Description Usage Arguments Details Value Author(s) References See Also Examples
Generate simulated ChIP-chip data, as described in Reference (1).
1 2 3 4 |
posns |
Probe centers |
n.pts |
Number of (randomly-chosen) binding sites to add |
noise |
Level of noise (as described in Reference (1)) |
min.pk |
Minimum peak intensity |
plot |
Plot the generated data? |
verbose |
Be verbose? |
in.pts |
2-column matrix with positions (column 1) and intensities (column 2) of input peaks; if n.pts is 'NA'. |
kernel |
Input peak profile to use. |
tile.size |
Input probe length (used to generate peak profile kernel if 'kernel' is 'NULL') |
noise.func |
Function used to generate noise as a function of signal intensity |
reps |
Number of replicate intensities per simulated probe |
... |
Further parameters for 'generate.binding.profile' (if 'kernel' is 'NULL') |
No details.
A list of class containing the following three elements:
input |
A two-column matrix containing positions and intensities of input binding sites |
data |
A two-column matrix containing positions and intensities of simulated probes, can be passed directly to 'chip.deconv' |
kernel |
The peak profile kernel used to generate the data (as produced by 'generate.binding.profile') |
David J Reiss, Institute for Systems Biology
Maintainer: <dreiss@systemsbiology.org>
Reiss, DJ and Facciotti, MT and Baliga, NS. (2007). "Model-based
deconvolution of genome-wide DNA binding",
Bioinformatics; doi: 10.1093/bioinformatics/btm592.
http://baliga.systemsbiology.net/medichi
chip.deconv, generate.binding.profile
1 2 3 4 5 6 7 8 9 10 11 | ## Generate data with 2 peaks at positions 3000 and 4000, deconvolve the
## data, and plot the resulting data and fit.
kern.300 <- generate.binding.profile( fragment=function(x) dgamma( x,
shape=6, scale=50 ), verbose=TRUE )
data <- generate.fake.data( in.pts=cbind( c( 3000, 4000 ), c( 1, 0.7 ) ), reps=3,
kernel=kern.300 )
plot( data$data, pch=20 )
fit <- chip.deconv( data$data, center=NA, wind=NA, kernel=data$kernel,
fit.res=30, n.boot=10, verbose=TRUE, boot.sample.opt="case" )
plot( fit, boot="prob.scaled" )
print( fit )
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