extract the CN change between adjacent segments.

Change points are the change in copy number state between adjacent segments. If one segment is 4 and the adjacent segment is 1, the change point is 3.

Usage

extract_changepoint(
  segs_df,
  first_seg_correction = c("ignore", "cn_mode", "diploid"),
  sample_col = "cell_id",
  chrom_col = "chr",
  cn_col = "state",
  return = c("values", "counts"),
  ...
)

Arguments

segs_df

dataframe. Sample copy number segments.

first_seg_correction

string. Default: 'ignore'. Options include "diploid" or "cn_mode".

sample_col

string. Name of column with cell/sample names

chrom_col

string. Name of column with chromosome names

cn_col

string. Name of column with segment copy number states.

return

string. "values" (default) or "counts". Values are the observed values for cells, counts are the counts of these values in pre-determined categories.

...

can pass arguments to segs_to_reads

Value

dataframe. Sample IDs and the observed breakpoint counts per scope.

Details

Change points are based on the difference to the "left" adjacent segment, when moving from BP 1 to the end of a chromosome. So if there are 3 segments: 4 - 1 - 2, the change points would be: |1 - 4| and |2 - 1| resulting in: 3, 1

For the first segment on a chromosome, Drews et al. compared it to a hypothetical diploid. So if the first segment on a chromosome is 5, the change point would be 5 - 2 = 3. That's fine if the base genome is diploid, but doesn't work so well for other ploidies, or cases where you don't want to assume a diploid base case.

first_seg_correction provides options over what to do. "diploid" for Drews solution, "cn_mode" to compare to sample ploidy estimate based on mode, or "ignore" to not count anything for first segments.

Predefined categories for summarizing counts are:

Changepoint = 1, 2, 3, 4, 5+