Help for package CoDiNA

Type:

Package

Title:

Co-Expression Differential Network Analysis

Version:

1.1.2

Author:

Deisy Morselli Gysi, Tiago de Miranda Fragoso, Eivind Almaas and Katja Nowick.

Maintainer:

Deisy Morselli Gysi <deisy.ccnr@gmail.com>

Description:

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <doi:10.48550/arXiv.1802.00828>.

License:

GPL-2

LazyData:

TRUE

Depends:

R (≥ 3.1)

Imports:

data.table, igraph, magrittr, plyr, visNetwork, reshape2

RoxygenNote:

7.1.0

Suggests:

knitr, rmarkdown, wTO

NeedsCompilation:

Packaged:

2020-07-02 15:25:35 UTC; deisygysi

Repository:

CRAN

Date/Publication:

2020-07-15 12:30:02 UTC

.Random.seed

Description

Random numbers generated by set.seed(123)

AST

Description

This data.table contains node and the weighted topological overlap (wTO) of Transcription Factors (TFs), from GSE4290 (Sun, 2006) for 50 brain samples with ASTgodendrogliomas. The wTO was calculated using the package wTO.

Usage

data("AST")

Format

A data frame with 3488761 observations on the following 3 variables.

Node.1: a factor with levels. TF names
Node.2: a factor with levels. TF names
cor: a numeric vector. wTO values calculated using only the TFs

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4290

References

Sun L, Hui AM, Su Q, Vortmeyer A et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell 2006 Apr;9(4):287-300. PMID: 16616334

Examples

data(AST)
str(AST)

CTR

Description

This data.table contains node and the weighted topological overlap (wTO) of Transcription Factors (TFs), from GSE4290 (Sun, 2006) for 23 brain samples with Controls. The wTO was calculated using the package wTO.

Usage

data("CTR")

Format

A data frame with 3488761 observations on the following 3 variables.

Node.1: a factor with levels. TF names
Node.2: a factor with levels. TF names
cor: a numeric vector. wTO values calculated using only the TFs

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4290

References

Sun L, Hui AM, Su Q, Vortmeyer A et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell 2006 Apr;9(4):287-300. PMID: 16616334

Examples

data(CTR)
str(CTR)

Categorize

Description

Categorize the links into -1, 0 and 1 given a cutoff.

Usage

Categorize(M, cutoff = 0.33)

Arguments

M

A data.frame to be categorized.

cutoff

By default, the cutoff is 0.33. If the user wants to use another value, it has to be cited on the description of the used methodology that the cutoff was changed.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

ClusterNodes

Description

Categorize the Nodes into Phi and Phi tilde.

Usage

ClusterNodes(DiffNet, cutoff.external = 0.8, cutoff.internal = 0.5)

Arguments

DiffNet

The Differential network from MakeDiffNet

cutoff.external

The cut-off between the clusters (delta from the center to the edge coordinates), the closer to 1, the better.

cutoff.internal

The cut-off inside the clusters (delta from the theoretical cluster to the edge coordinates), the closer to zero, the better.

Examples

DiffNet = MakeDiffNet (Data = list(CTR,  AST), Code = c('CTR', 'AST') )
Genes_Phi = ClusterNodes(DiffNet, cutoff.external = 0.5, cutoff.internal = 0.25)
table(Genes_Phi$Phi_tilde)

CreateFullBase

Description

Joins a set of data.frames, order the nodes names by it's smaller value.

Usage

CreateFullBase(x, Code)

Arguments

x

List of data.frames containig Node.1, Node.2 and the correlation value

Code

Name of each one of the networks.

Value

Returns a list contating: The nodes names and it's correlation values in all networks, 0 if this node is absent.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

GLI

Description

This data.table contains node and the weighted topological overlap (wTO) of Transcription Factors (TFs), from GSE4290 (Sun, 2006) for 81 brain samples with glioblastomas. The wTO was calculated using the package wTO.

Usage

data("GLI")

Format

A data frame with 3488761 observations on the following 3 variables.

Node.1: a factor with levels. TF names
Node.2: a factor with levels. TF names
cor: a numeric vector. wTO values calculated using only the TFs

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4290

References

Sun L, Hui AM, Su Q, Vortmeyer A et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell 2006 Apr;9(4):287-300. PMID: 16616334

Examples

data(GLI)
str(GLI)

MakeDiffNet

Description

Categorize links into Phi categories, calculate the distance to the center and also normlize the distance into some categories: Phi and Phi tilda, group and all.

Usage

MakeDiffNet(Data, Code, cutoff = 0.33, stretch = TRUE)

Arguments

Data

List of data.frames containig Node.1, Node.2 and the correlation value

Code

Name of each one of the networks.

cutoff

By default, the cutoff is 0.33. If the user wants to use another value, it has to be cited on the description of the used methodology that the cutoff was changed.

stretch

Should the input data be normalized? Default to TRUE.

Value

Returns a data.table contating: Nodes names, correlation value for each network (the input values), the k means cluster that link belongs, the Phi groups (Phi and Phi tilda), the signed group that link belongs to, the unsigned group. The distance to the center, and the distance normalized by: Phi_tilda, Phi, signed group or all data.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

suppressWarnings(RNGversion("3.5.0"))
Nodes = LETTERS[1:20]
Net1 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net2 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net3 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
DiffNet = MakeDiffNet (Data = list(Net1,Net2,Net3), Code = c('Net1', 'Net2', 'Net3') )
print(DiffNet)

OLI

Description

This data.table contains node and the weighted topological overlap (wTO) of Transcription Factors (TFs), from GSE4290 (Sun, 2006) for 50 brain samples with oligodendrogliomas. The wTO was calculated using the package wTO.

Usage

data("OLI")

Format

A data frame with 3488761 observations on the following 3 variables.

Node.1: a factor with levels. TF names
Node.2: a factor with levels. TF names
cor: a numeric vector. wTO values calculated using only the TFs

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4290

References

Sun L, Hui AM, Su Q, Vortmeyer A et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell 2006 Apr;9(4):287-300. PMID: 16616334

Examples

data(OLI)
str(OLI)

OrderNames

Description

Sorts each link's Nodes by the smallest value. Removes links that both nodes are the same.

Usage

OrderNames(M)

Arguments

M

data.frame to have the names ordered. Node.1, Node.2 and correlation value.

Value

a data.table whith Node.1 and Node.2, sorted by the smallest value between both.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

Nodes = LETTERS[1:10]
Z = data.frame(Node.1 = sample(Nodes) ,
Node.2 = sample(Nodes), cor = runif(10,-1,1))
OrderNames(Z)

PhiCategory

Description

Categorize the links into Phi and Phi tilda categories.

Usage

PhiCategory(Base, n, CAT_BASE = CAT_BASE)

Arguments

Base

data.frame to be categorized

n

number of networks to be compared

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Title Recode

Description

Title Recode

Usage

Recode(Phi, values)

Arguments

Phi

Phi categories

values

Categorical base

as.igraph

Description

Converts the CoDiNA.plot into an igraph object.

Usage

as.igraph(x)

Arguments

x

the output from the function plot.

Value

the CoDiNA plot as an igraph object.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

suppressWarnings(RNGversion("3.5.0"))
Nodes = LETTERS[1:10]
Net1 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net2 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net3 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
DiffNet = MakeDiffNet (Data = list(Net1,Net2,Net3), Code = c('Net1', 'Net2', 'Net3') )
Graph = plot(x = DiffNet,
 layout = NULL, smooth.edges = TRUE,
 path = NULL, MakeGroups = FALSE, Cluster = FALSE,
 legend = TRUE, manipulation = FALSE, sort.by.Phi = FALSE)
x = as.igraph(Graph)

plot(x)

normalize

Description

Normalize a given variable.

Usage

normalize(m)

Arguments

m

variable to be normalized in the interval [0,1]

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

Z = runif(10,-10,10)
normalize(Z)

plot.CoDiNA

Description

Categorize the Nodes into Phi and groups categories. Also, creates an interactive view of the CoDiNA network.

Usage

## S3 method for class 'CoDiNA'
plot(
  x,
  cutoff.external = 0,
  cutoff.internal = 1,
  cutoff.ratio = 1,
  layout = NULL,
  smooth.edges = TRUE,
  path = NULL,
  MakeGroups = FALSE,
  Cluster = FALSE,
  legend = TRUE,
  manipulation = FALSE,
  sort.by.Phi = FALSE,
  ...
)

Arguments

x

Output from MakeDiffNet

cutoff.external

The cut-off between the clusters (delta from the center to the edge coordinates), the closer to 1, the better.

cutoff.internal

The cut-off inside the clusters (delta from the theoretical cluster to the edge coordinates), the closer to zero, the better.

cutoff.ratio

The cut-off for the ratio of both scores. Default is set to 1. The greater, the better.

layout

a layout from the igraph package.

smooth.edges

If the edges should be smoothed or not.

path

If the graph should be saved specify the name of the file.

MakeGroups

algorithm to find clusters. One of the followings: walktrap, optimal, spinglass, edge.betweenness, fast_greedy, infomap, louvain, label_prop, leading_eigen. Default to FALSE.

Cluster

TRUE or FALSE if the nodes should be clustered (double click to uncluster).

legend

TRUE or FALSE if the legend should appear.

manipulation

TRUE or FALSE if the graph should be editable.

sort.by.Phi

if the graph should be plotted in the Phi order

...

Additional plotting parameters.

Value

Returns a list contatining: The nodes description, the Edges description and the network graph.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

suppressWarnings(RNGversion("3.5.0"))

Nodes = LETTERS[1:10]
Net1 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net2 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net3 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
DiffNet = MakeDiffNet (Data = list(Net1,Net2,Net3), Code = c('Net1', 'Net2', 'Net3') )
Graph = plot(x = DiffNet,
 layout = NULL, smooth.edges = TRUE,
 path = NULL, MakeGroups = FALSE, Cluster = FALSE,
 legend = TRUE, manipulation = FALSE, sort.by.Phi = FALSE)
Graph

print.CoDiNA

Description

Print on the screen the number of nodes and edges. To see the data.frame, call: data.frame().

Usage

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

Arguments

x

Output from MakeDiffNet

...

Additional plotting parameters.

Value

Print on the screen the number of nodes and edges.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

Nodes = LETTERS[1:10]
Net1 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net2 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net3 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
DiffNet = MakeDiffNet (Data = list(Net1,Net2,Net3), Code = c('Net1', 'Net2', 'Net3') )
print(DiffNet)

summary.CoDiNA

Description

summary of the CoDiNA network.

Usage

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

Arguments

object

Output from MakeDiffNet

...

Additional plotting parameters.

Value

Returns a summary describing the network.

Author(s)

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

Examples

Nodes = LETTERS[1:10]
Net1 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net2 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
Net3 = data.frame(Node.1 = sample(Nodes) , Node.2 = sample(Nodes), wTO = runif(10,-1,1))
DiffNet = MakeDiffNet (Data = list(Net1,Net2,Net3), Code = c('Net1', 'Net2', 'Net3') )
summary(DiffNet)