Ridgeogram {SigWinR} | R Documentation |
Create a RIDGEOGRAM for a sequence of data. The function tests the input sequence against the null hypothesis that the value in the sequence does depend on position. The function produces p-values for the probability that the sequence is locally higher or lower than average for all possible positions and all odd window-sizes. The p-value is corrected for multiple testing using the Benjamini-Hochberg correction.
Ridgeogram(s,adjust.method="BH",circular=FALSE)
s |
sequence of numeric data |
adjust.method |
method for multiple testing correction (see p.adjust for valid options) |
circular |
assume the sequence is circular |
A list containing
high |
Array containing p-values for the probability that the sequence is locally higher than average within the given window-size , the rows contain the windowsizes. starting from 3, the significance for all odd window sizes are calculated. The columns contain the p-values along the input sequence for a window size. If circular is FALSE the number of possible windows decreases with increasing window size and the output is centered within the rows. In this way the output can be rendered as an image (see RidgeogramPlot). |
low |
Same as pos for signifcantly lower regions |
The computation time increases quadratically with the length of the input sequence. Sequences up to about 10.000 do not pose a problem but if they are longer you'll need patience. Alternatively, reducing the input sequence lenght using averaging or median filtering can be considered.
w.c.deleeuw@uva.nl
SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences Márcia A Inda, Marinus F van Batenburg, Marco Roos, Adam SZ Belloum, Dmitry Vasunin, Adianto Wibisono, Antoine HC van Kampen, and Timo M Breit BMC Research Notes 2008; 1:63
SigWin,RidgeogramPlot
lseq <- c(rnorm(200),rnorm(100,-1),rnorm(50),rnorm(50,4.0),rnorm(100)) RidgeogramPlot(Ridgeogram(lseq),0.5,"Example ridgeogram",high.col="green",low.col="red")