cleanCounts: Remove effects from covariates of interest

In plbaldoni/epigraHMM: Epigenomic R-based analysis with hidden Markov models

Remove effects from covariates of interest

Description

This function removes the effect from covariates of interest (such as GC content) from experimental counts

Usage

cleanCounts(object, effectNames, byNames = NULL, log = TRUE)

Arguments

object	an epigraHMMDataSet
effectNames	a character vector with the names of assays for which the effect will be removed from the experimental counts. Names in 'effectNames' must be assays stored in the epigraHMMDataSet 'object'.
byNames	a character vector with the name of an assay containing stratification variables which will be used to define stratum-specific effects. Examples of byNames assays include the 'peaks' assay from 'initializer()'. In this case, models will be fit separately for peaks and non-peaks regions. This can be useful for effects such as GC content, which are known to have a differential effect between peaks and non-peak regions. Default is NULL, i.e., effects will be removed without stratification.
log	a logical indicating if the effect from 'effectNames' should be log-transformed in the regression model (default is TRUE)

Value

An epigraHMMDataSet with an 'offset' assay filled in.

Author(s)

Pedro L. Baldoni, [e-mail]

References

https://github.com/plbaldoni/epigraHMM

Examples

# Creating dummy object
gc <- rbeta(3e3,50,50)

countData <- list('counts' = rbind(matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1),
                                   matrix(rnbinom(3e3,mu = exp(0.5 + 8*gc),size = 5),ncol = 1),
                                   matrix(rnbinom(2e3,mu = 7.5,size = 10),ncol = 1)),
                  'gc' = matrix(c(rbeta(2e3,50,50),gc,rbeta(2e3,50,50)),ncol = 1))

colData <- data.frame(condition = 'A', replicate = 1)
object <- epigraHMMDataSetFromMatrix(countData,colData)

# Initializing
object <- initializer(object = object,controlEM())

# Cleaning counts
object <- cleanCounts(object = object,effectNames = 'gc',byNames = 'peaks')

# Plotting the cleaned data
#par(mfrow = c(2,1))
#smoothScatter(log1p(assay(object))~assay(object,'gc'),xlab = 'gc',ylab = 'log counts')
#smoothScatter(as.numeric(log(assay(object)+1) - assay(object,'offsets'))~assay(object,'gc'),
#              xlab = 'gc',ylab = 'log cleaned counts')

plbaldoni/epigraHMM documentation built on Oct. 15, 2023, 7:53 p.m.