Package: MBASED 1.47.0

Oleg Mayba

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

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.

Authors:Oleg Mayba, Houston Gilbert

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MBASED/json (API)

# Install 'MBASED' in R:

install.packages('MBASED', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

On BioConductor:MBASED-1.47.0(bioc 3.24)MBASED-1.46.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

sequencing geneexpression transcription

4.48 score 15 scripts 568 downloads 18 mentions 1 exports 27 dependencies

Last updated from:99d308db7a. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	177
linux-devel	NOTE	307
source / vignettes	OK	359
linux-release	NOTE	284
macos-release	NOTE	249
macos-oldrel	NOTE	183
windows-devel	NOTE	223
windows-release	NOTE	212
windows-oldrel	NOTE	216
wasm-release	OK	145

Exports:runMBASED

Dependencies:abind BH Biobase BiocGenerics BiocParallel codetools cpp11 DelayedArray formatR futile.logger futile.options generics GenomicRanges IRanges lambda.r lattice Matrix MatrixGenerics matrixStats RUnit S4Arrays S4Vectors Seqinfo snow SparseArray SummarizedExperiment XVector

MBASED

1 MBASED algorithm | 2 Caveat Emptor | 3 Examples | 4 Session Info

Rendered fromMBASED.Rnwusingutils::Sweave

Last update: 2015-05-14
Started: 2014-08-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
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.	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
Freeman-Tukey transformation functions.	FT FTAdjust isCountMajorFT unFT
Functions to convert between shape parameters a and b for beta distribution and parameters mu (mean) and rho (dispersion).	getAB getMuRho
Function that adjusts true underlying allele frequency for pre-existing allelic bias to produce actual generating probability of observing allele-supporting read	getPFinal
Function to calculate simulations-based p-values	getSimulationPvalue
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.	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 maximum likelihood estimate of the 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
MBASED	MBASED-package MBASED
Generic function to perform standard meta analysis.	MBASEDMetaAnalysis
Helper function to obtain estimate of underlying mean and the standard error of the estimate in meta analysis framework.	MBASEDMetaAnalysisGetMeansAndSEs
Vectorized wrapper around metaprop() function from R package "meta" with some modifications and extensions to beta-binomial count models.	MBASEDVectorizedMetaprop
Vectorized wrapper around a test for difference of 2 proportions.	MBASEDVectorizedPropDiffTest
Main function that implements MBASED.	runMBASED
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.	runMBASED1s
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.	runMBASED1s1aseID
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.	runMBASED2s
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.	runMBASED2s1aseID
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).	shiftAndAttenuateProportions
Function that checks to see if the difference between 2 number is small enough.	testNumericDiff
Function to test quantile equality for theoretical and observed distributions	testQuantiles
Functions to generate beta-binomial random variables.	vectorizedRbetabinomAB vectorizedRbetabinomMR