Package: EBSeq 2.5.0

Xiuyu Ma

EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data

Differential Expression analysis at both gene and isoform level using RNA-seq data

Authors:Xiuyu Ma [cre, aut], Ning Leng [aut], Christina Kendziorski [ctb], Michael A. Newton [ctb]

EBSeq_2.5.0.tar.gz
EBSeq_2.5.0.zip(r-4.5)EBSeq_2.5.0.zip(r-4.4)EBSeq_2.5.0.zip(r-4.3)
EBSeq_2.5.0.tgz(r-4.4-x86_64)EBSeq_2.5.0.tgz(r-4.4-arm64)EBSeq_2.5.0.tgz(r-4.3-x86_64)EBSeq_2.5.0.tgz(r-4.3-arm64)
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EBSeq.pdf |EBSeq.html✨
EBSeq/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • GeneMat - The simulated data for two condition gene DE analysis
  • IsoList - The simulated data for two condition isoform DE analysis
  • IsoMultiList - The simulated data for multiple condition isoform DE analysis
  • MultiGeneMat - The simulated data for multiple condition gene DE analysis

On BioConductor:EBSeq-2.5.0(bioc 3.21)EBSeq-2.4.0(bioc 3.20)

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

immunooncologystatisticalmethoddifferentialexpressionmultiplecomparisonrnaseqsequencing

7.46 score 6 packages 159 scripts 680 downloads 261 mentions 28 exports 42 dependencies

Last updated 23 days agofrom:4664faa0eb. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-win-x86_64WARNINGOct 31 2024
R-4.5-linux-x86_64WARNINGOct 31 2024
R-4.4-win-x86_64WARNINGOct 31 2024
R-4.4-mac-x86_64WARNINGOct 31 2024
R-4.4-mac-aarch64WARNINGOct 31 2024
R-4.3-win-x86_64WARNINGOct 31 2024
R-4.3-mac-x86_64WARNINGOct 31 2024
R-4.3-mac-aarch64WARNINGOct 31 2024

Exports:beta.momcrit_funDenNHistEBMultiTestEBSeqTestEBTestf0f1GetDEResultsGetMultiFCGetMultiPPGetNgGetNormalizedMatGetPatternsGetPPMatGetSelectedPatternsLikefunLikefunMultiLogNLogNMultiMedianNormPlotPatternPlotPostVsRawFCPolyFitPlotPostFCQQPQuantileNormRankNorm

Dependencies:BHbitopsblockmodelingbriocallrcaToolsclicrayondescdiffobjdigestevaluatefansifsgluegplotsgtoolsjsonliteKernSmoothlatticelifecyclemagrittrMatrixpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RcppRcppEigenrematch2rlangrprojroottestthattibbleutf8vctrswaldowithr

EBSeq Vignette

Rendered fromEBSeq_Vignette.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2023-06-16
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
EBSeq: RNA-Seq Differential Expression Analysis on both gene and isoform levelEBSeq_NingLeng-package EBSeq_NingLeng
Fit the beta distribution by method of momentsbeta.mom
Calculate the soft threshold for a target FDRcrit_fun
Density plot to compare the empirical q's and the simulated q's from the fitted beta distribution.DenNHist
Using EM algorithm to calculate the posterior probabilities of interested patterns in a multiple condition studyEBMultiTest
EBSeq coreEBSeqTest
Using EM algorithm to calculate the posterior probabilities of being DEEBTest
The Prior Predictive Distribution of being EEf0
The Prior Predictive Distribution of being DEf1
The simulated data for two condition gene DE analysisGeneMat
Obtain Differential Expression Analysis Results in a Two-condition TestGetDEResults
Calculate the Fold Changes for Multiple ConditionsGetMultiFC
Posterior Probability of Each TranscriptGetMultiPP
Ng VectorGetNg
Calculate normalized expression matrixGetNormalizedMat
Generate all possible patterns in a multiple condition studyGetPatterns
Posterior Probability of TranscriptsGetPPMat
Get selected patterns in a multiple condition studyGetSelectedPatterns
The simulated data for two condition isoform DE analysisIsoList
The simulated data for multiple condition isoform DE analysisIsoMultiList
Likelihood Function of the NB-Beta ModelLikefun
Likelihood Function of the NB-Beta Model In Multiple Condition TestLikefunMulti
The function to run EM (one round) algorithm for the NB-beta model.LogN
EM algorithm for the NB-beta model in the multiple condition testLogNMulti
Median NormalizationMedianNorm
The simulated data for multiple condition gene DE analysisMultiGeneMat
Visualize the patternsPlotPattern
Plot Posterior FC vs FCPlotPostVsRawFC
Fit the mean-var relationship using polynomial regressionPolyFitPlot
Calculate the posterior fold change for each transcript across conditionsPostFC
The Quantile-Quantile Plot to compare the empirical q's and simulated q's from fitted beta distributionQQP
Quantile NormalizationQuantileNorm
Rank NormalizationRankNorm