| estParamHyperChIP {MAnorm2} | R Documentation |
The Parameter Estimation Framework of HyperChIP
Description
Given a bioCond object with which a mean-variance curve has
been associated, estParamHyperChIP estimates the related parameters
(i.e., the number of prior degrees of freedom and the variance ratio factor)
by following the framework designed in HyperChIP.
Usage
estParamHyperChIP(
cond,
occupy.only = TRUE,
prob = 0.1,
subset = NULL,
p_low = 0.01,
p_up = 0.1,
return.d0 = FALSE,
.call = TRUE,
...
)
Arguments
cond |
A |
occupy.only |
A logical scalar. If it is |
prob |
The proportion of the intervals with the lowest (observed) mean signal intensities that shall be used in the subsequent Winsorization procedure (see "Details"). |
subset |
Alternatively, you can set this argument to a logical vector
to directly specify the intervals to be used in the Winsorization
procedure. This option overrides |
p_low, p_up |
Lower- and upper-proportions of extreme values to be Winsorized (see "References"). Each of them must be strictly larger than 0, and their sum must be strictly smaller than 1. |
return.d0 |
A logical scalar. If set to |
.call |
Never care about this argument. |
... |
Further arguments to be passed to
|
Details
Technically, estParamHyperChIP first derives a lower quantile of the
observed mean signal intensities in different genomic intervals based on the
prob argument. It then selects the intervals whose mean intensities
are less than or equal to the quantile. Finally, it applies the
Winsorization technique to the selected intervals to finish the parameter
estimation (see also "References"), by using the
estimatePriorDfRobust function as the underlying engine.
estParamHyperChIP is primarily designed for coordinating with
varTestBioCond to call hypervariable and lowly variable
intervals across samples. See "Examples" for the workflow of a
standard HyperChIP analysis.
Value
By default, estParamHyperChIP returns the argument
bioCond object, whose
"df.prior" and "ratio.var" components
have been updated. If return.d0 is set to TRUE,
it simply returns the estimated number of prior degrees of freedom.
References
Tukey, J.W., The future of data analysis. The annals of mathematical statistics, 1962. 33(1): p. 1-67.
Phipson, B., et al., Robust Hyperparameter Estimation Protects against Hypervariable Genes and Improves Power to Detect Differential Expression. Annals of Applied Statistics, 2016. 10(2): p. 946-963.
Chen, H., et al., HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples. Genome Biol, 2022. 23(1): p. 62.
See Also
bioCond for creating a bioCond object;
fitMeanVarCurve for fitting a mean-variance curve and
using a fit.info field to characterize it;
estimatePriorDfRobust for estimating the number of
prior degrees of freedom and adjusting the variance ratio factors of
a set of bioConds in a robust manner;
varTestBioCond
for calling hypervariable and invariant intervals across ChIP-seq
samples contained in a bioCond.
Examples
data(H3K27Ac, package = "MAnorm2")
attr(H3K27Ac, "metaInfo")
## Treat all the samples as independent and perform a HyperChIP analysis.
# Use a pseudo-reference profile as baseline in the MA normalization
# process.
autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY"))
norm <- normalize(H3K27Ac, 4:8, 9:13, baseline = "pseudo-reference",
common.peak.regions = autosome)
plot(attr(norm, "MA.cor"), symbreaks = TRUE, margins = c(8, 8))
# Construct a bioCond.
cond <- bioCond(norm[4:8], norm[9:13], occupy.num = 1,
name = "all")
# Fit a mean-variance curve by using local regression.
cond <- fitMeanVarCurve(list(cond), method = "local",
occupy.only = TRUE, args.lp = list(nn = 1))[[1]]
summary(cond)
# Apply the parameter estimation framework of HyperChIP.
cond <- estParamHyperChIP(cond)
summary(cond)
# Perform statistical tests and visualize the results.
res <- varTestBioCond(cond)
head(res)
hist(res$pval, breaks = 100, col = "red")
plot(res)