PosRidgeogram {SigWinR} | R Documentation |
Create a RIDGEOGRAM for a sequence of data of non-regularly sampled 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 a certain position and window-size for a range of positions and window sizes. By default the p-value is corrected for multiple testing using the Benjamini-Hochberg correction.
PosRidgeogram(seq,pos,resolution=400,winrange=c(0,max(pos)-min(pos)),adjust.method="BH",circular=FALSE)
seq |
sequence of samples |
pos |
sequence containing location of samples |
resolution |
resolution of ridgeogram. Both the number of samples along the sequence for which a p-value has to be calculated and the number of widow sizes to be used between the minimum and maximum value specified in winrange. |
winrange |
minimum and maximum values for the range of window sizes for which the Ridgeogram has to be calculated. The resolution parameter determines the number of window sizes between the minimum and maximum will be calculated. |
adjust.method |
method for multiple testing correction (see p.adjust for valid options) |
circular |
assume the sequence is circular |
For a PosRidgeogram the number of entries in the ridgeogram given a certain window-size is not fixed by the length of the sequence as it is in the standard ridgeogram. The window-size is linked to the position values in the input sequence and not to element number in the sequence. The sampling density must be specified seperately by the resolution parameter. This resolution is also independent the window-size for which ridges and anti-ridges will be calculated.
A list containing
high |
Array containing Benjamini-Hochberg corrected 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 high for signifcantly lower regions. |
sequence |
The input sequence used. |
position |
The input location used. |
range |
The location range used. |
circular |
Circularity was assumed. |
The computation time is significantly higher than the Ridgeogram function. Resolutions below 1000 do not pose a problem but for higher resolutions you'll need patience.
w.c.deleeuw@uva.nl
SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences Marcia 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
slen <- 200 pos <- 4*sort(rnorm(slen)) RidgeogramPlot(PosRidgeogram(sin(pos)+rnorm(slen,sd=0.3),pos,circular=TRUE))