calcSdc {RPPanalyzer} | R Documentation |
Calculates the concentration of serial diluted samples
Description
Calculates the protein concentration of a serial diluted sample stored in an RPPA data list using the serial dilution curve algorithm published by Zhang et.al, Bioinformatics 2009.
Usage
calcSdc(x,sample.id=c("sample","sample.n"),
sel=c("measurement","control"), dilution="dilution",
D0=2,sensible.min=5, sensible.max=1.e9,minimal.err=5,
plot=T, r=1.2)
Arguments
x |
RPPA data list with replicates aggregated with median |
sample.id |
Attributes to identify the samples |
sel |
The sample type that should be calculated. Has to be "measurements","control", "neg_control",or "blank". |
dilution |
Name of the column in the feature data matrix describing the dilution steps of the samples. |
D0 |
Dilution factor. |
sensible.min |
Signals below this value are marked as undetected |
sensible.max |
Signals above the value are marked as saturated |
minimal.err |
Minimal valid estimate for the background noise |
plot |
Logical. If true, model fits are plotted |
r |
Constant factor used to determine the confidence interval for the saturation limit $M$ and the background noise $a$, shoul be $>1$. Can be lower if accuracy of signals is improved. |
Details
The method of Zhang et. al doesn't fit the dose response curve but a derive model describing the functional relationship between the signals of two consecutive dilution steps. Since this new model does not contain the protein concentration anymore all spots of one array can be used for the fit, allowing a much more robust estimation of the underlying paramters.
Value
expression |
matrix with expression values |
error |
matrix with error values |
arraydescription |
data frame with feature data |
sampledescription |
data frame with pheno data |
Author(s)
Heiko Mannsperger <h.mannsperger@dkfz.de>, Stephan Gade <s.gade@dkfz.de>
References
Zhang et. al, Bioinformatics 2009,Serial dilution curve: a new method for analysis of reverse phase protein array data
Examples
## Not run:
library(RPPanalyzer)
data(ser.dil.samples)
ser.dil_median <- sample.median(ser.dil.samples)
predicted.data <- calcSdc(ser.dil_median,D0=2,sel=c("measurement"), dilution="dilution")
## End(Not run)