Background: Background

In CNVPanelizer: Reliable CNV detection in targeted sequencing applications

Description

Makes use of a subsampling approach to estimate the background noise when sequencing a gene with a specific number of amplicons. The 95 percent confidence interval is returned for each unique number of amplicons in the experiment.

Usage

Background(geneNames,
           samplesNormalizedReadCounts,
           referenceNormalizedReadCounts,
           bootList,
           replicates = 1000,
           significanceLevel = 0.05,
           robust = FALSE)

Arguments

geneNames	A vector of gene names, with one entry for each sequenced amplicon.
samplesNormalizedReadCounts	A matrix with the normalized read counts of the samples of interest
referenceNormalizedReadCounts	A matrix with the normalized reference read counts
bootList	A list as returned by BootList
replicates	an integer number of how many replicates should be performed
significanceLevel	The significance level for the calculated confidence interval
robust	If set to true the confidence interval is calculated replacing mean with median and sd with mad.

Value

Returns a list of data frames. One data frame for each sample of interest. The data frames report the 95 percent confidence interval of the background noise for each number of amplicons and sample combination.

Author(s)

Thomas Wolf, Cristiano Oliveira

Examples

data(sampleReadCounts)
data(referenceReadCounts)
## Gene names should be same size as row columns
geneNames <- row.names(referenceReadCounts)

ampliconNames <- NULL

normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts,
                                                 referenceReadCounts,
                                                 ampliconNames = ampliconNames)

# After normalization data sets need to be splitted again to perform bootstrap
samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]

#Values above 10000 should be used
replicates <- 10

# Perform the bootstrap based analysis
bootList <- BootList(geneNames,
                     samplesNormalizedReadCounts,
                     referenceNormalizedReadCounts,
                     replicates = replicates)

background <- Background(geneNames,
                        samplesNormalizedReadCounts,
                        referenceNormalizedReadCounts,
                        bootList,
                        replicates = replicates,
                        significanceLevel = 0.1)

CNVPanelizer documentation built on Nov. 8, 2020, 6:47 p.m.