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MBASED (version 1.6.0)

Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection

Description

The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE.

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Version

Version

1.6.0

License

Artistic-2.0

Maintainer

Oleg Mayba

Last Published

February 15th, 2017

Functions in MBASED (1.6.0)

Function that given observed count data returns a maximum likelihood estimate of the underlying haplotype frequency. Both situations where the haplotype are known and unknown are handled. In the latter case, likelihood is further maximized over all possible assignments of alleles to haplotypes.

MBASEDVectorizedPropDiffTest

Vectorized wrapper around a test for difference of 2 proportions.

MBASEDMetaAnalysis

Generic function to perform standard meta analysis.

logLikelihoodCalculator1s

Function that given observed count data along a known haplotype returns a function that can calculate the likelihood of observing that data for a supplied underlying haplotype frequency.

shiftAndAttenuateProportions

Helper function to adjust proportions for pre-existing allelic bias and also to obtain estimate of proportion variance based on attenuated read counts (adding pseudocount of 0.5 to each allele in each sample).

runMBASED2s1aseID

Function that runs between-sample (differential) ASE calling using data from loci (SNVs) within a single unit of ASE (gene). The i-th entry of each of vector arguments 'lociAllele1CountsSample1', 'lociAllele2CountsSample1', 'lociAllele1NoASEProbsSample1', 'lociRhosSample1', 'lociAllele1CountsSample2', 'lociAllele2CountsSample2', 'lociAllele1NoASEProbsSample2', and 'lociRhosSample2' should correspond to the i-th locus. If argument 'isPhased' (see below) is true, then entries corresponding to allele1 at each locus must represent the same haplotype. Note: for each locus in each sample, at least one allele should have >0 supporting reads.

Functions to convert between shape parameters a and b for beta distribution and parameters mu (mean) and rho (dispersion).

maxLogLikelihoodCalculator2s

Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.

MBASEDVectorizedMetaprop

Vectorized wrapper around metaprop() function from R package "meta" with some modifications and extensions to beta-binomial count models.

Function that given observed count data returns a maximum likelihood estimate of the underlying haplotype frequency. Both situations where the haplotype are known and unknown are handled. In the latter case, likelihood is further maximized over all possible assignments of alleles to haplotypes.

vectorizedRbetabinomAB

Functions to generate beta-binomial random variables.

Main function that implements MBASED.

Function to test quantile equality for theoretical and observed distributions

Freeman-Tukey transformation functions.

Function that runs between-sample (differential) ASE calling using data from individual loci (SNVs) within units of ASE (genes). Vector arguments 'lociAllele1CountsSample1', 'lociAllele2CountsSample1', 'lociAllele1NoASEProbsSample1', 'lociRhosSample1', 'lociAllele1CountsSample2', 'lociAllele2CountsSample2', 'lociAllele1NoASEProbsSample2', 'lociRhosSample2', and 'aseIDs' should all be of the same length. Letting i1, i2, .., iN denote the indices corresponding to entries within aseIDs equal to a given aseID, the entries at those indices in the other vector arguments provide information for the loci within that aseID for the respective samples. This information is then used by runMBASED2s1aseID. It is assumed that for any i, the i-th entries of all vector arguments correspond to the same locus, and that the entries corresponding to allele1 in sample1 and sample2 provide information on the same allele. If argument 'isPhased' (see below) is true, then entries corresponding to allele1 at each locus must represent the same haplotype.

Function that runs single-sample ASE calling using data from individual loci (SNVs) within units of ASE (genes). Vector arguments 'lociAllele1Counts', 'lociAllele2Counts', 'lociAllele1NoASEProbs', 'lociRhos', and 'aseIDs' should all be of the same length. Letting i1, i2, .., iN denote the indices corresponding to entries within aseIDs equal to a given aseID, the entries at those indices in the other vector arguments provide information for the loci within that aseID. This information is then used by runMBASED1s1aseID. It is assumed that for any i, the i-th entries of all vector arguments correspond to the same locus. If argument 'isPhased' (see below) is true, then entries corresponding to allele1 at each locus must represent the same haplotype.

Function that adjusts true underlying allele frequency for pre-existing allelic bias to produce actual generating probability of observing allele-supporting read

runMBASED1s1aseID

Function that runs single-sample ASE calling using data from loci (SNVs) within a single unit of ASE (gene). The i-th entry of each of vector arguments 'lociAllele1Counts', 'lociAllele2Counts', 'lociAllele1NoASEProbs', 'lociRhos' should correspond to the i-th locus. If argument 'isPhased' (see below) is true, then entries corresponding to allele1 at each locus must represent the same haplotype. Note: for each locus, at least one allele should have >0 supporting reads.

logLikelihoodCalculator2s

Function that given observed count data along a known haplotype returns a function that can calculate the likelihood of observing that data for a supplied underlying haplotype frequency.

MBASEDMetaAnalysisGetMeansAndSEs

Helper function to obtain estimate of underlying mean and the standard error of the estimate in meta analysis framework.

testNumericDiff

Function that checks to see if the difference between 2 number is small enough.

maxLogLikelihoodCalculator1s

Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.

getSimulationPvalue

Function to calculate simulations-based p-values