View source: R/plot_rhcoclust.R
plot_rhcoclust | R Documentation |
This function used for two plots from output of rhcoclust (i) plot results for gene (row) and compound (column) co-cluster graph, and (ii) plot graph of QCC for identification of biomarker co-cluster.
plot_rhcoclust(CoClustObj, plot.cocluster = FALSE, plot.SCC = FALSE, cex.xaxis = 0.7, cex.yaxis = 0.5)
CoClustObj |
Output objects from rhcoclust |
plot.cocluster |
To set no plotting as the default for cocluster. |
plot.SCC |
To set no plotting as the default for SCC. |
cex.xaxis |
A numerical value giving to control/annotation text size in x-axis. Default is 0.7. |
cex.yaxis |
A numerical value giving to control/annotation text size in y-axis. Default is 0.5. |
Plots
Md. Bahadur Badsha <mbbadshar@gmail.com>
rhcoclust for generating a graph objects for clustering network
# Load necessary library library(rhcoclust) library(fields) # Load real data data("FCGE_Data_GMP") data("FCGE_Data_PPARs") # Load predefined real data # Real data use: data <- FCGE_Data_PPARs # Real data use: data <- FCGE_Data_GMP # Load predefined simulated data data("simu_data") # simulated data data <- simu_data # Apply rhcoclust to identify significant co-cluster of samples and their regulatory features CoClustObj <- rhcoclust(data, rk=4, ck=3, method.dist = "manhattan", method.hclust = "ward.D") # For real data either FCGE_Data_PPARs or FCGE_Data_GMP #CoClustObj <- rhcoclust(data, rk=3, ck=3, method.dist = "manhattan", method.hclust = "ward.D") # Plot co-cluster # Please use par(mar=c(6, 10, 3, 6)) or modify if needed for best fit of the graph # mar order: bottom, left, top, and right plot_rhcoclust (CoClustObj, plot.coclust = TRUE, plot.SCC = FALSE, cex.xaxis = 0.7, cex.yaxis = 0.5) # Plot SCC # Please use dev.off() to avoid the figure margin from previous plot plot_rhcoclust (CoClustObj, plot.coclust = FALSE, plot.SCC = TRUE) # Please add legend with change or add any parameters if needed. legend("topleft", legend = c("Upper-significant", "Insignificant","Down-significant"), col = c("red","black","blue"), bty = "n", pch = c(20,20,20), pt.cex = 2, cex = 1.2, x.intersp = 0.2, y.intersp = 0.4, text.col = "black", horiz = FALSE , inset = c(0.3, -0.08))
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