plotLOD {Anaquin} | R Documentation |
Create Limit-of-Detection Ratio (LOD) plot between measured abundance (x-axis) and p-value probability (y-axis).
plotLOD(measured, pval, ratio, qval, FDR, title, xlab, ylab, legTitle, showConf)
measured |
Measured abundance |
pval |
P-value probability |
ratio |
How to group ROC points |
qval |
Q-value probability. (Default to |
FDR |
Chosen false-discovery-rate. Default to |
title |
Title of the plot. (Default to |
xlab |
Label for the x-axis. (Default to |
ylab |
Label for the y-axis. (Default to |
legTitle |
Title for the legend. (Default to |
showConf |
Display confidence interval. (Default to |
Create a Limit-of-Detection Ratio (LOD) plot between measured abundance (x-axis) and p-value probability (y-axis).
The LOD plot indicates the confidence in measurement relative to the magnitude of the measurement. For example, p-value should converge to zero as the sequencing depth increases.
The function also fits non-parametric curves for each sequin ratio group. The curves are modelled with local regression analysis, and are colored by the sequin group.
plotLODR is a simplification from the ERCC dashboard R-package. Further details on the statistical algorithm is available in the ERCC documentation at https://bioconductor.org/packages/release/bioc/html/erccdashboard.html.
The function prints a LODR plot and return associated statistics.
Ted Wong t.wong@garvan.org.au
library(Anaquin) # # Data set generated by DESeq2 and Anaquin. described in Section 5.6.3.3 of # the user guide. # data(UserGuideData_5.6.3) xlab <- 'Average Counts' ylab <- 'P-value' title <- 'LOD Curves' # Sequin names seqs <- row.names(UserGuideData_5.6.3) # Expected log-fold group <- UserGuideData_5.6.3$ExpLFC # Measured average abundance measured <- UserGuideData_5.6.3$Mean # P-value pval <- UserGuideData_5.6.3$Pval # Q-value qval <- UserGuideData_5.6.3$Qval plotLOD(measured, pval, group, qval, xlab=xlab, ylab=ylab, title=title, FDR=0.1)