Type: Package
Title: Network Analysis for Omics Data
Version: 0.2.7
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.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 4.1.0), igraph (≥ 1.3.5)
LazyData: true
Imports: graphics, dplyr, ggplot2 (≥ 3.2.0), ggnewscale, ggrepel, grDevices, magrittr, reshape2, stats, tibble, utils, pcutils (≥ 0.2.7), rlang
Suggests: pheatmap, vegan, stringr, foreach, doSNOW, snow, knitr, rmarkdown, prettydoc, Hmisc, gifski, ggraph, networkD3, ggpmisc, ggtree, treeio, circlize, jsonify, ggpubr, corrplot, philentropy, spatstat.random, spatstat.geom, sf
VignetteBuilder: knitr
BugReports: https://github.com/Asa12138/MetaNet/issues
URL: https://github.com/Asa12138/MetaNet
ByteCompile: true
biocViews: DataImport, Network analysis, Omics, Software, Visualization
NeedsCompilation: no
Packaged: 2025-06-26 08:47:51 UTC; asa
Author: Chen Peng ORCID iD [aut, cre]
Maintainer: Chen Peng <pengchen2001@zju.edu.cn>
Repository: CRAN
Date/Publication: 2025-06-26 09:50:02 UTC

MetaNet: Network Analysis for Omics Data

Description

logo

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.

Author(s)

Maintainer: Chen Peng pengchen2001@zju.edu.cn (ORCID)

See Also

Useful links:

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Assignment pipe

Description

See magrittr::%<>% for details.

Usage

lhs %<>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

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, ...)

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)
}

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)

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

Calculate Similarity Between Two Graphs via Adjacency Matrices

Description

Computes the similarity between two igraph objects using their adjacency matrices. Supports Frobenius norm-based similarity and cosine similarity.

Usage

adjacency_similarity(g1, g2, method = "frobenius")

Arguments

g1

An igraph object representing the first graph.

g2

An igraph object representing the second graph.

method

A character string specifying the similarity method: "frobenius" (default) or "cosine".

Value

A numeric value between 0 (no similarity) and 1 (identical graphs).

Examples

library(igraph)
g1 <- graph_from_edgelist(matrix(c(1, 2, 2, 3), ncol = 2, byrow = TRUE), directed = FALSE)
g2 <- graph_from_edgelist(matrix(c(1, 2, 2, 4), ncol = 2, byrow = TRUE), directed = FALSE)
adjacency_similarity(g1, g2, method = "frobenius") # Output: 0.5
adjacency_similarity(g1, g2, method = "cosine") # Output: 0.5

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)

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

See Also

Other manipulate: anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), get_v(), is_metanet()

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

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)

Arguments

go

metanet object

anno_tab

a dataframe using to annotate (with rowname or a "name" column)

verbose

logical

Value

a annotated metanet object

See Also

Other manipulate: anno_edge(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), get_v(), is_metanet()

Examples

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()

Transfer an igraph object to a ggig

Description

Transfer an igraph object to a ggig

Usage

as.ggig(go, coors = NULL)

Arguments

go

igraph or meatnet

coors

coordinates for nodes,columns: name, X, Y

Value

ggig object

See Also

Other plot: c_net_plot(), input_cytoscape(), input_gephi(), netD3plot(), olympic_rings_net(), plot.ggig(), twocol_edgelist(), venn_net()

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)

Layout as a arc

Description

Layout as a arc

Usage

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.

See Also

Other layout: as_circle_tree(), as_line(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polycircle(), as_polygon(), c_net_layout()

Examples

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

Layout as a circle_tree

Description

Layout as a circle_tree

Usage

as_circle_tree()

Value

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

See Also

Other layout: as_arc(), as_line(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polycircle(), as_polygon(), c_net_layout()

Transfer to a coors object

Description

Transfer to a coors object

Usage

as_coors(coors, curved = NULL)

Arguments

coors

data.frame

curved

line curved

Value

coors object

Layout as a line

Description

Layout as a line

Usage

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.

See Also

Other layout: as_arc(), as_circle_tree(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polycircle(), as_polygon(), c_net_layout()

Examples

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

Layout as a multi_layer

Description

Layout as a multi_layer

Usage

as_multi_layer(n = 3, layout = on_grid())

Arguments

n

how many arcs of this multi_layer

layout

see method in c_net_layout

Value

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

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_poly_sector(), as_polyarc(), as_polycircle(), as_polygon(), c_net_layout()

Examples

as_multi_layer()(co_net)

Layout as a multi_layer

Description

Layout as a multi_layer

Usage

as_poly_sector(n = 3)

Arguments

n

how many arcs of this multi_layer

Value

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

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_multi_layer(), as_polyarc(), as_polycircle(), as_polygon(), c_net_layout()

Layout as a polyarc

Description

Layout as a polyarc

Usage

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.

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_multi_layer(), as_poly_sector(), as_polycircle(), as_polygon(), c_net_layout()

Examples

as_polyarc()(co_net)

Layout as a polycircle

Description

Layout as a polycircle

Usage

as_polycircle(n = 5)

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.

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polygon(), c_net_layout()

Examples

as_polycircle()(co_net)

Layout as a polygon

Description

Layout as a polygon

Usage

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.

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polycircle(), c_net_layout()

Examples

as_polygon()(co_net)

Annotate a metanet

Description

Annotate a metanet

Usage

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

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), get_v(), is_metanet()

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)

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
)

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

See Also

Other build: c_net_from_edgelist(), c_net_set(), c_net_update(), multi_net_build()

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

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
)

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

See Also

Other calculate: cal_sim(), fast_cor(), p.adjust.table(), read_corr()

Examples

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

Compare Two Networks

Description

Compare Two Networks

Usage

c_net_compare(g1, g2)

Arguments

g1

network1

g2

network2

Value

A list containing the following elements:

Examples

data("c_net")
set.seed(12)
co_net_p1 <- c_net_filter(co_net, name %in% sample(V(co_net)$name, 300))
co_net_p2 <- c_net_filter(co_net, name %in% sample(V(co_net)$name, 300))
c_net_compare(co_net_p1, co_net_p2) -> c_net_comp
plot(c_net_comp)

Difference two networks

Description

Difference two networks

Usage

c_net_difference(go1, go2, ...)

Arguments

go1

metanet object

go2

metanet object

...

add

Value

metanet

Extract ego-centric subnetwork with preserved class attributes

Description

Wrapper around igraph::make_ego_graph() that ensures output retains "metanet" and "igraph" class structure. Supports single or multiple center nodes.

Usage

c_net_ego(graph, nodes, order = 1, mode = "all")

Arguments

graph

An igraph object with potential "metanet" class

nodes

Center node(s) for subnetwork extraction (vertex IDs or names)

order

Integer specifying the order of neighbors to include

mode

Character scalar, either "in", "out" or "all" for directed networks

Value

metanet

Examples

library(igraph)
c_net_plot(co_net)
c_net_plot(c_net_ego(co_net, "s__Kribbella_catacumbae"))
nodes <- c("s__Kribbella_catacumbae", "s__Verrucosispora_andamanensis")
c_net_plot(c_net_ego(co_net, nodes))

Filter a network according to some attributes

Description

Filter a network according to some attributes

Usage

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

Arguments

go

metanet object

...

some attributes of vertex and edge

mode

"v" or "e"

Value

metanet

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), get_v(), is_metanet()

Examples

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
)

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

See Also

Other build: c_net_build(), c_net_set(), c_net_update(), multi_net_build()

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)

Highlight specific nodes in a network

Description

Adds highlight markers to specified nodes and grays out non-highlighted nodes. Preserves all existing vertex/edge attributes and class structure.

Usage

c_net_highlight(graph, nodes = NULL, edges = NULL, gray_color = "gray80")

Arguments

graph

An igraph/metatnet object

nodes

Vector of node names to highlight

edges

a data.frame of edges to highlight, colnames must be "from" and "to"

gray_color

Color for non-highlighted nodes (default: "gray80")

Value

metanet

Examples

par(mfrow = c(1, 3))
nodes <- c("s__Kribbella_catacumbae", "s__Verrucosispora_andamanensis")
nodes <- V(c_net_ego(co_net, nodes))$name
g_hl <- c_net_highlight(co_net, nodes = nodes)
plot(g_hl) # Highlighted nodes keep colors, others turn gray
get_e(co_net) %>% head(20) -> hl_edges
g_hl2 <- c_net_highlight(co_net, edges = hl_edges[, 2:3])
c_net_plot(g_hl2)
g_hl3 <- c_net_highlight(co_net, nodes = nodes, edges = hl_edges[, 2:3])
c_net_plot(g_hl3)

Intersect two networks

Description

Intersect two networks

Usage

c_net_intersect(go1, go2, ...)

Arguments

go1

metanet object

go2

metanet object

...

add

Value

metanet

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,
  rescale = TRUE,
  ...
)

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

rescale

logical, scale the X, Y to (-1,1)

...

add

Value

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

See Also

Other layout: as_arc(), as_circle_tree(), as_line(), as_multi_layer(), as_poly_sector(), as_polyarc(), as_polycircle(), as_polygon()

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)))

Load network file

Description

Load network file

Usage

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

Arguments

filename

filename

format

"data.frame","graphml"

Value

metanet

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_save(), c_net_union(), get_e(), get_n(), get_v(), is_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,
  label_cex = 1,
  arrow_size_cex = 1,
  arrow_width_cex = 1,
  params_list = NULL,
  rescale = FALSE,
  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

label_cex

label cex, default 1, relative to the vertex size

arrow_size_cex

arrow size cex, default 1, relative to the vertex size

arrow_width_cex

arrow width cex, default 1, relative to the vertex size

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.

rescale

Logical constant, whether to rescale the coordinates to the (-1,1).

seed

random seed, default:1234, make sure each plot is the same.

Value

a network plot

See Also

Other plot: as.ggig(), input_cytoscape(), input_gephi(), netD3plot(), olympic_rings_net(), plot.ggig(), twocol_edgelist(), venn_net()

Examples

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")

Arguments

go

metanet network

filename

filename

format

"data.frame","graphml"

Value

No value

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_union(), get_e(), get_n(), get_v(), is_metanet()

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
)

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

See Also

Other build: c_net_build(), c_net_from_edgelist(), c_net_update(), multi_net_build()

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")

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)

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)

Union two networks

Description

Union two networks

Usage

c_net_union(go1, go2, ...)

Arguments

go1

metanet object

go2

metanet object

...

add

Value

metanet

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), get_e(), get_n(), get_v(), is_metanet()

Examples

g1 <- make_graph(edges = c("1", 2, 2, 3, 3, 4, 4, 5, 5, 1), directed = FALSE) %>% as.metanet()
g2 <- make_graph(edges = c("4", 5, 5, 6, 6, 7, 7, 8, 8, 4), directed = FALSE) %>% as.metanet()
par(mfrow = c(1, 3))
plot(c_net_union(g1, g2))
plot(c_net_intersect(g1, g2))
plot(c_net_difference(g1, g2))

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
)

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

See Also

Other build: c_net_build(), c_net_from_edgelist(), c_net_set(), multi_net_build()

Calculate similarity for one t(otutab)

Description

Calculate similarity for one t(otutab)

Usage

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

See Also

vegdist

Other calculate: c_net_calculate(), fast_cor(), p.adjust.table(), read_corr()

Examples

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(...)

Arguments

...

tables

Value

formatted tables

Examples

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

Clean a igraph object

Description

Clean a igraph object

Usage

clean_igraph(go, direct = NULL)

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)

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))

MetaNet networks

Description

MetaNet co_nets

MetaNet networks

Description

MetaNet co_nets

MetaNet networks

Description

MetaNet co_nets

Transform a dataframe to a network edgelist.

Description

Transform a dataframe to a network edgelist.

Usage

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())

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
)

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

See Also

Other topological: fit_power(), get_group_skeleton(), links_stat(), nc(), net_par(), rand_net(), rand_net_par(), smallworldness()

Examples

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"))

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

See Also

Other calculate: c_net_calculate(), cal_sim(), p.adjust.table(), read_corr()

Examples

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)

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

See Also

Other module: get_community(), get_module(), get_module_eigen(), module_detect(), module_eigen(), module_net(), summary_module(), to_module_net(), zp_analyse()

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)

Fit power-law distribution for an igraph

Description

Fit power-law distribution for an igraph

Usage

fit_power(go, p.value = FALSE)

Arguments

go

igraph

p.value

calculate p.value

Value

ggplot

See Also

Other topological: extract_sample_net(), get_group_skeleton(), links_stat(), nc(), net_par(), rand_net(), rand_net_par(), smallworldness()

Examples

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,
  rescale = TRUE,
  ...
)

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.

rescale

logical, scale the X, Y to (-1,1)

...

add

Value

coors

See Also

Other g_layout: g_layout_nice(), g_layout_poly_sector(), g_layout_polygon()

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)

Layout with group nicely

Description

Layout with group nicely

Usage

g_layout_nice(go, group = "module", mode = "circlepack", group_zoom = 1, ...)

get_big_lay_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

group_zoom

zoom for each group

...

add

Value

coors

See Also

Other g_layout: g_layout(), g_layout_poly_sector(), g_layout_polygon()

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"))
}

Layout with group

Description

Layout with group

Usage

g_layout_poly_sector(
  go,
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  space = pi/4,
  jitter = 0,
  scale_node_num = TRUE,
  type = c("regular", "random"),
  mode = c("surface", "boundary"),
  curved = 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

space

the space between each arc, default: pi/4

jitter

for surface-regular, defalut 0

scale_node_num

scale with the node number in each group

type

Type of distribution: "random", "regular"

mode

"surface", "boundary"

curved

Optional curved attribute for coors

Value

coors

See Also

Other g_layout: g_layout(), g_layout_nice(), g_layout_polygon()

Layout with group as a polygon

Description

Layout with group as a polygon

Layout with group as a polyarc

Layout with group as a polycircle

Layout with group as a multi_layer

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_multi_layer(
  go,
  layout = igraph::in_circle(),
  group = "v_group",
  group_order = NULL,
  group2 = NULL,
  group2_order = NULL,
  scale_node_num = TRUE
)

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

layout

see method in c_net_layout

Value

coors

See Also

Other g_layout: g_layout(), g_layout_nice(), g_layout_poly_sector()

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_multi_layer(co_net2, group2 = "v_class") -> oridata
c_net_plot(co_net2, oridata)

Get community

Description

Get community

Usage

get_community(go_m)

Arguments

go_m

module metanet

Value

community

See Also

Other module: filter_n_module(), get_module(), get_module_eigen(), module_detect(), module_eigen(), module_net(), summary_module(), to_module_net(), zp_analyse()

Get edge information

Description

Get edge information

Usage

get_e(go, index = NULL)

Arguments

go

metanet object

index

attribute name, default: NULL

Value

data.frame

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_n(), get_v(), is_metanet()

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, ...)

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

See Also

Other topological: extract_sample_net(), fit_power(), links_stat(), nc(), net_par(), rand_net(), rand_net_par(), smallworldness()

Examples

get_group_skeleton(co_net) -> ske_net
skeleton_plot(ske_net)

Get module

Description

Get module

Usage

get_module(go_m)

Arguments

go_m

module metanet

Value

module

See Also

Other module: filter_n_module(), get_community(), get_module_eigen(), module_detect(), module_eigen(), module_net(), summary_module(), to_module_net(), zp_analyse()

Get module_eigen

Description

Get module_eigen

Usage

get_module_eigen(go_m)

Arguments

go_m

module metanet

Value

module_eigen

See Also

Other module: filter_n_module(), get_community(), get_module(), module_detect(), module_eigen(), module_net(), summary_module(), to_module_net(), zp_analyse()

Get network information

Description

Get network information

Usage

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

Arguments

go

metanet object

index

attribute name, default: NULL

simple

logical, get simple index

Value

data.frame

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_v(), is_metanet()

Get vertex information

Description

Get vertex information

Usage

get_v(go, index = NULL)

Arguments

go

metanet object

index

attribute name, default: NULL

Value

data.frame

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), is_metanet()

Input a cyjs file exported by Cytoscape

Description

Input a cyjs file exported by Cytoscape

Usage

input_cytoscape(file)

Arguments

file

cyjs file exported by Cytoscape

Value

list contains the igraph object and coordinates

See Also

Other plot: as.ggig(), c_net_plot(), input_gephi(), netD3plot(), olympic_rings_net(), plot.ggig(), twocol_edgelist(), venn_net()

Input a graphml file exported by Gephi

Description

Input a graphml file exported by Gephi

Usage

input_gephi(file)

Arguments

file

graphml file exported by Gephi

Value

list contains the igraph object and coordinates

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), netD3plot(), olympic_rings_net(), plot.ggig(), twocol_edgelist(), venn_net()

Is this object a metanet object?

Description

Is this object a metanet object?

Usage

is_metanet(go)

Arguments

go

a test object

Value

logical

See Also

Other manipulate: anno_edge(), anno_vertex(), c_net_annotate(), c_net_filter(), c_net_load(), c_net_save(), c_net_union(), get_e(), get_n(), get_v()

Examples

data(c_net)
is_metanet(co_net)

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()
)

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

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), nc(), net_par(), rand_net(), rand_net_par(), smallworldness()

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)
}

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
)

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

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_eigen(), module_net(), summary_module(), to_module_net(), zp_analyse()

Examples

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
)

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

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_detect(), module_net(), summary_module(), to_module_net(), zp_analyse()

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)

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
)

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

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_detect(), module_eigen(), summary_module(), to_module_net(), zp_analyse()

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"))

MetaNet networks

Description

MetaNet co_nets

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
)

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

See Also

Other build: c_net_build(), c_net_from_edgelist(), c_net_set(), c_net_update()

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)

Calculate natural_connectivity

Description

Calculate natural_connectivity

Usage

nc(p)

Arguments

p

an igraph or metanet object

Value

natural_connectivity (numeric)

References

`nc` in `ggClusterNet`

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), links_stat(), net_par(), rand_net(), rand_net_par(), smallworldness()

Examples

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

plot use networkD3

Description

plot use networkD3

Usage

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

Arguments

go

metanet

v_class

which attributes use to be v_class

...

see forceNetwork

Value

D3 plot

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), input_gephi(), olympic_rings_net(), plot.ggig(), twocol_edgelist(), venn_net()

Examples

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

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)

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

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), links_stat(), nc(), rand_net(), rand_net_par(), smallworldness()

Examples

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

Plot olympic rings using network

Description

Plot olympic rings using network

Usage

olympic_rings_net()

Value

network plot

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), input_gephi(), netD3plot(), plot.ggig(), twocol_edgelist(), venn_net()

Examples

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")

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

See Also

Other calculate: c_net_calculate(), cal_sim(), fast_cor(), read_corr()

Examples

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
)

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

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), input_gephi(), netD3plot(), olympic_rings_net(), twocol_edgelist(), venn_net()

Plot a metanet

Description

Plot a metanet

Usage

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

Arguments

x

metanet object

...

add

Value

plot

Plot a metanet_compare

Description

Plot a metanet_compare

Usage

## S3 method for class 'metanet_compare'
plot(x, coors_com = NULL, mains = NULL, ...)

Arguments

x

metanet_compare object

coors_com

coors object

mains

a vector of two strings for the main titles of the two networks

...

add

Value

plot

Plot a rmt_res

Description

Plot a rmt_res

Usage

## 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,
  ...
)

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", ...)

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, ...)

Arguments

x

vulnerability() result (vulnerability object)

...

add

Value

a ggplot

Plot e_type bar

Description

Plot e_type bar

Usage

plot_e_type_bar(go, mode = c("left", "right")[1], degree_threshold = 0)

Arguments

go

metanet object

mode

"left" or "right"

degree_threshold

degree threshold

Value

ggplot

Examples

data("c_net")
plot_e_type_bar(co_net, degree_threshold = 10)

Batch drawing multiple network diagrams

Description

Batch drawing multiple network diagrams

Usage

plot_multi_nets(graph_ls, nrow = NULL, ncol = NULL, multi_params_list = NULL)

Arguments

graph_ls

a list containing igraph objects

nrow

nrow

ncol

ncol

multi_params_list

a list of parameters for each network

Value

No value

Examples

plot_multi_nets(list(co_net, co_net2),
  multi_params_list = list(
    list(vertex.color = "skyblue"),
    list(vertex.color = "green3")
  )
)

Plot degree distribution of networks

Description

Plot degree distribution of networks

Usage

plot_net_degree(gols, net_names = NULL)

Arguments

gols

list of metanet

net_names

names of networks

Value

ggplot

Print method for 'cohesion' objects

Description

Print method for 'cohesion' objects

Usage

## 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, ...)

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, ...)

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, ...)

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, ...)

Arguments

x

'metanet' object

...

Additional arguments

Value

No value

Print method for 'metanet_compare' objects

Description

Print method for 'metanet_compare' objects

Usage

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

Arguments

x

'metanet_compare' 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, ...)

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, ...)

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, ...)

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)

Arguments

go

igraph object

plot

plot or not

Value

ggplot

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), links_stat(), nc(), net_par(), rand_net_par(), smallworldness()

Examples

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")
)

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

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), links_stat(), nc(), net_par(), rand_net(), smallworldness()

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)

Read a corr object

Description

Read a corr object

Usage

read_corr(filename)

Arguments

filename

filename of .corr

Value

a corr object

See Also

Other calculate: c_net_calculate(), cal_sim(), fast_cor(), p.adjust.table()

Save a corr object

Description

Save a corr object

Usage

save_corr(corr, filename = "corr")

Arguments

corr

a corr object

filename

filename without extension, default: "corr"

Value

a .corr file

Description

Show MetaNet logo

Usage

show_MetaNet_logo()

Value

picture

Calculate small-world coefficient

Description

Calculate small-world coefficient

Usage

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

Arguments

go

igraph or metanet

reps

simulation time

threads

threads

verbose

verbose

Value

number

See Also

Other topological: extract_sample_net(), fit_power(), get_group_skeleton(), links_stat(), nc(), net_par(), rand_net(), rand_net_par()

Examples


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

Generate spatial layout using spatstat

Description

Generate spatial layout using spatstat

Usage

spatstat_layout(
  go,
  win,
  type = c("random", "regular"),
  mode = c("surface", "boundary"),
  jitter = 0,
  curved = NULL,
  order_by = NULL,
  order_ls = NULL,
  order_circle = FALSE,
  seed = 1234,
  rescale = TRUE
)

Arguments

go

igraph or metanet object

win

A spatstat window object (owin), e.g. disc(), owin(poly=...); Or sf object.

type

Type of distribution: "random", "regular"

mode

"surface", "boundary"

jitter

for surface-regular, defalut 0

curved

Optional curved attribute for coors

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

order_circle

order nodes from the center of a circle

seed

random seed

rescale

rescale the coordinates to (0,1)

Value

A coors object (data.frame with class "coors" and attribute "curved")

Examples


if (requireNamespace("spatstat.geom") && requireNamespace("spatstat.random")) {
  poly_x <- c(0, 2, 2, 0)
  poly_y <- c(0, 0, 1, 1)
  win_poly <- spatstat.geom::owin(poly = list(x = poly_x, y = poly_y))
  plot(win_poly)
  coors1 <- spatstat_layout(co_net, win_poly, type = "regular", mode = "surface")
  plot(co_net, coors = coors1)
  coors2 <- spatstat_layout(co_net2, win_poly, type = "random", mode = "boundary")
  plot(co_net2, coors = coors2)

  if (requireNamespace("sf")) {
    nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
    poly <- nc[1, ]
    coors <- spatstat_layout(go = multi1, win = poly, type = "regular", mode = "surface")
    plot(multi1, coors = coors)
  }
}

Summaries two columns information

Description

Summaries two columns information

Usage

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()
}

Summary method for 'corr' objects

Description

Summary method for 'corr' objects

Usage

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

Arguments

object

'corr' object

...

Additional arguments

Value

No value

Summary module index

Description

Summary module index

Usage

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

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_detect(), module_eigen(), module_net(), to_module_net(), zp_analyse()

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")

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])

Arguments

go

metanet

edge_type

"module", "module_from", "module_to"

Value

metanet with modules

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_detect(), module_eigen(), module_net(), summary_module(), zp_analyse()

MetaNet networks abundance

Description

MetaNet co_nets

MetaNet networks metadata

Description

MetaNet co_nets

Transform the layout of a 'coors' object

Description

This function applies various transformations to a 'coors' object, including scaling, aspect ratio adjustment, rotation, mirroring, and pseudo-3D perspective transformation.

Usage

transform_coors(
  coors,
  scale = 1,
  aspect_ratio = 1,
  rotation = 0,
  mirror_x = FALSE,
  mirror_y = FALSE,
  shear_x = 0,
  shear_y = 0
)

Arguments

coors

An object of class 'coors', containing node coordinates.

scale

A numeric value to scale the layout (default = 1).

aspect_ratio

A numeric value to adjust the Y-axis scaling (default = 1).

rotation

A numeric value in degrees to rotate the layout (default = 0).

mirror_x

A logical value indicating whether to mirror along the X-axis (default = FALSE).

mirror_y

A logical value indicating whether to mirror along the Y-axis (default = FALSE).

shear_x

A numeric value to apply a shear transformation in the X direction (default = 0).

shear_y

A numeric value to apply a shear transformation in the Y direction (default = 0).

Value

A transformed 'coors' object with updated coordinates.

Quick build a metanet from two columns table

Description

Quick build a metanet from two columns table

Usage

twocol_edgelist(edgelist)

Arguments

edgelist

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

Value

metanet

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), input_gephi(), netD3plot(), olympic_rings_net(), plot.ggig(), venn_net()

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))

Venn network

Description

Venn network

Usage

venn_net(tab)

Arguments

tab

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

Value

plot

See Also

Other plot: as.ggig(), c_net_plot(), input_cytoscape(), input_gephi(), netD3plot(), olympic_rings_net(), plot.ggig(), twocol_edgelist()

Examples

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)

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

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

See Also

Other module: filter_n_module(), get_community(), get_module(), get_module_eigen(), module_detect(), module_eigen(), module_net(), summary_module(), to_module_net()

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)