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plot-methods

Plot


Description

Considering the class of the argument which is passed to plot, the graphical output differs.

Usage

## S4 method for signature 'micro_array,ANY'
plot(x, y, ...)

## S4 method for signature 'network,ANY'
plot(
  x,
  y,
  choice = "network",
  nv = 0,
  gr = NULL,
  ini = NULL,
  color.vertex = NULL,
  video = TRUE,
  weight.node = NULL,
  ani = FALSE,
  taille = c(2000, 1000),
  label_v = 1:dim(x@network)[1],
  horiz = TRUE,
  legend.position = "topleft",
  frame.color = "black",
  label.hub = FALSE,
  ...
)

## S4 method for signature 'micropredict,ANY'
plot(
  x,
  time = NULL,
  label_v = NULL,
  frame.color = "white",
  ini = NULL,
  label.hub = FALSE,
  edge.arrow.size = 0.7,
  edge.thickness = 1
)

Arguments

x

a micro\_array object, a network object or a micropredict object

y

optional and not used if x is an appropriate structure

...

additional parameters

choice

what graphic should be plotted: either "F" (for a representation of the matrices F) or "network".

nv

the level of cutoff. Defaut to '0'.

gr

a vector giving the group of each gene

ini

using the “position” function, you can fix the position of the nodes.

color.vertex

a vector defining the color of the vertex.

video

if ani is TRUE and video is TRUE, the result of the animation is saved as an animated GIF.

weight.node

nodes weighting. Defaults to 'NULL'.

ani

animated plot?

taille

vector giving the size of the plot. Default to 'c(2000,1000)'.

label_v

vector defining the vertex labels.

horiz

landscape? Defaults to 'TRUE'.

legend.position

position of the legend.

frame.color

color of the frames.

label.hub

logical ; if TRUE only the hubs are labeled.

time

sets the time for plot of the prediction. Defaults to 'NULL'

edge.arrow.size

size of the arrows ; default to 0.7.

edge.thickness

edge thickness ; default to 1.

Methods

list("signature(x = \"micro_array\", y = \"ANY\",...)")
x

a micro\_array object

list_nv

a vector of cutoff at which the network should be shown

list("signature(x = \"network\", y = \"ANY\",...)")
x

a network object

list()

Optionnal arguments:

gr

a vector giving the group of each gene

choice

what graphic should be plotted: either "F" (for a representation of the matrices F) or "network".

nv

the level of cutoff. Defaut to 0.

ini

using the “position” function, you can fix the position of the nodes

color.vertex

a vector defining the color of the vertex

ani

animated plot?

size

vector giving the size of the plot. Default to c(2000,1000)

video

if ani is TRUE and video is TRUE, the animation result is a GIF video

label_v

vector defining the vertex labels

legend.position

position of the legend

frame.color

color of the frames

label.hub

logical ; if TRUE only the hubs are labeled

edge.arrow.size

size of the arrows ; default to 0.7

edge.thickness

edge thickness ; default to 1.

list("signature(x = \"micropredict\", y = \"ANY\",...)")
x

a micropredict object

list()

Optionnal arguments: see plot for network

Examples

data(Net)
plot(Net)

data(M)
plot(M)

data(Selection)
data(network)
nv<-0.11
plot(network,choice="network",gr=Selection@group,nv=nv,label_v=Selection@name,
edge.arrow.size=0.9,edge.thickness=1.5)

Cascade

Selection, Reverse-Engineering and Prediction in Cascade Networks

v2.0
GPL (>= 2)
Authors
Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Laurent Vallat [ctb], Nicolas Jung [ctb]
Initial release
2021-03-18

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