xoi (version 0.72)

est.coi.um: Estimate the coincidence as a function of micron distance

Description

Estimate the coincidence as a function of micron distance, with data on XO locations in microns plus SC length in microns.

Usage

est.coi.um(
  xoloc,
  sclength,
  centromeres = NULL,
  group = NULL,
  intwindow = 0.05,
  coiwindow = NULL,
  intloc = NULL,
  coiloc = NULL
)

Value

A list containing the estimated coincidence (as a matrix with two columns, micron distance and corresponding estimated coincidence) and the estimated intensity functions (as a matrix with length(group)+1 columns (the locations at which the intensity functions were estimated followed by the group-specific estimates).

Arguments

xoloc

list of crossover locations (in microns) for each of several oocytes or spermatocytes.

sclength

vector of SC lengths (in microns).

centromeres

vector of centromere locations (in microns). If NULL, taken to be sclength/2.

group

nominal vector of groups; the intensity function of the crossover process will be estimated separately for each group, but a joint coincidence function will be estimated.

intwindow

Window size used to smooth the estimated intensity function.

coiwindow

Window size used to smooth the estimated coincidence function.

intloc

Locations at which to estimate the intensity function, in the interval [0,1]

coiloc

Values at which the coincidence function is to be estimated, in microns, less than max(sclength)

Author

Karl W Broman,

Details

The coincidence function is the probability of a recombination event in both of two intervals, divided by the product of the two intensity function for the two intervals.

We estimate this as a function of the distance between the two intervals in microns, taking account of varying SC lengths,.

See Also

gammacoi(), stahlcoi(), kfunc(), est.coi()

Examples

# simple example using data simulated with no crossover interference
ncells <- 1000
L <- 2                      # chr lengths in Morgans (constant here)
nchi <- rpois(ncells, 2*L)  # number of chiasmata
xoloc <- lapply(nchi, function(a) runif(a, 0, L)) # chi locations
coi <- est.coi.um(xoloc, rep(L, ncells))

# plot estimated coincidence and intensity
#    (intensity is after scaling chromosome to length 1)
par(mfrow=c(2,1), las=1)
plot(coi$coincidence, type="l", lwd=2, ylim=c(0, max(coi$coincidence[,2])))
plot(coi$intensity, type="l", lwd=2, ylim=c(0, max(coi$intensity[,2])))