bayes: Model allele-specific copy numbers with specified cellularity...
In sequenza: Copy Number Estimation from Tumor Genome Sequencing Data

Description Usage Arguments Details Value See Also Examples

Given a pair of cellularity and ploidy parameters, the function returns the most likely allele-specific copy numbers with the corresponding log-posterior probability of the fit, for given values of B-allele frequency and depth ratio.

  baf.bayes(Bf, depth.ratio, cellularity, ploidy, avg.depth.ratio,
            sd.Bf = 0.1, sd.ratio = 0.5, weight.Bf = 1, weight.ratio = 1,
            CNt.min = 0, CNt.max = 7, CNn = 2,
            priors.table = data.frame(CN = CNt.min:CNt.max, value = 1),
            ratio.priority = FALSE)
  mufreq.bayes(mufreq, depth.ratio, cellularity, ploidy, avg.depth.ratio,
            weight.mufreq = 100, weight.ratio = 100, CNt.min = 1, CNt.max = 7, CNn = 2,
            priors.table = data.frame(CN = CNt.min:CNt.max, value = 1))

`Bf`	vector of B-allele frequencies (values can range from 0 to 0.5).
`mufreq`	vector of mutation frequencies (values can range from 0 to 1).
`depth.ratio`	vector of depth ratios.
`sd.ratio`	standard deviation observed in the depth ratio measures in a segment
`sd.Bf`	standard deviation observed in the B-allele frequency measures in a segment
`weight.Bf`	vector of weights for B-allele frequency values.
`weight.mufreq`	vector of weights for the mutation frequency values.
`weight.ratio`	vector of weights for the depth ratio values.
`cellularity`	fraction of tumor cells in the sample.
`ploidy`	2 * ratio between total DNA content in a tumor cell and a normal cell.
`avg.depth.ratio`	average normalized depth ratio.
`CNt.min`	minimum copy number to consider in the model.
`CNt.max`	maximum copy number to consider in the model.
`CNn`	copy number of the normal genome.
`priors.table`	data frame with columns `CN` and `value`, containing the copy numbers and the corresponding weights. To every copy number is assigned the value 1 as default, so any values different from 1 will change the corresponding weight.
`ratio.priority`	logical, if TRUE only the depth ratio will be used to determine the copy number state, while the Bf value will be used to determine the number of B-alleles.

baf.bayes and mufreq.bayes use a naive Bayesian approach to calculate the posterior probability of fitness of the data point with the model point resulting from the given values of cellularity and DNA-content.

`CNt`	copy number of the tumor cell at the tested point.
`A`	number of A-alleles at the tested point.
`B`	number of B-alleles at the tested point.
`CNn`	copy number of the normal cell at the tested point (equal to CNn given as argument).
`Mt`	number of mutated alleles at the tested point.
`LPP`	log-posterior probability of model fitting at the given point/segment.

baf.model.fit, mufreq.model.fit.

  ## Not run: 
data.file <-  system.file("extdata", "example.seqz.txt.gz", package = "sequenza")
# read all the chromosomes:
seqz.data  <- read.seqz(data.file)
# Gather genome wide GC-stats from raw file:
gc.stats <- gc.sample.stats(data.file)
gc.vect  <- setNames(gc.stats$raw.mean, gc.stats$gc.values)
# Read only one chromosome:
seqz.data  <- read.seqz(data.file, chr.name = 1)

# Correct the coverage of the loaded chromosome:
seqz.data$adjusted.ratio <- seqz.data$depth.ratio /
                           gc.vect[as.character(seqz.data$GC.percent)]
# Select the heterozygous positions
seqz.hom  <- seqz.data$zygosity.normal == 'hom'
seqz.het  <- seqz.data[!seqz.hom, ]
# Detect breakpoints
breaks <- find.breaks(seqz.het, gamma = 80, kmin = 10, baf.thres = c(0, 0.5))
# use heterozygous and homozygous position to measure segment values
seg.s1 <- segment.breaks(seqz.data, breaks = breaks)

# filter out small ambiguous segments, and conveniently weight the segments by size:
seg.filtered <- seg.s1[(seg.s1$end.pos - seg.s1$start.pos) > 10e6, ]
weights.seg  <- 150 + round((seg.filtered$end.pos -
                             seg.filtered$start.pos) / 1e6, 0)
# get the genome wide mean of the normalized depth ratio:
avg.depth.ratio <- mean(gc.stats$adj[,2])
# run the BAF model fit

CP <- baf.model.fit(Bf = seg.filtered$Bf, depth.ratio = seg.filtered$depth.ratio,
                    weight.ratio = weights.seg,
                    weight.Bf = weights.seg,
                    avg.depth.ratio = avg.depth.ratio,
                    cellularity = seq(0.1,1,0.01),
                    ploidy = seq(0.5,3,0.05))

confint <- get.ci(CP)
ploidy   <- confint$max.ploidy
cellularity <- confint$max.cellularity

#detect copy number alteration on the segments:

cn.alleles <- baf.bayes(Bf = seg.s1$Bf, depth.ratio = seg.s1$depth.ratio,
                        cellularity = cellularity, ploidy = ploidy,
                        avg.depth.ratio = 1)

head(cbind(seg.s1, cn.alleles))

# create mutation table:
mut.tab   <- mutation.table(seqz.data, mufreq.treshold = 0.15,
                            min.reads = 40, max.mut.types = 1,
                            min.type.freq = 0.9, segments = seg.s1)

mut.tab.clean <- na.exclude(mut.tab)

# Detect mutated alleles:
mut.alleles <- mufreq.bayes(mufreq = mut.tab.clean$F,
                            depth.ratio = mut.tab.clean$adjusted.ratio,
                            cellularity = cellularity, ploidy = ploidy,
                            avg.depth.ratio = avg.depth.ratio)
head(cbind(mut.tab.clean[,c("chromosome","position","F",
                            "adjusted.ratio", "mutation")],
           mut.alleles))


   
## End(Not run)