| setPriorDf {MAnorm2} | R Documentation |
Set the Number of Prior Degrees of Freedom of Mean-Variance Curves
Description
Given a set of bioCond objects of which each has been
associated with a mean-variance curve, setPriorDf assigns a
common number of prior degrees of freedom to all the bioConds
and accordingly adjusts their variance ratio factors.
Usage
setPriorDf(conds, d0, occupy.only = TRUE, no.rep.rv = NULL, .call = TRUE)
Arguments
conds |
A list of |
d0 |
A non-negative real specifying the number of prior degrees of
freedom. |
occupy.only |
A logical scalar. If it is |
no.rep.rv |
A positive real specifying the variance ratio factor of
those |
.call |
Never care about this argument. |
Details
The specific behavior of this function is pretty much the same as
estimatePriorDf, except that
the number of prior degrees of freedom is
directly specified by users rather than estimated based on the observed
data. Refer to estimatePriorDf for more information.
Note also that there is a robust version of this function that uses
Winsorized statistics to derive variance ratio factors (see
setPriorDfRobust for details).
Value
setPriorDf returns the argument list of
bioCond objects, with the specified
number of prior degrees of
freedom substituted for the "df.prior" component of each of them.
Besides, their "ratio.var" components have been adjusted
accordingly, and an attribute named "no.rep.rv" is added to the
list if it's ever been used as the variance ratio factor of the
bioConds without replicate samples.
To be noted, if the specified number of prior degrees of freedom is 0,
setPriorDf won't adjust existing variance ratio factors.
In this case, you may want to use setPriorDfVarRatio to
explicitly specify variance ratio factors.
See Also
bioCond for creating a bioCond object;
fitMeanVarCurve for fitting a mean-variance curve and
using a fit.info field to characterize it;
estimatePriorDf for estimating the number of prior
degrees of freedom and adjusting the variance ratio factors of a set of
bioConds;
setPriorDfRobust for a robust version of
setPriorDf;
diffTest for calling
differential intervals between two bioCond objects.
Examples
data(H3K27Ac, package = "MAnorm2")
attr(H3K27Ac, "metaInfo")
## Fit a mean-variance curve for the GM12892 cell line (i.e., individual)
## and set the number of prior degrees of freedom of the curve to Inf.
# Perform the MA normalization and construct a bioCond to represent GM12892.
norm <- normalize(H3K27Ac, 7:8, 12:13)
GM12892 <- bioCond(norm[7:8], norm[12:13], name = "GM12892")
# Variations in ChIP-seq signals across biological replicates of a cell line
# are generally of a low level, and typically their relationship with the
# mean signal intensities could be well modeled by the presumed parametric
# form.
GM12892 <- fitMeanVarCurve(list(GM12892), method = "parametric",
occupy.only = TRUE, init.coef = c(0.1, 10))[[1]]
# In the vast majority of cases for modeling biological replicates of cell
# lines, the associated variance structure is so regular that variances of
# individual genomic intervals could be reliably estimated by fully
# depending on the mean-variance curve.
GM12892_2 <- setPriorDf(list(GM12892), Inf, occupy.only = TRUE)[[1]]
# The resulting model makes few differences from the original one, though.
# This is because MAnorm2 will adaptively deduce a large number of prior
# degrees of freedom for the mean-variance curve if the underlying variance
# structure is of high regularity. In practice, we recommend leaving the
# specification of prior df to the estimation method implemented in MAnorm2
# all the time.
summary(GM12892)
summary(GM12892_2)