| Type: | Package |
| Title: | Calculates Basic Network Measures Commonly Used in Network Medicine |
| Version: | 1.0.1 |
| 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. |
| Imports: | igraph, magrittr, wTO, dplyr, Rfast, utils, binr, cubature |
| Suggests: | CoDiNA |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-10-10 19:40:55 UTC; deisygysi |
| Author: | Deisy Morselli Gysi [aut, cre] |
| Maintainer: | Deisy Morselli Gysi <deisy.gysi@ufpr.br> |
| Repository: | CRAN |
| Date/Publication: | 2024-10-14 12:20:21 UTC |
Histogram_LCC
Description
Plots the histogram to evaluate the significance of the Largest Connected Component (LCC).
Usage
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")
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
)
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
)
Jaccard
Description
Calculates the Jaccard index between different sets.
Usage
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)
Internal
Description
Internal functions. Not exported.
Usage
LCC_Calc(PPIg, bins = 100, nodes, n, min_per_bin = 20)
Arguments
PPIg |
network |
bins |
number of bins |
nodes |
nodes |
n |
number of resamples |
min_per_bin |
minimum number of nodes per bin |
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
)
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)
Global Definition
Description
Basic global variables to make sure the package runs.
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
)
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)
Extract LCC from a graph
Description
Extract LCC from a graph
Usage
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)
Proximity from target to source
Description
Calculates the proximity (average or closest) from source to targets.
Usage
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$value = 1
net = CoDiNA::OrderNames(net)
net = unique(net)
g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average(g, source = S, targets = T)
Proximity from target to source
Description
Calculates the weighted average proximity from source to targets.
Usage
proximity_average_weighted(G, source, targets)
Arguments
G |
The original graph (often a weighted interactome). |
source |
nodes from the network (in a drug repurpusing set-up those are the disease genes) |
targets |
targets in the network (in a drug repurpusing set-up those are the drug-targets) |
Value
the proximity value for the source-targets
Examples
set.seed(666)
net = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$value = 1
net = CoDiNA::OrderNames(net)
net = unique(net)
net$weight = runif(nrow(net))
g <- igraph::graph_from_data_frame(net, directed = FALSE )
T = c("G", "A", "D")
S = c("C", "M")
proximity_average_weighted(g, source = S, targets = T)
Proximity from target to source
Description
Calculates the proximity (average or closest) from source to targets.
Usage
proximity_close(G, source, targets)
Arguments
G |
The original graph (often an interactome). |
source |
nodes from the network (in a drug repurpusing set-up those are the disease genes) |
targets |
targets in the network (in a drug repurpusing set-up those are the drug-targets) |
Value
the proximity value for the source-targets
Examples
set.seed(666)
net = data.frame(
Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
net$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)
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)
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)
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)
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)