Package 'NetSci' reference manual

Title:	Calculates Basic Network Measures Commonly Used in Network Medicine
Description:	Calculates network measures commonly used in Network Medicine. Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving.
Authors:	Deisy Morselli Gysi [aut, cre]
Maintainer:	Deisy Morselli Gysi <[email protected]>
License:	GPL-2
Version:	1.0.1
Built:	2024-11-14 04:29:34 UTC
Source:	https://github.com/cran/NetSci

avr_proximity_multiple_target_sets

Description

Calculates the average proximity from a set of targets to a set of source nodes. It is calculate using a degree preserving randomization. It is calculated as described in Guney, E. et al (2016) <doi.org:10.1038/ncomms10331>

Usage

avr_proximity_multiple_target_sets(
  set,
  G,
  ST,
  source,
  N = 1000,
  bins = 100,
  min_per_bin = 20,
  weighted = FALSE
)
avr_proximity_multiple_target_sets(
  set,
  G,
  ST,
  source,
  N = 1000,
  bins = 100,
  min_per_bin = 20,
  weighted = FALSE
)

Arguments

`set`	Name of the sets you have targets for. (In a drug-target setup, those would be the drugs of interest).
`G`	The original graph (often an interactome).
`ST`	Set-Target data. It is a data.frame with two columns. ID and Target.
`source`	The source nodes (disease genes).
`N`	Number of randomizations.
`bins`	the number os bins for the degree preserving randomization.
`min_per_bin`	the minimum size of each bin.
`weighted`	consider a weighted graph? TRUE/FALSE

Value

proximity and its significance based on the degree preserving randomization.

Examples

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)
net$weight = runif(nrow(net))

g <- igraph::graph_from_data_frame(net, directed = FALSE )
S = c("N", "A", "F", "I")
T1 = data.frame(ID = "T1", Target = c("H", "M"))
T2 = data.frame(ID = "T2", Target = c("G", "O"))

avr_proximity_multiple_target_sets(set = c('T1', 'T2'),
G = g,
 source = S,
 ST = rbind(T1,T2),
 bins = 1,
 min_per_bin = 2)

# In a weighted graph
# avr_proximity_multiple_target_sets(set = c('T1', 'T2'),
# G = g,
#  source = S,
#  ST = rbind(T1,T2),
#  bins = 1,
#  min_per_bin = 2,
#  weighted = TRUE)

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)
net$weight = runif(nrow(net))

g <- igraph::graph_from_data_frame(net, directed = FALSE )
S = c("N", "A", "F", "I")
T1 = data.frame(ID = "T1", Target = c("H", "M"))
T2 = data.frame(ID = "T2", Target = c("G", "O"))

avr_proximity_multiple_target_sets(set = c('T1', 'T2'),
G = g,
 source = S,
 ST = rbind(T1,T2),
 bins = 1,
 min_per_bin = 2)

# In a weighted graph
# avr_proximity_multiple_target_sets(set = c('T1', 'T2'),
# G = g,
#  source = S,
#  ST = rbind(T1,T2),
#  bins = 1,
#  min_per_bin = 2,
#  weighted = TRUE)

Extract LCC from a graph

Description

Extract LCC from a graph

Usage

extract_LCC(g)
extract_LCC(g)

Arguments

`g`	is the graph you want to extract the largest connected component

Value

a graph (from igraph) with only the largest connected component

Examples

set.seed(12)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)
LCC = extract_LCC(g)


set.seed(12)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)
LCC = extract_LCC(g)

Histogram_LCC

Description

Plots the histogram to evaluate the significance of the Largest Connected Component (LCC).

Usage

Histogram_LCC(LCC_L, Name = NULL)
Histogram_LCC(LCC_L, Name = NULL)

Arguments

`LCC_L`	an output from the function LCC_Significance or LCC_Bipartide
`Name`	title of the plot

Value

An Histogram of the simulated LCC, and a red line of the actual LCC.

Examples

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
targets = c("N", "A", "I", "F")
LCC_Out = LCC_Significance(N = 1000,
                 Targets = targets,
                                  G = g,
                                  bins = 5,
                                  min_per_bin = 2)
                                  # in a real interactome, please use the default

Histogram_LCC(LCC_Out, "Example")
set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
targets = c("N", "A", "I", "F")
LCC_Out = LCC_Significance(N = 1000,
                 Targets = targets,
                                  G = g,
                                  bins = 5,
                                  min_per_bin = 2)
                                  # in a real interactome, please use the default

Histogram_LCC(LCC_Out, "Example")

Hypergeometric.test

Description

Calculates the significance of an overlap of two sets using an hypergeometric test. It is a wrapper of the 'phyper' function.

Usage

Hypergeometric.test(
  success,
  universe_success,
  universe_failure,
  size_collected,
  lower.tail = FALSE
)
Hypergeometric.test(
  success,
  universe_success,
  universe_failure,
  size_collected,
  lower.tail = FALSE
)

Arguments

`success`	Is the number of elements in the overlap of the sets.
`universe_success`	Is the number of elements of the set of interest.
`universe_failure`	Is the number of elements of the set of the other set.
`size_collected`	The total of elements in the universe
`lower.tail`	Should the test be calculated on the lower tail? (Hypothesis test is lower than)

Value

the p-value for the hypergeometric test.

Examples

require(magrittr)
s = 10; S = 15; f = 10; T = 30
Hypergeometric.test(success = s,
universe_success = S,
universe_failure = f,
size_collected = T
)
require(magrittr)
s = 10; S = 15; f = 10; T = 30
Hypergeometric.test(success = s,
universe_success = S,
universe_failure = f,
size_collected = T
)

Jaccard

Description

Calculates the Jaccard index between different sets.

Usage

Jaccard(Data)
Jaccard(Data)

Arguments

Data

A data.frame with 2 columns. The first refers to the set and the second the elements

Value

a data.frame with the set names and their Jaccard index

Examples

set.seed(123)
Data = data.frame(Class = sample(c("X", "Y", "Z"), replace = TRUE, size = 50),
                  Element = sample(LETTERS[1:15], replace = TRUE, size = 50))
Data = unique(Data)
Jaccard(Data)
set.seed(123)
Data = data.frame(Class = sample(c("X", "Y", "Z"), replace = TRUE, size = 50),
                  Element = sample(LETTERS[1:15], replace = TRUE, size = 50))
Data = unique(Data)
Jaccard(Data)

LCC Significance

Description

Calculates the Largest Connected Component (LCC) from a given graph, and calculates its significance using a degree preserving approach. Menche, J., et al (2015) <doi.org:10.1126/science.1065103>

Usage

LCC_Significance(
  N = N,
  Targets = Targets,
  G,
  bins = 100,
  hypothesis = "greater",
  min_per_bin = 20
)
LCC_Significance(
  N = N,
  Targets = Targets,
  G,
  bins = 100,
  hypothesis = "greater",
  min_per_bin = 20
)

Arguments

`N`	Number of randomizations.
`Targets`	Name of the nodes that the subgraph will focus on - Those are the nodes you want to know whether if forms an LCC.
`G`	The graph of interest (often, in NetMed it is an interactome - PPI).
`bins`	the number os bins for the degree preserving randomization. When bins = 1, assumes a uniform distribution for nodes.
`hypothesis`	are you expecting an LCC greater or smaller than the average?
`min_per_bin`	the minimum size of each bin.

Value

a list with the LCC - $LCCZ all values from the randomizations - $mean the average LCC of the randomizations - $sd the sd LCC of the randomizations - $Z The score - $LCC the LCC of the given targets - $emp_p the empirical p-value for the LCC - $rLCC the relative LCC

Examples

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
plot(g)
targets = c("I", "H", "F", "E")
 LCC_Significance(N = 100,
                   Targets = targets,
                                    G = g,
                                  bins = 1,
                                  min_per_bin = 2)


set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
plot(g)
targets = c("I", "H", "F", "E")
 LCC_Significance(N = 100,
                   Targets = targets,
                                    G = g,
                                  bins = 1,
                                  min_per_bin = 2)

Global Definition

Description

Basic global variables to make sure the package runs.

Proximity from target to source

Description

Calculates the proximity (average or closest) from source to targets.

Usage

proximity_average(G, source, targets)
proximity_average(G, source, targets)

Arguments

`G`	The original graph (often an interactome).
`source`	nodes from the network (in a drug repurpusing set-up those are the disease genes)
`targets`	targets in the network (in a drug repurpusing set-up those are the drug-targets)

Value

the proximity value for the source-targets

Examples

#' set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average(g, source = S, targets = T)
#' set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average(g, source = S, targets = T)

Proximity from target to source

Description

Calculates the weighted average proximity from source to targets.

Usage

proximity_average_weighted(G, source, targets)
proximity_average_weighted(G, source, targets)

Arguments

`G`	The original graph (often a weighted interactome).
`source`	nodes from the network (in a drug repurpusing set-up those are the disease genes)
`targets`	targets in the network (in a drug repurpusing set-up those are the drug-targets)

Value

the proximity value for the source-targets

Examples

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)
net$weight = runif(nrow(net))
g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average_weighted(g, source = S, targets = T)
set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)
net$weight = runif(nrow(net))
g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average_weighted(g, source = S, targets = T)

Proximity from target to source

Description

Calculates the proximity (average or closest) from source to targets.

Usage

proximity_close(G, source, targets)
proximity_close(G, source, targets)

Arguments

`G`	The original graph (often an interactome).
`source`	nodes from the network (in a drug repurpusing set-up those are the disease genes)
`targets`	targets in the network (in a drug repurpusing set-up those are the drug-targets)

Value

the proximity value for the source-targets

Examples

set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_close(g, source = S, targets = T)
set.seed(666)
net  = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net =  CoDiNA::OrderNames(net)
net = unique(net)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_close(g, source = S, targets = T)

Separation

Description

Calculates the separation of two set of targets on a network. Often used to measure separation of disease modules in a interactome. Separation is calculated as in Menche, J. et al (2015) <doi:10.1126/science.1257601>.

Usage

separation(G, ST)
separation(G, ST)

Arguments

`G`	The original graph (often an interactome).
`ST`	Set-Target data. It is a data.frame with two columns. ID and Target.

Value

the separation and distance of modules.

Examples

require(magrittr)
set.seed(12)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

D1 = data.frame(gene = c("H", "I", "S", "N", "A"), disease = "D1")
D2 = data.frame(gene = c("E", "C",  "R" , "J", "Q", "O"), disease = "D2")
D3 = data.frame(gene = c("E", "G", "T", "P"), disease = "D3")
D4 = data.frame(gene = c("A", "B", "E"), disease = "D4")

Diseases = rbind(D1, D2, D3, D4)
Diseases %<>% dplyr::select(disease, gene)
g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)

separation(G = g, ST = Diseases)
require(magrittr)
set.seed(12)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

D1 = data.frame(gene = c("H", "I", "S", "N", "A"), disease = "D1")
D2 = data.frame(gene = c("E", "C",  "R" , "J", "Q", "O"), disease = "D2")
D3 = data.frame(gene = c("E", "G", "T", "P"), disease = "D3")
D4 = data.frame(gene = c("A", "B", "E"), disease = "D4")

Diseases = rbind(D1, D2, D3, D4)
Diseases %<>% dplyr::select(disease, gene)
g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)

separation(G = g, ST = Diseases)

Separation Significance

Description

Calculates the separation of two set of targets on a network and assigns a p-value to it. Often used to measure separation of disease modules in a interactome. Separation is calculated as in Menche, J. et al (2015) <doi:10.1126/science.1257601>. p-values are calculates based on the permutation of nodes, you can set the full network to be in the set for permutation or can select the ones you include as input.

Usage

separation_Significance(G, ST, Threads = 2, N = 1000, correct_by_target = TRUE)
separation_Significance(G, ST, Threads = 2, N = 1000, correct_by_target = TRUE)

Arguments

`G`	The original graph (often an interactome / PPI).
`ST`	Set-Target data. It is a data.frame with two columns. ID and Target.
`Threads`	How many threads you'd like to use (for parallel computation).
`N`	default to 1000. The number of permutations
`correct_by_target`	TRUE by default. If you want to use the set of targets for the permutation or the full network.

Value

the separation and distance of modules and its p-value.

Examples

set.seed(12)
require(magrittr)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

D1 = data.frame(gene = c("H", "I", "S", "N", "A"), disease = "D1")
D2 = data.frame(gene = c("E", "C",  "R" , "J", "Q", "O"), disease = "D2")
D3 = data.frame(gene = c("E", "G", "T", "P"), disease = "D3")
D4 = data.frame(gene = c("A", "B", "E"), disease = "D4")
D5 = data.frame(gene = c("D", "F", "L"), disease = "D5")
D6 = data.frame(gene = c("D", "F", "K"), disease = "D6")
D7 = data.frame(gene = c("A", "B" ,"F", "K"), disease = "D7")

Diseases = rbind(D1, D2, D3, D4, D5, D6, D7)
Diseases %<>% dplyr::select(disease, gene)
g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)

separation_Significance(G = g,
ST = Diseases,
correct_by_target = FALSE,
Threads = 2)
set.seed(12)
require(magrittr)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

D1 = data.frame(gene = c("H", "I", "S", "N", "A"), disease = "D1")
D2 = data.frame(gene = c("E", "C",  "R" , "J", "Q", "O"), disease = "D2")
D3 = data.frame(gene = c("E", "G", "T", "P"), disease = "D3")
D4 = data.frame(gene = c("A", "B", "E"), disease = "D4")
D5 = data.frame(gene = c("D", "F", "L"), disease = "D5")
D6 = data.frame(gene = c("D", "F", "K"), disease = "D6")
D7 = data.frame(gene = c("A", "B" ,"F", "K"), disease = "D7")

Diseases = rbind(D1, D2, D3, D4, D5, D6, D7)
Diseases %<>% dplyr::select(disease, gene)
g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)

separation_Significance(G = g,
ST = Diseases,
correct_by_target = FALSE,
Threads = 2)

Package 'NetSci'

Help Index

avr_proximity_multiple_target_sets

Description

Usage

Arguments

Value

Examples

Extract LCC from a graph

Description

Usage

Arguments

Value

Examples

Histogram_LCC

Description

Usage

Arguments

Value

Examples

Hypergeometric.test

Description

Usage

Arguments

Value

Examples

Jaccard

Description

Usage

Arguments

Value

Examples

LCC Significance

Description

Usage

Arguments

Value

Examples

Global Definition

Description

Proximity from target to source

Description

Usage

Arguments

Value

Examples

Proximity from target to source

Description

Usage

Arguments

Value

Examples

Proximity from target to source

Description

Usage

Arguments

Value

Examples

Separation

Description

Usage

Arguments

Value

Examples

Separation Significance

Description

Usage

Arguments

Value

Examples