bnpathplot | R Documentation |
Plot pathway relationship
bnpathplot(
results,
exp,
expSample = NULL,
algo = "hc",
algorithm.args = NULL,
expRow = "ENSEMBL",
cl = NULL,
returnNet = FALSE,
otherVar = NULL,
otherVarName = NULL,
qvalueCutOff = NULL,
adjpCutOff = 0.05,
nCategory = 15,
R = 20,
interactive = FALSE,
color = "p.adjust",
cexCategory = 1,
cexLine = 0.5,
chooseDir = FALSE,
showDir = FALSE,
delZeroDegree = TRUE,
labelSize = 4,
layout = "nicely",
onlyDf = FALSE,
disc = FALSE,
tr = NULL,
remainCont = NULL,
shadowText = TRUE,
bgColor = "white",
textColor = "black",
compareRef = FALSE,
strThresh = NULL,
strType = "normal",
hub = NULL,
scoreType = "bic-g",
databasePal = "Set2",
dep = NULL,
sizeDep = FALSE,
orgDb = org.Hs.eg.db,
bypassConverting = FALSE,
useSiGN = FALSE,
edgeLink = TRUE,
cellLineName = "5637_URINARY_TRACT",
strengthPlot = FALSE,
nStrength = 10,
seed = 1
)
results |
the enrichment analysis result |
exp |
gene expression matrix |
expSample |
candidate rows to be included in the inference default to all |
algo |
structure learning method used in boot.strength() default to "hc" |
algorithm.args |
parameters to pass to bnlearn structure learnng function |
expRow |
the type of the identifier of rows of expression matrix |
cl |
cluster object from parallel::makeCluster() |
returnNet |
whether to return the network |
otherVar |
other variables to be included in the inference |
otherVarName |
the names of other variables |
qvalueCutOff |
the cutoff value for qvalue |
adjpCutOff |
the cutoff value for adjusted pvalues |
nCategory |
the number of pathways to be included |
R |
the number of bootstrap |
interactive |
whether to use bnviewer (default to FALSE) |
color |
color of node, default to adjusted p-value |
cexCategory |
scaling factor of size of nodes |
cexLine |
scaling factor of width of edges |
chooseDir |
if undirected edges are present, choose direction of edges |
showDir |
show the confidence of direction of edges |
delZeroDegree |
delete zero degree nodes |
labelSize |
the size of label of the nodes |
layout |
ggraph layout, default to "nicely" |
onlyDf |
return only data.frame used for inference |
disc |
discretize the expressoin data |
tr |
Specify data.frame if one needs to discretize as the same parameters as the other dataset |
remainCont |
Specify characters when perform discretization, if some columns are to be remain continuous |
shadowText |
whether to use shadow text for the better readability (default: TRUE) |
bgColor |
color for text background when shadowText is TRUE |
textColor |
color for text when shadowText is TRUE |
compareRef |
whether compare to the reference network between pathway |
strThresh |
threshold for strength, automatically determined if NULL |
strType |
"normal" or "ms" for multiscale implementation |
hub |
change the shape of node according to hub scores (default NULL) |
scoreType |
score type to use on choosing edge direction |
databasePal |
palette to be used in scale_color_brewer when the multiple results are to be shown |
dep |
the tibble storing dependency score from library depmap |
sizeDep |
whether to reflect DepMap score to the node size |
orgDb |
perform clusterProfiler::setReadable based on this organism database |
bypassConverting |
bypass the symbol converting If you use custom annotation databases that does not have SYMBOL listed in keys. ID of rownames and those listed in EA result must be same. |
useSiGN |
default to FALSE. For using SiGN-BN in the function in Windows 10/11, 1. Download the SiGN-BN HC+BS binary in WSL (https://sign.hgc.jp/signbn/download.html) 2. Set PATH to executable (sign.1.8.3) |
edgeLink |
whether to set edge to geom_edge_link() FALSE to use geom_edge_diagonal() |
cellLineName |
the cell line name to be included |
strengthPlot |
append the barplot depicting edges with high strength |
nStrength |
specify how many edges are included in the strength plot |
seed |
A random seed to make the analysis reproducible, default is 1. |
ggplot2 object
data("exampleEaRes");data("exampleGeneExp")
res <- bnpathplot(results = exampleEaRes, exp = exampleGeneExp,
R = 10, expRow = "ENSEMBL")
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