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Get consensus segments across clusters

Source: R/cnaCalling.R
getSegConsensus.Rd

This function processes segments data, in which clusters have different segment breakpoints, to identify consensus segments across all clusters. It groups breakpoints within a specified genomic distance (dist.breakpoints) and calculates representative breakpoints for each group based on cluster size and median coordinates.

Usage

getSegConsensus(x, ncells, dist.breakpoints = 1e+06)

Arguments

x

A data frame containing cluster segments information with the following required columns:

  • chrom: Chromosome name (factor or character).

  • start: Start position of the segment (numeric).

  • end: End position of the segment (numeric).

  • cluster: Cluster identifier for each breakpoint (numeric).

ncells

A named vector specifying the number of cells per cluster (numeric vector). The names should correspond to the cluster identifiers in the cluster column of x.

dist.breakpoints

A numeric value specifying the minimum genomic distance between adjacent breakpoints to be grouped into the same consensus segment (numeric value). Default: 1e6.

Value

A data frame containing consensus segments with the following columns:

  • chrom: Chromosome name.

  • start: Start position of the consensus segment.

  • end: End position of the consensus segment.

See also

cnaCalling()

Examples

# Example data frame
segs <- data.frame(
  chrom = c("chr1", "chr1", "chr2", "chr2"),
  start = c(1.2e6, 1.1e6, 3.1e6, 3.2e6),
  end = c(2.5e6, 2.6e6, 5.5e6, 5.7e6),
  cluster = c("1", "2", "1", "2")
)

# Generate consensus segments
consensus_segs <- getSegConsensus(segs,
                                  ncells = c("1" = 50, "2" = 30),
                                  dist.breakpoints = 1e6)
print(consensus_segs)
#>   chrom   start     end
#> 1  chr1 1200000 2500000
#> 2  chr2 3100000 5500000