Package: baySeq 2.41.0

Samuel Granjeaud

baySeq: Empirical Bayesian analysis of patterns of differential expression in count data

This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

Authors:Thomas J. Hardcastle [aut], Samuel Granjeaud [cre]

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baySeq.pdf |baySeq.html✨
baySeq/json (API)

# Install 'baySeq' in R:
install.packages('baySeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/samgg/bayseq/issues

Datasets:
  • CDPost - 'countData' object derived from data file 'simData' with estimated likelihoods of differential expression.
  • CDPriors - 'countData' object derived from data file 'simData' with estimated priors.
  • mobAnnotation - Annotation data for a set of small RNA loci derived from sequencing of grafts of Arabidopsis thaliana intended for differential expression analyses.
  • mobData - Data from a set of small RNA sequencing experiments carried out on grafts of Arabidopsis thaliana intended for differential expression analyses.
  • pairData - Simulated data for testing the baySeq package methods for paired data
  • simData - Simulated data for testing the baySeq package methods
  • zimData - Simulated data for testing the baySeq package methods

On BioConductor:baySeq-2.41.0(bioc 3.21)baySeq-2.40.0(bioc 3.20)

sequencingdifferentialexpressionmultiplecomparisonsagebayesiancoverage

7.75 score 3 packages 79 scripts 882 downloads 139 mentions 41 exports 24 dependencies

Last updated 2 months agofrom:cf527bc236. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 03 2024
R-4.5-winOKDec 03 2024
R-4.5-linuxOKDec 03 2024
R-4.4-winOKDec 03 2024
R-4.4-macOKDec 03 2024
R-4.3-winOKDec 03 2024
R-4.3-macOKDec 03 2024

Exports:allModelsbbDensitybbNCDistbimodalSeparatordensityFunctiondensityFunction<-densityFunctionsflattengetLibsizesgetLikelihoodsgetLikelihoods.BBgetLikelihoods.NBgetPosteriorsgetPriorsgetPriors.NBgetTPsgroupsgroups<-libsizeslibsizes<-makeOrderingsmarginaliseEqualmarginalisePairwisemd2Densitymd3DensitymdDensitymethObservablesnbinomDensitynormDensityplotMA.CDplotNullPriorplotPosteriorsplotPriorsreplicatesreplicates<-seglensseglens<-selectTopsummarisePosteriorstopCountsZINBDensity

Dependencies:abindaskpassBiocGenericscurledgeRgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesjsonlitelatticelimmalocfitmimeopensslR6S4VectorsstatmodsysUCSC.utilsXVectorzlibbioc

Advanced baySeq analyses

Rendered frombaySeq_generic.Rnwusingutils::Sweaveon Dec 03 2024.

Last update: 2023-06-26
Started: 2014-10-08

baySeq

Rendered frombaySeq.Rnwusingutils::Sweaveon Dec 03 2024.

Last update: 2024-10-26
Started: 2013-10-23

Readme and manuals

Help Manual

Help pageTopics
Empirical Bayesian analysis of patterns of differential expression in count data.baySeq-package baySeq
Function to generate all possible models for a countData object based on the replicate data.allModels
baySeq - classesbaySeq-class baySeq-classes c,countData-method countData countData-class densityFunction densityFunction,countData-method densityFunction<- densityFunction<-,countData-method dim,countData-method flatten flatten,countData-method groups groups,countData-method groups<- groups<-,countData-method libsizes libsizes,countData-method libsizes<- libsizes<-,countData-method rbind rbind,countData-method replicates replicates,countData-method replicates<- replicates<-,countData-method seglens seglens,countData-method seglens<- seglens<-,countData-method show,countData-method [,countData,ANY-method [,countData-method
A function that, given a numeric vector, finds the value which splits the data into two sets of minimal total variance using Otsu's method.bimodalSeparator
`countData' object derived from data file `simData' with estimated likelihoods of differential expression.CDPost
`countData' object derived from data file `simData' with estimated priors.CDPriors
Class '"densityFunction"'densityFunction-class
Lists all currently available densityFunctions.bbDensity bbNCDist densityFunctions md2Density md3Density mdDensity nbinomDensity normDensity ZINBDensity
Estimates library scaling factors (library sizes) for count data.getLibsizes
Finds posterior likelihoods for each count or paired count as belonging to some model.getLikelihoods getLikelihoods.BB getLikelihoods.NB
An internal function in the baySeq package for calculating posterior likelihoods given likelihoods of the data.getPosteriors
Estimates prior parameters for the underlying distributions of 'count' data.getPriors getPriors.NB
Gets the number of true positives in the top n counts selected by ranked posterior likelihoodsgetTPs
Construct orderings for count data given a model structure and an ordering function.makeOrderings
Computes marginal likelihoods that two replicate groups are equal.marginaliseEqual
Computes marginal likelihoods that two replicate groups differ, in a paticular direction.marginalisePairwise
Generation of intermediate values in likelihood estimation for methylation data.methObservables
Annotation data for a set of small RNA loci derived from sequencing of grafts of Arabidopsis thaliana intended for differential expression analyses.mobAnnotation
Data from a set of small RNA sequencing experiments carried out on grafts of Arabidopsis thaliana intended for differential expression analyses.mobData
Simulated data for testing the baySeq package methods for paired datapairData
'MA'-plot for count data.plotMA.CD
Plots distribution of null function and shows the threshold separator.plotNullPrior
Plots the posterior likelihoods estimated for a 'countData' object against the log-ratios observed between two sets of sample data.plotPosteriors
Plots the density of the log values estimated for the mean rate in the prior data for the Negative Binomial approach to detecting differential expressionplotPriors
Selects the top genomic events, based on posterior likelihoods, from a `countData' object.selectTop
Simulated data for testing the baySeq package methodssimData
Summarises expected number of genomic events given the calculated posterior likelihoods of a countData object.summarisePosteriors
Get the top counts corresponding to some group from a 'countData' objecttopCounts
Simulated data for testing the baySeq package methodszimData