
  plot(robust_res, index = "Average_degree", mode = 2)
}

c_net_stability(co_net, mode = "vulnerability") -> vulnerability_res
plot(vulnerability_res)

robustness(co_net) -> robustness_res
plot(robustness_res)

module_detect(co_net) -> co_net_modu
zp_analyse(co_net_modu, mode = 2) -> co_net_modu

c_net_stability(co_net_modu, mode = "robustness", keystone = TRUE) -> robustness_res
plot(robustness_res)

data("c_net")
if (requireNamespace("ggpmisc")) {
  c_net_stability(co_net, mode = "robust_test", step = 20, reps = 9) -> robust_res
  plot(robust_res, index = "Average_degree", mode = 2)
}

c_net_stability(co_net, mode = "vulnerability") -> vulnerability_res
plot(vulnerability_res)

robustness(co_net) -> robustness_res
plot(robustness_res)

module_detect(co_net) -> co_net_modu
zp_analyse(co_net_modu, mode = 2) -> co_net_modu

c_net_stability(co_net_modu, mode = "robustness", keystone = TRUE) -> robustness_res
plot(robustness_res)

Union two networks

Description

Union two networks

Usage

c_net_union(go1, go2)
c_net_union(go1, go2)

Arguments

`go1`	metanet object
`go2`	metanet object

Value

metanet

Examples

data("c_net")
co_net_union <- c_net_union(co_net, co_net2)
c_net_plot(co_net_union)
data("c_net")
co_net_union <- c_net_union(co_net, co_net2)
c_net_plot(co_net_union)

Update a metanet object or transform igraph object to metanet object

Description

Update a metanet object or transform igraph object to metanet object

Usage

c_net_update(
  go,
  node_break = 5,
  edge_break = 5,
  initialize = FALSE,
  verbose = TRUE,
  uniq_v_class = FALSE
)
c_net_update(
  go,
  node_break = 5,
  edge_break = 5,
  initialize = FALSE,
  verbose = TRUE,
  uniq_v_class = FALSE
)

Arguments

`go`	a metanet object or igraph object
`node_break`	node_break if v_class is numeric, default: 5
`edge_break`	edge_break if e_type is numeric, default: 5
`initialize`	initialize?
`verbose`	verbose?
`uniq_v_class`	if TRUE, add prefix to v_class if multiple v_class belong to same v_group.

Value

metanet

Calculate similarity for one t(otutab)

Description

Calculate similarity for one t(otutab)

Usage

cal_sim(totu, totu2 = NULL, method = "bray")
cal_sim(totu, totu2 = NULL, method = "bray")

Arguments

`totu`	t(otutab), row are samples, column are features.
`totu2`	t(otutab) or NULL, row are samples, column are features.
`method`	Dissimilarity index, see `vegdist`.

Value

similarity = 1-distance

Examples

if (requireNamespace("vegan")) {
  data("otutab", package = "pcutils")
  t(otutab) -> totu
  cal_sim(totu) -> sim_corr
}
if (requireNamespace("vegan")) {
  data("otutab", package = "pcutils")
  t(otutab) -> totu
  cal_sim(totu) -> sim_corr
}

Check tables and extract common samples

Description

Check tables and extract common samples

Usage

check_tabs(...)
check_tabs(...)

Arguments

...

tables

Value

formatted tables

Examples

data("otutab", package = "pcutils")
check_tabs(otutab)
data("otutab", package = "pcutils")
check_tabs(otutab)

Clean a igraph object

Description

Clean a igraph object

Usage

clean_igraph(go, direct = TRUE)
clean_igraph(go, direct = TRUE)

Arguments

`go`	igraph, metanet objects
`direct`	direct?

Value

a igraph object

Clean multi edge metanet to plot

Description

Clean multi edge metanet to plot

Usage

clean_multi_edge_metanet(go)
clean_multi_edge_metanet(go)

Arguments

`go`	metanet object

Value

metanet object

Examples

g <- igraph::make_ring(2)
g <- igraph::add.edges(g, c(1, 1, 1, 1, 2, 1))
plot(g)
plot(clean_multi_edge_metanet(g))
g <- igraph::make_ring(2)
g <- igraph::add.edges(g, c(1, 1, 1, 1, 2, 1))
plot(g)
plot(clean_multi_edge_metanet(g))

MetaNet networks

Description

MetaNet co_nets

MetaNet networks

Description

MetaNet co_nets

MetaNet networks

Description

MetaNet co_nets

Cohesion calculation

Description

Cohesion calculation

Plot cohesion

Usage

Cohesion(otutab, reps = 200, threads = 1, mycor = NULL, verbose = TRUE)

## S3 method for class 'cohesion'
plot(x, group, metadata, mode = 1, ...)
Cohesion(otutab, reps = 200, threads = 1, mycor = NULL, verbose = TRUE)

## S3 method for class 'cohesion'
plot(x, group, metadata, mode = 1, ...)

Arguments

`otutab`	otutab
`reps`	iteration time
`threads`	threads
`mycor`	a correlation matrix you want to use, skip the null model build when mycor is not NULL, default: NULL
`verbose`	verbose
`x`	`Cohesion()` result (cohesion object)
`group`	group name in colnames(metadata)
`metadata`	metadata
`mode`	plot mode, 1~2
`...`	additional arguments for `group_box` (mode=1) or `group_box` (mode=2)

Value

Cohesion object: a list with two dataframe

a ggplot

References

Herren, C. M. & McMahon, K. (2017) Cohesion: a method for quantifying the connectivity of microbial communities. doi:10.1038/ismej.2017.91.

Examples


data("otutab", package = "pcutils")
# set reps at least 99 when you run.
Cohesion(otutab[1:50, ], reps = 19) -> cohesion_res
if (requireNamespace("ggpubr")) {
  plot(cohesion_res, group = "Group", metadata = metadata, mode = 1)
  plot(cohesion_res, group = "Group", metadata = metadata, mode = 2)
}

data("otutab", package = "pcutils")
# set reps at least 99 when you run.
Cohesion(otutab[1:50, ], reps = 19) -> cohesion_res
if (requireNamespace("ggpubr")) {
  plot(cohesion_res, group = "Group", metadata = metadata, mode = 1)
  plot(cohesion_res, group = "Group", metadata = metadata, mode = 2)
}

Transform a dataframe to a network edgelist.

Description

Transform a dataframe to a network edgelist.

Usage

df2net_tree(test, fun = sum)
df2net_tree(test, fun = sum)

Arguments

`test`	df
`fun`	default: sum

Value

metanet

Examples

data("otutab", package = "pcutils")
cbind(taxonomy, num = rowSums(otutab))[1:20, ] -> test
df2net_tree(test) -> ttt
plot(ttt)
if (requireNamespace("ggraph")) plot(ttt, coors = as_circle_tree())
data("otutab", package = "pcutils")
cbind(taxonomy, num = rowSums(otutab))[1:20, ] -> test
df2net_tree(test) -> ttt
plot(ttt)
if (requireNamespace("ggraph")) plot(ttt, coors = as_circle_tree())

Extract each sample network from the whole network

Description

Extract each sample network from the whole network

Usage

extract_sample_net(
  whole_net,
  otutab,
  threads = 1,
  save_net = FALSE,
  fast = TRUE,
  remove_negative = FALSE,
  verbose = TRUE
)
extract_sample_net(
  whole_net,
  otutab,
  threads = 1,
  save_net = FALSE,
  fast = TRUE,
  remove_negative = FALSE,
  verbose = TRUE
)

Arguments

`whole_net`	the whole network
`otutab`	otutab, columns are samples, these columns will be extract
`threads`	threads, default: 1
`save_net`	should save these sub_nets? FALSE or a filename
`fast`	less indexes for faster calculate ?
`remove_negative`	remove negative edge or not? default: FALSE
`verbose`	verbose

Value

a dataframe contains all sub_net parameters

Examples

data(otutab, package = "pcutils")
extract_sample_net(co_net, otutab) -> sub_net_pars
data(otutab, package = "pcutils")
extract_sample_net(co_net, otutab) -> sub_net_pars

Fast correlation calculation

Description

Fast correlation calculation

Usage

fast_cor(totu, totu2 = NULL, method = c("pearson", "spearman"))
fast_cor(totu, totu2 = NULL, method = c("pearson", "spearman"))

Arguments

`totu`	t(otutab), row are samples, column are features.
`totu2`	t(otutab) or NULL, row are samples, column are features.
`method`	"spearman" or "pearson"

Value

a list with 2 elements:

`r`	default: spearman correlation
`p.value`	default: p-value of spearman correlation

Examples

data("otutab", package = "pcutils")
t(otutab[1:100, ]) -> totu
fast_cor(totu, method = "spearman") -> corr
data("otutab", package = "pcutils")
t(otutab[1:100, ]) -> totu
fast_cor(totu, method = "spearman") -> corr

Filter some modules as others

Description

Filter some modules as others

Combine or cut modules to module_number

Plot module tree

Usage

filter_n_module(go_m, n_node_in_module = 0, keep_id = NULL, delete = FALSE)

combine_n_module(go_m, module_number = 5)

plot_module_tree(go_m, module = "module", community = NULL, label.size = 2)
filter_n_module(go_m, n_node_in_module = 0, keep_id = NULL, delete = FALSE)

combine_n_module(go_m, module_number = 5)

plot_module_tree(go_m, module = "module", community = NULL, label.size = 2)

Arguments

`go_m`	module metanet
`n_node_in_module`	transfer the modules less than n_node_in_module to "others"
`keep_id`	keep modules ids, will not be "others"
`delete`	logical, delete others modules? default:FALSE, the others module will be "others".
`module_number`	number of modules
`module`	which column name is module. default: "module"
`community`	community object, default: NULL, use the community of go_m
`label.size`	label.size

Value

metanet with modules

ggplot

Examples

data("c_net")
module_detect(co_net) -> co_net_modu
filter_n_module(co_net_modu, n_node_in_module = 30) -> co_net_modu
if (requireNamespace("ggtree") && requireNamespace("treeio")) plot_module_tree(co_net_modu)
combine_n_module(co_net_modu, 20) -> co_net_modu1
if (requireNamespace("ggtree") && requireNamespace("treeio")) plot_module_tree(co_net_modu1)
data("c_net")
module_detect(co_net) -> co_net_modu
filter_n_module(co_net_modu, n_node_in_module = 30) -> co_net_modu
if (requireNamespace("ggtree") && requireNamespace("treeio")) plot_module_tree(co_net_modu)
combine_n_module(co_net_modu, 20) -> co_net_modu1
if (requireNamespace("ggtree") && requireNamespace("treeio")) plot_module_tree(co_net_modu1)

Fit power-law distribution for an igraph

Description

Fit power-law distribution for an igraph

Usage

fit_power(go, p.value = FALSE)
fit_power(go, p.value = FALSE)

Arguments

`go`	igraph
`p.value`	calculate p.value

Value

ggplot

Examples

fit_power(co_net)
fit_power(co_net)

Layout with group

Description

Layout with group

Usage

g_layout(
  go,
  group = "module",
  group_order = NULL,
  layout1 = in_circle(),
  zoom1 = 20,
  layout2 = in_circle(),
  zoom2 = 3,
  show_big_layout = FALSE,
  ...
)
g_layout(
  go,
  group = "module",
  group_order = NULL,
  layout1 = in_circle(),
  zoom1 = 20,
  layout2 = in_circle(),
  zoom2 = 3,
  show_big_layout = FALSE,
  ...
)

Arguments

`go`	igraph or metanet object
`group`	group name (default: module)
`group_order`	group_order
`layout1`	layout1 method, one of (1) a dataframe or matrix: rowname is group, two columns are X and Y (2) function: layout method for `c_net_layout` default: in_circle()
`zoom1`	big network layout size
`layout2`	one of functions: layout method for `c_net_layout`, or a list of functions.
`zoom2`	average sub_network layout size, or numeric vector, or "auto"
`show_big_layout`	show the big layout to help you adjust.
`...`	add

Value

coors

Examples


data("c_net")
module_detect(co_net, method = "cluster_fast_greedy") -> co_net_modu
g_layout(co_net_modu, group = "module", zoom1 = 30, zoom2 = "auto", layout2 = as_line()) -> oridata
plot(co_net_modu, coors = oridata)

data("c_net")
module_detect(co_net, method = "cluster_fast_greedy") -> co_net_modu
g_layout(co_net_modu, group = "module", zoom1 = 30, zoom2 = "auto", layout2 = as_line()) -> oridata
plot(co_net_modu, coors = oridata)

Layout with group nicely

Description

Layout with group nicely

Usage

g_layout_nice(go, group = "module", mode = "circlepack", ...)

g_layout_circlepack(go, group = "module", ...)

g_layout_treemap(go, group = "module", ...)

g_layout_backbone(go, group = "module", ...)

g_layout_stress(go, group = "module", ...)
g_layout_nice(go, group = "module", mode = "circlepack", ...)

g_layout_circlepack(go, group = "module", ...)

g_layout_treemap(go, group = "module", ...)

g_layout_backbone(go, group = "module", ...)

g_layout_stress(go, group = "module", ...)

Arguments

`go`	igraph or metanet
`group`	group name (default: module)
`mode`	circlepack, treemap, backbone, stress
`...`	add

Value

coors

Examples


data("c_net")
module_detect(co_net, method = "cluster_fast_greedy") -> co_net_modu
if (requireNamespace("ggraph")) {
  plot(co_net_modu, coors = g_layout_nice(co_net_modu, group = "module"))
  plot(co_net_modu, coors = g_layout_nice(co_net_modu, group = "module", mode = "treemap"))
}

data("c_net")
module_detect(co_net, method = "cluster_fast_greedy") -> co_net_modu
if (requireNamespace("ggraph")) {
  plot(co_net_modu, coors = g_layout_nice(co_net_modu, group = "module"))
  plot(co_net_modu, coors = g_layout_nice(co_net_modu, group = "module", mode = "treemap"))
}

Layout with group as a polygon

Description

Layout with group as a polygon

Layout with group as a polyarc

Usage

g_layout_polygon(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  line_curved = 0.5
)

g_layout_polyarc(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  space = pi/4,
  scale_node_num = TRUE
)

g_layout_polycircle(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL
)
g_layout_polygon(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  line_curved = 0.5
)

g_layout_polyarc(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  space = pi/4,
  scale_node_num = TRUE
)

g_layout_polycircle(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL
)

Arguments

`go`	igraph
`group`	group name (default:v_group)
`group_order`	group_order
`group2`	group2 name, will order nodes in each group according to group2_order
`group2_order`	group2_order
`line_curved`	line_curved 0~1
`space`	the space between each arc, default: pi/4
`scale_node_num`	scale with the node number in each group

Value

coors

Examples

g_layout_polygon(multi1) -> oridata
c_net_plot(multi1, oridata)
g_layout_polyarc(multi1, group2 = "v_class", group2_order = c(LETTERS[4:1])) -> oridata
c_net_plot(multi1, oridata)
g_layout_polycircle(co_net2, group2 = "v_class") -> oridata
c_net_plot(co_net2, oridata)
g_layout_polygon(multi1) -> oridata
c_net_plot(multi1, oridata)
g_layout_polyarc(multi1, group2 = "v_class", group2_order = c(LETTERS[4:1])) -> oridata
c_net_plot(multi1, oridata)
g_layout_polycircle(co_net2, group2 = "v_class") -> oridata
c_net_plot(co_net2, oridata)

Get community

Description

Get community

Usage

get_community(go_m)
get_community(go_m)

Arguments

go_m

module metanet

Value

community

Get edge information

Description

Get edge information

Usage

get_e(go, index = NULL)
get_e(go, index = NULL)

Arguments

`go`	metanet object
`index`	attribute name, default: NULL

Value

data.frame

Get skeleton network according to a group

Description

Get skeleton network according to a group

Skeleton plot

Usage

get_group_skeleton(go, Group = "v_class", count = NULL, top_N = 8)

skeleton_plot(ske_net, split_e_type = TRUE, ...)
get_group_skeleton(go, Group = "v_class", count = NULL, top_N = 8)

skeleton_plot(ske_net, split_e_type = TRUE, ...)

Arguments

`go`	network
`Group`	vertex column name
`count`	take which column count, default: NULL
`top_N`	top_N
`ske_net`	skeleton
`split_e_type`	split by e_type? default: TRUE
`...`	additional parameters for `igraph.plotting`

Value

skeleton network

Examples

get_group_skeleton(co_net) -> ske_net
skeleton_plot(ske_net)
get_group_skeleton(co_net) -> ske_net
skeleton_plot(ske_net)

Get module

Description

Get module

Usage

get_module(go_m)
get_module(go_m)

Arguments

go_m

module metanet

Value

module

Get module_eigen

Description

Get module_eigen

Usage

get_module_eigen(go_m)
get_module_eigen(go_m)

Arguments

go_m

module metanet

Value

module_eigen

Get network information

Description

Get network information

Usage

get_n(go, index = NULL, simple = FALSE)
get_n(go, index = NULL, simple = FALSE)

Arguments

`go`	metanet object
`index`	attribute name, default: NULL
`simple`	logical, get simple index

Value

data.frame

Get vertex information

Description

Get vertex information

Usage

get_v(go, index = NULL)
get_v(go, index = NULL)

Arguments

`go`	metanet object
`index`	attribute name, default: NULL

Value

data.frame

Input a graphml file exported by Gephi

Description

Input a graphml file exported by Gephi

Usage

input_gephi(file)
input_gephi(file)

Arguments

file

graphml file exported by Gephi

Value

list contains the igraph object and coordinates

Is this object a metanet object?

Description

Is this object a metanet object?

Usage

is_metanet(go)
is_metanet(go)

Arguments

`go`	a test object

Value

logical

Examples

data(c_net)
is_metanet(co_net)
data(c_net)
is_metanet(co_net)

Link summary of the network

Description

Link summary of the network

Usage

links_stat(
  go,
  group = "v_class",
  e_type = "all",
  topN = 10,
  colors = NULL,
  mode = 1,
  plot_param = list()
)
links_stat(
  go,
  group = "v_class",
  e_type = "all",
  topN = 10,
  colors = NULL,
  mode = 1,
  plot_param = list()
)

Arguments

`go`	igraph or metanet
`group`	summary which group of vertex attribution in names(vertex_attr(go))
`e_type`	"positive", "negative", "all"
`topN`	topN of group, default: 10
`colors`	colors
`mode`	1~2
`plot_param`	plot parameters

Value

plot

Examples

if (requireNamespace("circlize")) {
  links_stat(co_net, topN = 10)
  module_detect(co_net) -> co_net_modu
  links_stat(co_net_modu, group = "module")
}
if (requireNamespace("corrplot")) {
  links_stat(co_net, topN = 10, mode = 2)
}
if (requireNamespace("circlize")) {
  links_stat(co_net, topN = 10)
  module_detect(co_net) -> co_net_modu
  links_stat(co_net_modu, group = "module")
}
if (requireNamespace("corrplot")) {
  links_stat(co_net, topN = 10, mode = 2)
}

MetaNet networks abundance

Description

MetaNet co_nets

MetaNet networks metadata

Description

MetaNet co_nets

MetaNet networks abundance

Description

MetaNet co_nets

MetaNet networks metadata

Description

MetaNet co_nets

Detect the modules

Description

Detect the modules

Usage

module_detect(
  go,
  method = "cluster_fast_greedy",
  n_node_in_module = 0,
  delete = FALSE
)
module_detect(
  go,
  method = "cluster_fast_greedy",
  n_node_in_module = 0,
  delete = FALSE
)

Arguments

`go`	an igraph object
`method`	cluster_method: "cluster_walktrap", "cluster_edge_betweenness", "cluster_fast_greedy", "cluster_spinglass"
`n_node_in_module`	transfer the modules less than n_node_in_module to "others"
`delete`	logical, delete others modules? default:FALSE, the others module will be "others".

Value

an igraph object

Examples

data("c_net")
module_detect(co_net) -> co_net_modu
data("c_net")
module_detect(co_net) -> co_net_modu

Calculate the eigenvalue of each module and correlation of nodes and eigenvalue (node_eigen_cor).

Description

Calculate the eigenvalue of each module and correlation of nodes and eigenvalue (node_eigen_cor).

Plot the expression of each modules

Usage

module_eigen(go_m, totu, cor_method = "spearman")

module_expression(
  go_m,
  totu,
  group = NULL,
  r_threshold = 0.6,
  x_order = NULL,
  facet_param = NULL,
  plot_eigen = FALSE
)
module_eigen(go_m, totu, cor_method = "spearman")

module_expression(
  go_m,
  totu,
  group = NULL,
  r_threshold = 0.6,
  x_order = NULL,
  facet_param = NULL,
  plot_eigen = FALSE
)

Arguments

`go_m`	module metanet
`totu`	original abundance table used for module_eigen().
`cor_method`	"pearson", "kendall", "spearman"
`group`	group variable for totu
`r_threshold`	the threshold for node_eigen_cor, default: 0.6.
`x_order`	order the x axis.
`facet_param`	parameters parse to `facet_wrap`, e.g. nrow=2.
`plot_eigen`	plot the eigen value line.

Value

module metanet with module_eigen

Examples

data("otutab", package = "pcutils")
t(otutab) -> totu
data("c_net")
module_detect(co_net, n_node_in_module = 30) -> co_net_modu
module_eigen(co_net_modu, totu) -> co_net_modu
module_expression(co_net_modu, totu)
data("otutab", package = "pcutils")
t(otutab) -> totu
data("c_net")
module_detect(co_net, n_node_in_module = 30) -> co_net_modu
module_eigen(co_net_modu, totu) -> co_net_modu
module_expression(co_net_modu, totu)

Generate a n-modules network

Description

this is just a random generation method, the module number of result is not exactly the module_number, you can change the inter_module_density and intra_module_density to get the proper result.

Usage

module_net(
  module_number = 3,
  n_node_in_module = 30,
  intra_module_density = 0.3,
  inter_module_density = 0.01
)
module_net(
  module_number = 3,
  n_node_in_module = 30,
  intra_module_density = 0.3,
  inter_module_density = 0.01
)

Arguments

`module_number`	number of modules
`n_node_in_module`	number of nodes in each modules
`intra_module_density`	intra_module_density, recommend bigger than 20*inter_module_density, default:0.3
`inter_module_density`	inter_module_density, default:0.01

Value

n-modules metanet

Examples

g1 <- module_net()
get_n(g1)
plot(g1, mark_module = TRUE)
plot(g1, coors = g_layout(g1, zoom2 = 20))
plot(g1, coors = g_layout_polyarc(g1, group = "module"))
plot(g1, coors = g_layout_polygon(g1, group = "module"))
g1 <- module_net()
get_n(g1)
plot(g1, mark_module = TRUE)
plot(g1, coors = g_layout(g1, zoom2 = 20))
plot(g1, coors = g_layout_polyarc(g1, group = "module"))
plot(g1, coors = g_layout_polygon(g1, group = "module"))

Multi-omics network build

Description

Multi-omics network build

Usage

multi_net_build(
  ...,
  mode = "full",
  method = "spearman",
  filename = FALSE,
  p.adjust.method = NULL,
  r_threshold = 0.6,
  p_threshold = 0.05,
  use_p_adj = TRUE,
  delete_single = TRUE
)
multi_net_build(
  ...,
  mode = "full",
  method = "spearman",
  filename = FALSE,
  p.adjust.method = NULL,
  r_threshold = 0.6,
  p_threshold = 0.05,
  use_p_adj = TRUE,
  delete_single = TRUE
)

Arguments

`...`	some omics abundance tables
`mode`	"full"
`method`	"spearman" or "pearson"
`filename`	the prefix of saved .corr file or FALSE
`p.adjust.method`	see `p.adjust`
`r_threshold`	r_threshold (default: >0.6)
`p_threshold`	p_threshold (default: <0.05)
`use_p_adj`	use the p.adjust instead of p-value (default: TRUE)
`delete_single`	should delete single vertexes?

Value

metanet

Examples

data("multi_test")
multi1 <- multi_net_build(list(Microbiome = micro, Metabolome = metab, Transcriptome = transc))
multi1 <- c_net_set(multi1, micro_g, metab_g, transc_g,
  vertex_class = c("Phylum", "kingdom", "type")
)
multi1 <- c_net_set(multi1, data.frame("Abundance1" = colSums(micro)),
  data.frame("Abundance2" = colSums(metab)), data.frame("Abundance3" = colSums(transc)),
  vertex_size = paste0("Abundance", 1:3)
)
c_net_plot(multi1)
data("multi_test")
multi1 <- multi_net_build(list(Microbiome = micro, Metabolome = metab, Transcriptome = transc))
multi1 <- c_net_set(multi1, micro_g, metab_g, transc_g,
  vertex_class = c("Phylum", "kingdom", "type")
)
multi1 <- c_net_set(multi1, data.frame("Abundance1" = colSums(micro)),
  data.frame("Abundance2" = colSums(metab)), data.frame("Abundance3" = colSums(transc)),
  vertex_size = paste0("Abundance", 1:3)
)
c_net_plot(multi1)

MetaNet networks

Description

MetaNet co_nets

Calculate natural_connectivity

Description

Calculate natural_connectivity

Usage

nc(p)
nc(p)

Arguments

`p`	an igraph or metanet object

Value

natural_connectivity (numeric)

References

`nc` in `ggClusterNet`

Examples

igraph::make_ring(10) %>% nc()
igraph::make_ring(10) %>% nc()

Calculate all topological indexes of a network

Description

Calculate all topological indexes of a network

Add topological indexes for a network

Usage

net_par(
  go,
  mode = c("v", "e", "n", "all"),
  fast = TRUE,
  remove_negative = FALSE
)

c_net_index(go, force = FALSE)
net_par(
  go,
  mode = c("v", "e", "n", "all"),
  fast = TRUE,
  remove_negative = FALSE
)

c_net_index(go, force = FALSE)

Arguments

`go`	igraph or metanet
`mode`	calculate what? c("v", "e", "n", "all")
`fast`	less indexes for faster calculate ?
`remove_negative`	remove negative edge or not? default: FALSE
`force`	replace existed net_par

Value

a 3-elements list

`n_index`	indexs of the whole network
`v_index`	indexs of each vertex
`e_index`	indexs of each edge

Examples

igraph::make_graph("Walther") %>% net_par()
c_net_index(co_net) -> co_net_with_par
igraph::make_graph("Walther") %>% net_par()
c_net_index(co_net) -> co_net_with_par

plot use networkD3

Description

plot use networkD3

Usage

netD3plot(go, v_class = "v_class", ...)
netD3plot(go, v_class = "v_class", ...)

Arguments

`go`	metanet
`v_class`	which attributes use to be v_class
`...`	see `forceNetwork`

Value

D3 plot

Examples

data("c_net")
plot(co_net2)
if (requireNamespace("networkD3")) {
  netD3plot(co_net2)
}
data("c_net")
plot(co_net2)
if (requireNamespace("networkD3")) {
  netD3plot(co_net2)
}

Plot olympic rings using network

Description

Plot olympic rings using network

Usage

olympic_rings_net()
olympic_rings_net()

Value

network plot

Examples

olympic_rings_net()
olympic_rings_net()

p.adjust apply on a correlation table (matrix or data.frame)

Description

p.adjust apply on a correlation table (matrix or data.frame)

Usage

p.adjust.table(pp, method = "BH", mode = "all")
p.adjust.table(pp, method = "BH", mode = "all")

Arguments

`pp`	table of p-values
`method`	see `p.adjust`, default: "BH".
`mode`	"all" for all values; "rows" adjust each row one by one; "columns" adjust each column one by one. Default: "all".

Value

a table of adjusted p-values

Examples

matrix(abs(rnorm(100, 0.01, 0.1)), 10, 10) -> pp
p.adjust.table(pp, method = "BH", mode = "all") -> pp_adj
matrix(abs(rnorm(100, 0.01, 0.1)), 10, 10) -> pp
p.adjust.table(pp, method = "BH", mode = "all") -> pp_adj

Plot a ggig

Description

Plot a ggig

Usage

## S3 method for class 'ggig'
plot(
  x,
  coors = NULL,
  ...,
  labels_num = NULL,
  vertex_size_range = NULL,
  edge_width_range = NULL,
  plot_module = FALSE,
  mark_module = FALSE,
  mark_color = NULL,
  mark_alpha = 0.3,
  module_label = FALSE,
  module_label_cex = 2,
  module_label_color = "black",
  module_label_just = c(0.5, 0.5),
  legend_number = FALSE,
  legend = TRUE,
  legend_cex = 1,
  legend_position = c(left_leg_x = -2, left_leg_y = 1, right_leg_x = 1.2, right_leg_y =
    1),
  group_legend_title = NULL,
  group_legend_order = NULL,
  color_legend = TRUE,
  color_legend_order = NULL,
  size_legend = FALSE,
  size_legend_title = "Node Size",
  edge_legend = TRUE,
  edge_legend_title = "Edge type",
  edge_legend_order = NULL,
  width_legend = FALSE,
  width_legend_title = "Edge width",
  lty_legend = FALSE,
  lty_legend_title = "Edge class",
  lty_legend_order = NULL,
  params_list = NULL,
  seed = 1234
)
## S3 method for class 'ggig'
plot(
  x,
  coors = NULL,
  ...,
  labels_num = NULL,
  vertex_size_range = NULL,
  edge_width_range = NULL,
  plot_module = FALSE,
  mark_module = FALSE,
  mark_color = NULL,
  mark_alpha = 0.3,
  module_label = FALSE,
  module_label_cex = 2,
  module_label_color = "black",
  module_label_just = c(0.5, 0.5),
  legend_number = FALSE,
  legend = TRUE,
  legend_cex = 1,
  legend_position = c(left_leg_x = -2, left_leg_y = 1, right_leg_x = 1.2, right_leg_y =
    1),
  group_legend_title = NULL,
  group_legend_order = NULL,
  color_legend = TRUE,
  color_legend_order = NULL,
  size_legend = FALSE,
  size_legend_title = "Node Size",
  edge_legend = TRUE,
  edge_legend_title = "Edge type",
  edge_legend_order = NULL,
  width_legend = FALSE,
  width_legend_title = "Edge width",
  lty_legend = FALSE,
  lty_legend_title = "Edge class",
  lty_legend_order = NULL,
  params_list = NULL,
  seed = 1234
)

Arguments

`x`	ggig object
`coors`	the coordinates you saved
`...`	additional parameters for `igraph.plotting`
`labels_num`	show how many labels, >1 indicates number, <1 indicates fraction, "all" indicates all.
`vertex_size_range`	the vertex size range, e.g. c(1,10)
`edge_width_range`	the edge width range, e.g. c(1,10)
`plot_module`	logical, plot module?
`mark_module`	logical, mark the modules?
`mark_color`	mark color
`mark_alpha`	mark fill alpha, default 0.3
`module_label`	show module label?
`module_label_cex`	module label cex
`module_label_color`	module label color
`module_label_just`	module label just, default c(0.5,0.5)
`legend_number`	legend with numbers
`legend`	all legends
`legend_cex`	character expansion factor relative to current par("cex"), default: 1
`legend_position`	legend_position, default: c(left_leg_x=-1.9,left_leg_y=1,right_leg_x=1.2,right_leg_y=1)
`group_legend_title`	group_legend_title, length must same to the numbers of v_group
`group_legend_order`	group_legend_order vector
`color_legend`	logical
`color_legend_order`	color_legend_order vector
`size_legend`	logical
`size_legend_title`	size_legend_title
`edge_legend`	logical
`edge_legend_title`	edge_legend_title
`edge_legend_order`	edge_legend_order vector, e.g. c("positive","negative")
`width_legend`	logical
`width_legend_title`	width_legend_title
`lty_legend`	logical
`lty_legend_title`	lty_legend_title
`lty_legend_order`	lty_legend_order
`params_list`	a list of parameters, e.g. list(edge_legend = TRUE, lty_legend = FALSE), when the parameter is duplicated, the format argument will be used rather than the argument in params_list.
`seed`	random seed, default:1234, make sure each plot is the same.

Value

ggplot

Plot a metanet

Description

Plot a metanet

Usage

## S3 method for class 'metanet'
plot(x, ...)
## S3 method for class 'metanet'
plot(x, ...)

Arguments

`x`	metanet object
`...`	add

Value

plot

Plot a rmt_res

Description

Plot a rmt_res

Usage

## S3 method for class 'rmt_res'
plot(x, ...)
## S3 method for class 'rmt_res'
plot(x, ...)

Arguments

`x`	rmt_res
`...`	Additional arguments

Value

ggplot

Plot robust

Description

Plot robust

Usage

## S3 method for class 'robust'
plot(
  x,
  indexes = c("Natural_connectivity", "Average_degree"),
  use_ratio = FALSE,
  mode = 1,
  ...
)
## S3 method for class 'robust'
plot(
  x,
  indexes = c("Natural_connectivity", "Average_degree"),
  use_ratio = FALSE,
  mode = 1,
  ...
)

Arguments

`x`	`robust_test()` result (robust object)
`indexes`	indexes selected to show
`use_ratio`	use the delete nodes ratio rather than nodes number
`mode`	plot mode, 1~3
`...`	additional arguments for `group_box`

Value

a ggplot

Plot robustness

Description

Plot robustness

Usage

## S3 method for class 'robustness'
plot(x, indexes = "Node_number", ...)
## S3 method for class 'robustness'
plot(x, indexes = "Node_number", ...)

Arguments

`x`	`robustness()` result (robustness object)
`indexes`	indexes selected to show
`...`	additional arguments for `group_box`

Value

a ggplot

Plot vulnerability

Description

Plot vulnerability

Usage

## S3 method for class 'vulnerability'
plot(x, ...)
## S3 method for class 'vulnerability'
plot(x, ...)

Arguments

`x`	`vulnerability()` result (vulnerability object)
`...`	add

Value

a ggplot

Print method for 'cohesion' objects

Description

Print method for 'cohesion' objects

Usage

## S3 method for class 'cohesion'
print(x, ...)
## S3 method for class 'cohesion'
print(x, ...)

Arguments

`x`	'cohesion' object
`...`	Additional arguments

Value

No value

Print method for 'coors' objects

Description

Print method for 'coors' objects

Usage

## S3 method for class 'coors'
print(x, ...)
## S3 method for class 'coors'
print(x, ...)

Arguments

`x`	'coors' object
`...`	additional arguments

Value

No value

Print method for 'corr' objects

Description

Print method for 'corr' objects

Usage

## S3 method for class 'corr'
print(x, ...)
## S3 method for class 'corr'
print(x, ...)

Arguments

`x`	'corr' object
`...`	additional arguments

Value

No value

Print method for 'ggig' objects

Description

Print method for 'ggig' objects

Usage

## S3 method for class 'ggig'
print(x, ...)
## S3 method for class 'ggig'
print(x, ...)

Arguments

`x`	'ggig' object
`...`	Additional arguments

Value

No value

Print method for 'metanet' objects

Description

Print method for 'metanet' objects

Usage

## S3 method for class 'metanet'
print(x, ...)
## S3 method for class 'metanet'
print(x, ...)

Arguments

`x`	'metanet' object
`...`	Additional arguments

Value

No value

Print method for 'robust' objects

Description

Print method for 'robust' objects

Usage

## S3 method for class 'robust'
print(x, ...)
## S3 method for class 'robust'
print(x, ...)

Arguments

`x`	'robust' object
`...`	Additional arguments

Value

No value

Print method for 'robustness' objects

Description

Print method for 'robustness' objects

Usage

## S3 method for class 'robustness'
print(x, ...)
## S3 method for class 'robustness'
print(x, ...)

Arguments

`x`	'robustness' object
`...`	Additional arguments

Value

No value

Print method for 'vulnerability' objects

Description

Print method for 'vulnerability' objects

Usage

## S3 method for class 'vulnerability'
print(x, ...)
## S3 method for class 'vulnerability'
print(x, ...)

Arguments

`x`	'vulnerability' object
`...`	Additional arguments

Value

No value

Degree distribution comparison with random network

Description

Degree distribution comparison with random network

Usage

rand_net(go = go, plot = TRUE)
rand_net(go = go, plot = TRUE)

Arguments

`go`	igraph object
`plot`	plot or not

Value

ggplot

Examples

rand_net(co_net)
rand_net(co_net)

Net_pars of many random network

Description

Net_pars of many random network

Compare some indexes between your net with random networks

Usage

rand_net_par(go, reps = 99, threads = 1, verbose = TRUE)

compare_rand(
  pars,
  randp,
  index = c("Average_path_length", "Clustering_coefficient")
)
rand_net_par(go, reps = 99, threads = 1, verbose = TRUE)

compare_rand(
  pars,
  randp,
  index = c("Average_path_length", "Clustering_coefficient")
)

Arguments

`go`	igraph
`reps`	simulation time
`threads`	threads
`verbose`	verbose
`pars`	your net pars resulted by net_pars()
`randp`	random networks pars resulted by rand_net_par()
`index`	compared indexes: "Average_path_length","Clustering_coefficient" or else

Value

ggplot

Examples

data("c_net")
rand_net_par(co_net_rmt, reps = 30) -> randp
net_par(co_net_rmt, fast = FALSE) -> pars
compare_rand(pars, randp)
data("c_net")
rand_net_par(co_net_rmt, reps = 30) -> randp
net_par(co_net_rmt, fast = FALSE) -> pars
compare_rand(pars, randp)

Read a corr object

Description

Read a corr object

Usage

read_corr(filename)
read_corr(filename)

Arguments

filename

filename of .corr

Value

a corr object

Get RMT threshold for a correlation matrix

Description

Get RMT threshold for a correlation matrix

Get RMT threshold for a correlation matrix roughly

Usage

RMT_threshold(
  occor.r,
  out_dir,
  min_threshold = 0.5,
  max_threshold = 0.8,
  step = 0.02,
  gif = FALSE,
  verbose = FALSE
)

rmt(occor.r, min_threshold = 0.5, max_threshold = 0.85, step = 0.01)
RMT_threshold(
  occor.r,
  out_dir,
  min_threshold = 0.5,
  max_threshold = 0.8,
  step = 0.02,
  gif = FALSE,
  verbose = FALSE
)

rmt(occor.r, min_threshold = 0.5, max_threshold = 0.85, step = 0.01)

Arguments

`occor.r`	a corr object or a correlation matrix
`out_dir`	output dir
`min_threshold`	min_threshold
`max_threshold`	max_threshold
`step`	step
`gif`	render a .gif file?
`verbose`	verbose

Value

a r-threshold

recommend threshold

References

J. Zhou, Y. Deng, FALSE. Luo, Z. He, Q. Tu, X. Zhi, (2010) Functional Molecular Ecological Networks, doi:10.1128/mBio.00169-10. https://matstat.org/content_en/RMT/RMThreshold_Intro.pdf

Examples


data(otutab, package = "pcutils")
t(otutab) -> totu
c_net_calculate(totu) -> corr
rmt(corr)
# recommend: 0.69
c_net_build(corr, r_threshold = 0.69) -> co_net_rmt

data(otutab, package = "pcutils")
t(otutab) -> totu
c_net_calculate(totu) -> corr
rmt(corr)
# recommend: 0.69
c_net_build(corr, r_threshold = 0.69) -> co_net_rmt

Save a corr object

Description

Save a corr object

Usage

save_corr(corr, filename = "corr")
save_corr(corr, filename = "corr")

Arguments

`corr`	a corr object
`filename`	filename without extension, default: "corr"

Value

a .corr file

Show MetaNet logo

Description

Show MetaNet logo

Usage

show_MetaNet_logo()
show_MetaNet_logo()

Value

picture

Calculate small-world coefficient

Description

Calculate small-world coefficient

Usage

smallworldness(go, reps = 99, threads = 1, verbose = TRUE)
smallworldness(go, reps = 99, threads = 1, verbose = TRUE)

Arguments

`go`	igraph or metanet
`reps`	simulation time
`threads`	threads
`verbose`	verbose

Value

number

Examples


# set reps at least 99 when you run.
smallworldness(co_net, reps = 9)

# set reps at least 99 when you run.
smallworldness(co_net, reps = 9)

Summaries two columns information

Description

Summaries two columns information

Usage

summ_2col(df, from = 1, to = 2, count = 3, direct = FALSE)
summ_2col(df, from = 1, to = 2, count = 3, direct = FALSE)

Arguments

`df`	data.frame
`from`	first column name or index
`to`	second column name or index
`count`	(optional) weight column, if no, each equal to 1
`direct`	consider direct? default: FALSE

Value

data.frame

Examples

test <- data.frame(
  a = sample(letters[1:4], 10, replace = TRUE),
  b = sample(letters[1:4], 10, replace = TRUE)
)
summ_2col(test, direct = TRUE)
summ_2col(test, direct = FALSE)
if (requireNamespace("circlize")) {
  summ_2col(test, direct = TRUE) %>% pcutils::my_circo()
}
test <- data.frame(
  a = sample(letters[1:4], 10, replace = TRUE),
  b = sample(letters[1:4], 10, replace = TRUE)
)
summ_2col(test, direct = TRUE)
summ_2col(test, direct = FALSE)
if (requireNamespace("circlize")) {
  summ_2col(test, direct = TRUE) %>% pcutils::my_circo()
}

Summary module index

Description

Summary module index

Usage

summary_module(go_m, var = "v_class", module = "module", ...)
summary_module(go_m, var = "v_class", module = "module", ...)

Arguments

`go_m`	module metanet
`var`	variable name
`module`	which column name is module. default: "module"
`...`	add

Value

ggplot

Examples

data("c_net")
module_detect(co_net, n_node_in_module = 30) -> co_net_modu
summary_module(co_net_modu, var = "v_class", module = "module")
summary_module(co_net_modu, var = "Abundance", module = "module")
data("c_net")
module_detect(co_net, n_node_in_module = 30) -> co_net_modu
summary_module(co_net_modu, var = "v_class", module = "module")
summary_module(co_net_modu, var = "Abundance", module = "module")

Summary method for 'corr' objects

Description

Summary method for 'corr' objects

Usage

## S3 method for class 'corr'
summary(object, ...)
## S3 method for class 'corr'
summary(object, ...)

Arguments

`object`	'corr' object
`...`	Additional arguments

Value

No value

Transformation a network to a module network

Description

Transformation a network to a module network

Usage

to_module_net(go, edge_type = c("module", "module_from", "module_to")[1])
to_module_net(go, edge_type = c("module", "module_from", "module_to")[1])

Arguments

`go`	metanet
`edge_type`	"module", "module_from", "module_to"

Value

metanet with modules

MetaNet networks abundance

Description

MetaNet co_nets

MetaNet networks metadata

Description

MetaNet co_nets

Quick build a metanet from two columns table

Description

Quick build a metanet from two columns table

Usage

twocol_edgelist(edgelist)
twocol_edgelist(edgelist)

Arguments

edgelist

two columns table (no elements exist in two columns at same time)

Value

metanet

Examples

twocol <- data.frame(
  "col1" = sample(letters, 30, replace = TRUE),
  "col2" = sample(c("A", "B"), 30, replace = TRUE)
)
twocol_net <- twocol_edgelist(twocol)
plot(twocol_net)
c_net_plot(twocol_net, g_layout_polygon(twocol_net))
twocol <- data.frame(
  "col1" = sample(letters, 30, replace = TRUE),
  "col2" = sample(c("A", "B"), 30, replace = TRUE)
)
twocol_net <- twocol_edgelist(twocol)
plot(twocol_net)
c_net_plot(twocol_net, g_layout_polygon(twocol_net))

Venn network

Description

Venn network

Usage

venn_net(tab)
venn_net(tab)

Arguments

tab

data.frame (row is elements, column is group), or a list (names is group, value is elements)

Value

plot

Examples

data(otutab, package = "pcutils")
tab <- otutab[400:485, 1:3]
venn_net(tab) -> v_net
plot(v_net)
data(otutab, package = "pcutils")
tab <- otutab[400:485, 1:3]
venn_net(tab) -> v_net
plot(v_net)

Zi-Pi calculate

Description

Zi-Pi calculate

Zi-Pi plot of vertexes

Usage

zp_analyse(go_m, mode = 2, use_origin = TRUE)

zp_plot(go, label = TRUE, mode = 1)
zp_analyse(go_m, mode = 2, use_origin = TRUE)

zp_plot(go, label = TRUE, mode = 1)

Arguments

`go_m`	igraph object after `module_detect()`
`mode`	plot style, 1~3
`use_origin`	use original_module, default:TRUE, if FALSE, use module
`go`	igraph object after zp_analyse()
`label`	show label or not

Value

igraph

a ggplot object

References

Guimerà, R. & Amaral, L. Functional cartography of complex metabolic networks. (2005) doi:10.1038/nature03288.

Examples

data("c_net")
module_detect(co_net) -> co_net_modu
zp_analyse(co_net_modu) -> co_net_modu
zp_plot(co_net_modu)
zp_plot(co_net_modu, mode = 3)

data("c_net")
module_detect(co_net) -> co_net_modu
zp_analyse(co_net_modu) -> co_net_modu
zp_plot(co_net_modu)
zp_plot(co_net_modu, mode = 3)

Package 'MetaNet'

Help Index

Use dataframe to annotate edges of an igraph

Description

Usage

Arguments

Value

See Also

Examples

Use data.frame to annotate vertexes of metanet

Description

Usage

Arguments

Value

See Also

Examples

Edgelist

Description

Edgelist

Description

Layout as a arc

Description

Usage

Arguments

Value

See Also

Examples

Layout as a circle_tree

Description

Usage

Value

See Also

Layout as a line

Description

Usage

Arguments

Value

See Also

Examples

Layout as a polyarc

Description

Usage

Arguments

Value

See Also

Examples

Layout as a polycircle

Description

Usage

Arguments

Value

See Also

Examples

Layout as a polygon

Description

Usage

Arguments

Value

See Also

Examples

Transfer an igraph object to a ggig

Description

Usage

Arguments

Value

See Also

Examples

Annotate a metanet

Description

Usage

Arguments

Value

See Also

Examples

Construct a metanet from a corr object

Description

Usage

Arguments

Value

See Also