Description Usage Arguments Details Value Author(s) Examples
Set prior on expressed splicing variants using the genome annotation
contained in a knownGenome
object.
The prior probability of variants V1,...,Vn being expressed depends on n, on the number of exons in each variant V1,...,Vn and the number of exons in the gene. See the details section.
1 | modelPrior(genomeDB, maxExons=40, smooth=TRUE, verbose=TRUE)
|
genomeDB |
Object of class |
maxExons |
The prior distribution is estimated for genes with 1
up to |
smooth |
If set to |
verbose |
Set to |
The goal is to set a prior that takes into account the number of annotated variants for genes with E exons, as well as the number of exons in each variant.
Suppose we have a gene with E exons. Let V_1,...,V_n be n variants of interest and let |V_1|,...,|V_n| be the corresponding number of exons in each variant. The prior probability of variants V_1,...,V_n being expressed is modeled as
P(V_1,...,V_n|E)= P(n|E) P(|V_1| |E) ... P(|V_n| |E)
where P(n|E)= NegBinom(n; k_E, r_E) I(0 < n < 2^E) and P(|V_i| |E)= BetaBinomial(|V_i|-1; E-1, alpha_E, beta_E).
The parameters k_E, r_E, alpha_E, beta_E depend on E (the number of exons
in the gene) and are estimated from the available annotation via
maximum likelihood.
Parameters are estimated jointly for all genes with E>=
maxExons
in order to improve the precision.
For smooth==TRUE
, alpha_E and beta_E are modeled as a smooth
function of E by calling gam
and setting the smoothing
parameter via cross-validation. Estimates for genes with E>=10 are
substituted by their smooth versions, which typically helps improve
stability in the estimates.
List with 2 components.
nvarPrior |
List with prior distribution on the number of expressed variants for genes with 1,2,3... exons. Each element contains the truncated Negative Binomial parameters, observed and predicted frequencies (counting the number of genes with a given number of variants). |
nexonPrior |
List with prior distribution on the number of exons in a variant for genes with 1,2,3... exons. Each element contains the Beta-Binomial parameters, observed and predicted frequencies (counting the number of variants with a given number of exons) |
David Rossell, Camille Stephan-Otto Attolini
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(hg19DB)
mprior <- modelPrior(hg19DB, maxExons=10)
##Prior on number of expressed variants
##Genes with 2 exons
##mprior$nvarPrior[['2']]
##Genes with 3 exons
##mprior$nvarPrior[['3']]
##Prior on the number of exons in an expressed variant
##Genes with 2 exons
##mprior$nexonPrior[['2']]
##Genes with 3 exons
##mprior$nexonPrior[['3']]
|
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