View source: R/calc_genoprob.R
calc_genoprob | R Documentation |
Uses a hidden Markov model to calculate the probabilities of the true underlying genotypes given the observed multipoint marker data, with possible allowance for genotyping errors.
calc_genoprob(
cross,
map = NULL,
error_prob = 0.0001,
map_function = c("haldane", "kosambi", "c-f", "morgan"),
lowmem = FALSE,
quiet = TRUE,
cores = 1
)
cross |
Object of class |
map |
Genetic map of markers. May include pseudomarker
locations (that is, locations that are not within the marker
genotype data). If NULL, the genetic map in |
error_prob |
Assumed genotyping error probability |
map_function |
Character string indicating the map function to use to convert genetic distances to recombination fractions. |
lowmem |
If |
quiet |
If |
cores |
Number of CPU cores to use, for parallel calculations.
(If |
Let O_k
denote the observed marker genotype at position
k
, and g_k
denote the corresponding true underlying
genotype.
We use the forward-backward equations to calculate
\alpha_{kv} = \log Pr(O_1, \ldots, O_k, g_k = v)
and
\beta_{kv} = \log Pr(O_{k+1}, \ldots, O_n | g_k = v)
We then obtain
Pr(g_k | O_1, \ldots, O_n) = \exp(\alpha_{kv} + \beta_{kv}) / s
where
s = \sum_v \exp(\alpha_{kv} + \beta_{kv})
An object of class "calc_genoprob"
: a list of three-dimensional arrays of probabilities,
individuals x genotypes x positions. (Note that the arrangement is
different from R/qtl.) Also contains four attributes:
crosstype
- The cross type of the input cross
.
is_x_chr
- Logical vector indicating whether chromosomes
are to be treated as the X chromosome or not, from input cross
.
alleles
- Vector of allele codes, from input
cross
.
alleleprobs
- Logical value (FALSE
) that
indicates whether the probabilities are compressed to allele
probabilities, as from genoprob_to_alleleprob()
.
insert_pseudomarkers()
grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
gmap_w_pmar <- insert_pseudomarkers(grav2$gmap, step=1)
probs <- calc_genoprob(grav2, gmap_w_pmar, error_prob=0.002)
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