getExpInterval: Identify the interval of relative substitution frequencies...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/getExpInterval.R

Description

Identifies the interval/support of relative substitution frequencies (RSFs) dominated by the second model component, i.e. by the probability of being induced by the experimental procedure. In addition, this function can be used to generate diagnostic plots of the model fit, representing (i) model densities and log odds ratio (ii) the posterior class probability, i.e. the probability of a given observation being generated by experimental induction.

Usage

1
getExpInterval(model, bayes = TRUE, leftProb, rightProb, plot = TRUE)

Arguments

model

A list containing the model as returned by the function fitMixtureModel

bayes

Logical, if TRUE the Bayes classifier (cutoff at posterior class probabilities >= 0.5) is applied. If FALSE, custom cutoff values should be provided through leftProb and rightProb. Default is TRUE.

leftProb

Numeric, the posterior probability corresponding to the left boundary (start) of the high confidence RSF interval.

rightProb

Numeric, the posterior probability corresponding to the right boundary (end) of the high confidence RSF interval.

plot

Logical, if TRUE diagnostics plot showing the model components, log odds and the computed posterior with highlighted identified RSF interval are returned.

Value

A list with two numeric slots, corresponding to the extremes of the RSF interval (RSF support).

supportStart

start of the high confidence RSF interval

supportEnd

end of the high confidence RSF interval

Author(s)

Federico Comoglio and Cem Sievers

References

Sievers C, Schlumpf T, Sawarkar R, Comoglio F and Paro R. (2012) Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data, Nucleic Acids Res. 40(20):e160. doi: 10.1093/nar/gks697

Comoglio F, Sievers C and Paro R (2015) Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data, BMC Bioinformatics 16, 32.

See Also

fitMixtureModel getHighConfSub estimateFDR

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
data( model )

#default
support <- getExpInterval( model = model, bayes = TRUE, plot = TRUE )
support

#custom interval (based, e.g. on visual inspection of posterior class probability 
# or evaluation of FDR using the estimateFDRF function)
support <- getExpInterval( model = model, leftProb = 0.2, rightProb = 0.7, plot = TRUE )
support

Bioconductor-mirror/wavClusteR documentation built on June 1, 2017, 8:10 p.m.