sequenza (version 1.0.5)

mutation.table: Extract mutations on homozygous position from an ABfreq file.

Description

mutation.table extracts positions from an ABfreq file that differ from the reference genome.

Usage

mutation.table(abf.tab, mufreq.treshold = 0.15, min.reads = 40,
               max.mut.types = 3, min.type.freq = 0.9, segments = NULL)

Arguments

abf.tab

an ABfreq table, as output from read.abfreq.

mufreq.treshold

mutation frequency threshold.

min.reads

minimal number of reads above the quality threshold to accept the mutation call.

max.mut.types

maximum number of different base substitutions per position. Integer from 1 to 3 (since there are only 4 different bases). Default is 3, to accept "noisy" mutation calls.

min.type.freq

minimal frequency of aberrant types.

segments

if specified, the values of depth ratio would be taken from the segments rather than from the raw data.

Value

• A data frame, which in addition to some of the columns of the ABfreq table, contains the following two columns:
• F
  the mutation frequency
• mutation
  a character representation of the mutation. For example, a mutation from A in the germline to G in the tumor is annotated as "A>G".

Details

Calling mutations in impure tumor samples is a difficult task, because the degree of contamination by normal cells affects the measured mutation frequency. In highly impure samples, where the normal cells comprise the major component of the sample, mutations can be so diluted that it will even be difficult to distinguish them from sequencing errors.

The function mutation.table tries to separate true mutations from sequencing errors, based on the given threshold. In samples with low contamination, it should even be possible to catch sub-clonal mutations using this function.

Examples

data.file <-  system.file("data", "abf.data.abfreq.txt.gz", package = "sequenza")
abf.data  <- read.abfreq(data.file)
# Detect how many reads passed the quality treshold

# Normalize coverage by GC-content
gc.stats <- gc.norm(x = abf.data$depth.ratio,
                    gc = abf.data$GC.percent)
gc.vect  <- setNames(gc.stats$raw.mean, gc.stats$gc.values)
abf.data$adjusted.ratio <- abf.data$depth.ratio /
                           gc.vect[as.character(abf.data$GC.percent)]
# Subset mutations, apply mutation frequency treshold.
mut.tab   <- mutation.table(abf.data, mufreq.treshold = 0.15,
                            min.reads = 40, max.mut.types = 1,
                            min.type.freq = 0.9)
mut.tab <- na.exclude(mut.tab)