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

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Documentation for package ‘MBASED’ version 1.42.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

MBASED-package	MBASED
estimateMAF1s	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.
estimateMAF2s	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.
FT	Freeman-Tukey transformation functions.
FTAdjust	Freeman-Tukey transformation functions.
getAB	Functions to convert between shape parameters a and b for beta distribution and parameters mu (mean) and rho (dispersion).
getMuRho	Functions to convert between shape parameters a and b for beta distribution and parameters mu (mean) and rho (dispersion).
getPFinal	Function that adjusts true underlying allele frequency for pre-existing allelic bias to produce actual generating probability of observing allele-supporting read
getSimulationPvalue	Function to calculate simulations-based p-values
isCountMajorFT	Freeman-Tukey transformation functions.
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.
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.
maxLogLikelihoodCalculator1s	Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.
maxLogLikelihoodCalculator2s	Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.
MBASED	MBASED
MBASEDMetaAnalysis	Generic function to perform standard meta analysis.
MBASEDMetaAnalysisGetMeansAndSEs	Helper function to obtain estimate of underlying mean and the standard error of the estimate in meta analysis framework.
MBASEDVectorizedMetaprop	Vectorized wrapper around metaprop() function from R package "meta" with some modifications and extensions to beta-binomial count models.
MBASEDVectorizedPropDiffTest	Vectorized wrapper around a test for difference of 2 proportions.
runMBASED	Main function that implements MBASED.
runMBASED1s	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.
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.
runMBASED2s	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.
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.
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).
testNumericDiff	Function that checks to see if the difference between 2 number is small enough.
testQuantiles	Function to test quantile equality for theoretical and observed distributions
unFT	Freeman-Tukey transformation functions.
vectorizedRbetabinomAB	Functions to generate beta-binomial random variables.
vectorizedRbetabinomMR	Functions to generate beta-binomial random variables.