difconet.plot.gene.correlations {difconet} | R Documentation |
PLOTS THE CORRELATIONS OF A SPECIFIC GENE
Description
Draw scatter plots of the correlations of a specific gene.
Usage
difconet.plot.gene.correlations(dObj, gene,
stages=1:length(dObj$stages.data), type=c("density","scatter")[1],
main=rownames(dObj$stages.data[[1]])[gene],
legends=TRUE, plot=TRUE, ... )
Arguments
dObj |
The difconet object. |
gene |
Numeric or character. The gene index/rowname whose correlations will be drawn. |
stages |
Numeric or character. The stages to be included. If type="scatter" and more than two stages, a call to pairs is used instead of plot. |
type |
Character. The type of plot density or scatter. |
main |
Character. The main title passed to plot. |
legends |
Logical. Specifies whether the legends are drawn when type="density". |
plot |
Logical. Specifies whether the plots are actually drawn (to get the correlations). |
... |
Further parameters passed to plot/pairs. |
Details
Run the whole process of estimation differences in correlations for a given dataset. The estimations are done for all metric values, all cutoff values across all comparisons.
Value
The correlations of the gene across stages (invisible).
Author(s)
Elpidio Gonzalez and Victor Trevino vtrevino@itesm.mx
References
Gonzalez-Valbuena and Trevino 2017 Metrics to Estimate Differential Co-Expression Networks Journal Pending volume 00–10
See Also
Examples
xdata <- matrix(rnorm(1000), ncol=100)
xpredictor <- sample(c("A","B","C","D"),100,replace=TRUE)
dObj <- difconet.run(xdata, xpredictor, metric = 4, num_perms = 10,
comparisons = list(c("A","D"), c("A","B"), c("B","D")),
perm_mode = "columns")
#Top highest metric in first comparison but showing correlations in only 3 stages
difconet.plot.gene.correlations(dObj, order(dObj$combstats[[1]][,"M4.dist"],
decreasing=TRUE)[1], type="s", stages=1:3)
#Bottom lowest metric in second comparison showing all stages
difconet.plot.gene.correlations(dObj, order(dObj$combstats[[2]][,"M4.dist"],
decreasing=TRUE)[1], type="d")
#Another specific gene (3), showing densities of correlations
difconet.plot.gene.correlations(dObj, 3, type="d")