Package: NBPSeq 0.3.5

NBPSeq: Negative Binomial Models for RNA-Sequencing Data

Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data.

Authors:Yanming Di <[email protected]>, Daniel W Schafer <[email protected]>, with contributions from Jason S Cumbie <[email protected]> and Jeff H Chang <[email protected]>.

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NBPSeq/json (API)

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

Peer review:

Bug tracker:https://github.com/diystat/nbpseq/issues

Datasets:
  • arab - Arabidopsis RNA-Seq Data Set

On CRAN:

86 exports 1 stars 4.21 score 0 dependencies 3 dependents 32 mentions 15 scripts 402 downloads

Last updated 10 years agofrom:358f095f48. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 09 2024
R-4.5-win-x86_64WARNINGSep 09 2024
R-4.5-linux-x86_64WARNINGSep 09 2024
R-4.4-win-x86_64WARNINGSep 09 2024
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Exports:[.nb.data[.nb.glmbinxyCdqrlscompute.tail.probdisp.by.groupdisp.fun.nb2disp.fun.nbpdisp.fun.nbqdisp.fun.nbsdisp.fun.stepdisp.nbpdisp.nbqdisp.nbsdisp.predictor.mudisp.predictor.pidisp.predictor.rsdisp.stepestimate.dispestimate.dispersionestimate.dispersion.by.groupestimate.norm.factorsestimate.norm.factors.AH2010exact.nb.testexample.his2d.oldexample.hist2dexample.smart.plotexample.tablefilter.mu.prefit.nb.glmfit.nb.glm.1fit.nb.glm.1uget.mean.hatget.nbp.parsget.rel.meanget.var.hathist2dhoa.1dhoa.hdirls.nbirls.nb.1l.nbll.nblog.phi.nb2log.phi.nbplog.phi.nbqma.plotmake.dispmv.linemv.line.fittedmv.line.nbpmv.plotmv.pointsnb.glm.testnbp.testnll.log.phi.funoptim.disp.aploptim.disp.ploptim.pclphi.linephi.line.fittedphi.line.nbpphi.plotplot.nb.dataplot.nb.dispersionplot.nbpplot.qvalueprepare.nb.dataprepare.nbpprint.nb.dataprint.nb.dispersionprint.nb.testprint.nbpqplotqsummaryqvalueqvalue.guiqwritesmart.plotsmart.plot.newsmart.plot.oldsmart.pointssummary.qvaluetest.coefficienttest.compute.tail.probthin.counts

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Negative Binomial Regression Models for Statistical Analysis of RNA-Sequencing DataNBPSeq-package NBPSeq
hello[.nb.data
Arabidopsis RNA-Seq Data Setarab
(private) Compute the tail probability of a conditional distribution involving a pair of Negative Binomial (NB) random variables given their sumcompute.tail.prob
Specify a dispersion model where the parameters of the model will be estimated separately for different groupsdisp.by.group
(private) Specify a NBP dispersion modeldisp.nbp
(private) Specify a NBQ dispersion modeldisp.nbq
(private) Specify a NBS dispersion modeldisp.nbs
Dispersion precitordisp.predictor.mu
(private) Specify a piecewise constant dispersion modeldisp.step
(private) Specify a NB2, NBP, NBS, NBS, or STEP dispersion modeldisp.fun.nb2 disp.fun.nbp disp.fun.nbq disp.fun.nbs disp.fun.step Dispersion Models
Fit a parametric disperison model to thinned countsestimate.disp
Estimate Negative Binomial Dispersionestimate.dispersion
Estiamte Normalization Factorsestimate.norm.factors
Exact Negative Binomial Test for Differential Gene Expressionexact.nb.test
Create a logical vector specifyfing the subset of rows to be used when estimating the dispersion modelfilter.mu.pre
Fit a single negative binomial (NB) log-linear regression model with known dispersion paramretersfit.nb.glm.1
Fit a single negative binomial (NB) log-linear regression model with a common unknown dispersion paramreterfit.nb.glm.1u
(private) Extract row means of the pseudo counts for the specified group from an 'nbp' object.get.mean.hat
(private) Retrieve nbp parameters for one of the treatment groups from an nbp objectget.nbp.pars
(private) Extract row relative means of the pseudo counts for the specified group from an 'nbp' object.get.rel.mean
(private) Extract estimated variance from the oupput of 'nbp-mcle' or 'nbp-test'get.var.hat
2-d Histogramhist2d
(private) One-dimensional HOA test for a regression coefficient in an NB GLM modelhoa.1d
(private) HOA test for regression coefficients in an NBP GLM modelhoa.hd
(private) Estiamte the regression coefficients in an NB GLM modelirls.nb
Estimate the regression coefficients in an NB GLM modelirls.nb.1
(private) The Log Likelihood of a NB Modell.nb
(private) A NBP dispersion modellog.phi.nb2
(private) A NBP dispersion modellog.phi.nbp
(private) A NBQ dispersion modellog.phi.nbq
(private) MA plot with differently expressed genes highlightedma.plot
(private) Specify a dispersion modelmake.disp
(private) Overlay an estimated mean-variance linemv.line
(private) Overlay an estimated mean-variance linemv.line.fitted
(private) Overlay a NBP mean-variance line on an existing plotmv.line.nbp
(private) Mean-variance plotmv.plot
(private) Highlight a subset of points on the mean-variance plotmv.points
Fit Negative Binomial Regression Model and Test for a Regression Coefficientnb.glm.test
NBP Test for Differential Gene Expression from RNA-Seq Countsnbp.test
Negative profile conditional likelihood of the dispersion modelnll.log.phi.fun
(private) Estimate the parameters in a dispersion modeloptim.disp.apl
(private) Estimate the parameters in a dispersion modeloptim.disp.pl
(private) Overlay an mean-dispersion line on an esimtated plotphi.line
(private) Overlay an estimated mean-dispersion line on an existing plotphi.line.fitted
(private) Overlay an estimated mean-dispersion line on an existing plotphi.line.nbp
Plot estimated genewise NB2 dispersion parameter versus estimated meanphi.plot
Boxplot and scatterplot matrix of relative frequencies (after normalization)plot.nb.data
Plot the estimated dispersion as a function of the preliminarily estimated mean relative frequenciesplot.nb.dispersion
Diagnostic Plots for an NBP Objectplot.nbp
Prepare the NB Data Structure for RNA-Seq Read Countsprepare.nb.data
Prepare the Data Structure for Exact NB test for Two-Group Comparisonprepare.nbp
Print summary of the nb countsprint.nb.data
Print the estimated dispersion modelprint.nb.dispersion
Print output from 'test.coefficient'print.nb.test
Print summary of an NBP Objectprint.nbp
(private) An alternative to plot.default() for plotting a large number of densely distributed points.smart.plot.new
(private) An alternative to plot.default() for plotting a large number of densely distributed points.smart.plot.old
(private) An alternative to point.default() for plotting a large number of densely distributed points.smart.points
Large-sample Test for a Regression Coefficient in an Negative Binomial Regression Modeltest.coefficient
(private) Thin (downsample) counts to make the effective library sizes equal.thin.counts