rna_majiq.DeltaPsi

class rna_majiq.DeltaPsi(psi1, psi2, prior, psibins=40, min_experiments_f=0.5, name1='psi1', name2='psi2')

Compute DeltaPsi between two groups of PsiCoverage (replicate assumption)

Compute DeltaPsi between two groups of PsiCoverage under assumption that the experiments in each group are replicates with the given DPsiPrior.

Parameters:

psi1, psi2 (rna_majiq.PsiCoverage) – PsiCoverage files to be treated as replicates
prior (rna_majiq.DPsiPrior) – Prior on deltapsi for inference
min_experiments_f (float) – Only pass events that pass this many experiments in both groups
name1, name2 (str) – Names to use for the aggregated groups coverage/PSI

__init__(psi1, psi2, prior, psibins=40, min_experiments_f=0.5, name1='psi1', name2='psi2')

Methods

`__init__`(psi1, psi2, prior[, psibins, ...])
`bootstrap_logposterior`([ec_idx, nchunks])	Logposterior average of deltapsi inference over each bootstrap replicate
`dataset`([nchunks])	Reduce to `DeltaPsiDataset` used for VOILA visualization
`rebin`(psibins)	Get DeltaPsi with different bins
`smooth_logposterior`([ec_idx, nchunks])	Infer deltapsi logposterior on approximation of psi bootstrapped mixture

Attributes

`bootstrap_posterior`	`DeltaPsiPMF` for average bootstrapped dpsi posteriors
`discrete_logprior`
`discrete_prior`	`DeltaPsiPMF` for discretized deltapsi prior
`name1`
`name2`
`num_connections`
`passed`
`smooth_posterior`	`DeltaPsiPMF` for dpsi posterior from approximated PSI posteriors