Package: puma 3.47.0

Xuejun Liu

puma: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0)

Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions.

Authors:Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang

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puma.pdf |puma.html✨
puma/json (API)

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

Peer review:

Datasets:
  • Clust.exampleE - The example data of the mean gene expression levels
  • Clust.exampleStd - The example data of the standard deviation for gene expression levels
  • Clustii.exampleE - The example data of the mean gene expression levels
  • Clustii.exampleStd - The example data of the standard deviation for gene expression levels
  • eset_mmgmos - An example ExpressionSet created from the Dilution data with mmgmos
  • exampleE - The example data of the mean gene expression levels
  • exampleStd - The example data of the standard deviation for gene expression levels
  • hgu95aphis - Estimated parameters of the distribution of phi

On BioConductor:puma-3.47.0(bioc 3.20)puma-3.46.0(bioc 3.19)

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

bioconductor-package

86 exports 3.06 score 57 dependencies 14 mentions

Last updated 2 months agofrom:e7a258c70d

Exports:bcombcalcAUCcalculateFCcalculateLimmacalculateTtestclusterApplyLBDotsclusterNormEclusterNormVarcompareLimmapumaDEcreate_eset_rcreateContrastMatrixcreateDesignMatrixDEMethodDEMethod<-erfcFCFC<-gmhtagmoExonhcombigmoExonjust.mgmosjust.mmgmosjustmgMOSjustmmgMOSlegend2license.pumamatrixDistancemgmosmmgmosnewtonStepnormalisation.gsnumberOfContrastsnumberOfGenesnumberOfProbesetsnumFPnumOfFactorsToUsenumTPorig_pplrpLikeValuesplot.pumaPCAResplotErrorBarsplotHistTwoClassesplotROCplotWhiskersPMmmgmospplrpplrUnsortedprcfiftyprcfifty<-prcfiveprcfive<-prcninfiveprcninfive<-prcsevfiveprcsevfive<-prctwfiveprctwfive<-pumaClustpumaClustiipumaCombpumaCombImprovedpumaDEpumaDEUnsortedpumaFullpumaNormalizepumaPCApumaPCAEsteppumaPCALikelihoodBoundpumaPCALikelihoodCheckpumaPCANewtonUpdateLogSigmapumaPCARemoveRedundancypumaPCASigmaGradientpumaPCASigmaObjectivepumaPCAUpdateCinvpumaPCAUpdateMpumaPCAUpdateMupumaPCAUpdateWremoveUninformativeFactorsstatisticstatistic<-statisticDescriptionstatisticDescription<-topGeneIDstopGeneswrite.reslts

Dependencies:abindaffxparseraffyaffyioaskpassBiobaseBiocGenericsBiocManagerBiostringsbitbit64blobcachemclicodetoolscpp11crayoncurlDBIDelayedArrayfastmapffforeachGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrIRangesiteratorsjsonlitelatticelifecycleMatrixMatrixGenericsmatrixStatsmclustmemoisemimeoligooligoClassesopensslpkgconfigplogrpreprocessCoreR6rlangRSQLiteS4ArraysS4VectorsSparseArraySummarizedExperimentsysUCSC.utilsvctrsXVectorzlibbioc

puma User Guide

Rendered frompuma.Rnwusingutils::Sweaveon Jul 05 2024.

Last update: 2020-02-28
Started: 2015-07-17

Readme and manuals

Help Manual

Help pageTopics
puma - Propagating Uncertainty in Microarray Analysispuma-package puma
Combining replicates for each conditionbcomb
Calculate Area Under Curve (AUC) for a standard ROC plot.calcAUC
Calculate differential expression between conditions using FCcalculateFC
Calculate differential expression between conditions using limmacalculateLimma
Calculate differential expression between conditions using T-testcalculateTtest
The example data of the mean gene expression levelsClust.exampleE
The example data of the standard deviation for gene expression levelsClust.exampleStd
clusterApplyLB with dots to indicate progressclusterApplyLBDots
Zero-centered normalisationclusterNormE
Adjusting expression variance for zero-centered normalisationclusterNormVar
The example data of the mean gene expression levelsClustii.exampleE
The example data of the standard deviation for gene expression levelsClustii.exampleStd
Compare pumaDE with a default Limma modelcompareLimmapumaDE
Create an ExpressionSet from a PPLR matrixcreate_eset_r
Automatically create a contrast matrix from an ExpressionSet and optional design matrixcreateContrastMatrix
Automatically create a design matrix from an ExpressionSetcreateDesignMatrix
Class DEResultclass:DEResult DEMethod DEMethod,DEResult-method DEMethod<- DEMethod<-,DEResult,character-method DEResult DEResult-class FC FC,DEResult-method FC<- FC<-,DEResult,matrix-method numberOfContrasts numberOfContrasts,DEResult-method numberOfGenes numberOfGenes,DEResult-method numberOfProbesets numberOfProbesets,DEResult-method pLikeValues pLikeValues,DEResult-method show,DEResult-method statistic statistic,DEResult-method statistic<- statistic<-,DEResult,matrix-method statisticDescription statisticDescription,DEResult-method statisticDescription<- statisticDescription<-,DEResult,character-method topGeneIDs topGeneIDs,DEResult-method topGenes topGenes,DEResult-method write.reslts,DEResult-method
The complementary error functionerfc
An example ExpressionSet created from the Dilution data with mmgmoseset_mmgmos
The example data of the mean gene expression levelsexampleE
The example data of the standard deviation for gene expression levelsexampleStd
Class exprResltclass:exprReslt exprReslt exprReslt-class prcfifty prcfifty,exprReslt-method prcfifty<- prcfifty<-,exprReslt-method prcfive prcfive,exprReslt-method prcfive<- prcfive<-,exprReslt-method prcninfive prcninfive,exprReslt-method prcninfive<- prcninfive<-,exprReslt-method prcsevfive prcsevfive,exprReslt-method prcsevfive<- prcsevfive<-,exprReslt-method prctwfive prctwfive,exprReslt-method prctwfive<- prctwfive<-,exprReslt-method se.exprs se.exprs,exprReslt-method se.exprs<- se.exprs<-,exprReslt-method show,exprReslt-method write.reslts write.reslts,ExpressionSet-method write.reslts,exprReslt-method
Compute gene and transcript expression values and standard deviatons from hta2.0 CEL Filesgmhta
Compute gene and transcript expression values and standard deviatons from exon CEL FilesgmoExon
Combining replicates for each condition with the true gene expressionhcomb
Estimated parameters of the distribution of phihgu95aphis
Separately Compute gene and transcript expression values and standard deviatons from exon CEL Files by the conditions.igmoExon
Compute mgmos Directly from CEL Filesjust.mgmos justmgMOS
Compute mmgmos Directly from CEL Filesjust.mmgmos justmmgMOS
A legend which allows longer lineslegend2
Print puma licenselicense.puma
Calculate distance between two matricesmatrixDistance
modified gamma Model for Oligonucleotide Signalmgmos
Multi-chip modified gamma Model for Oligonucleotide Signalmmgmos
Global scaling normalisationnormalisation.gs
Number of False Positives for a given proportion of True Positives.numFP
Determine number of factors to use from an ExpressionSetnumOfFactorsToUse
Number of True Positives for a given proportion of False Positives.numTP
Probability of positive log-ratioorig_pplr
Plot method for pumaPCARes objectsplot,pumaPCARes,missing-method plot,pumaPCARes-method plot-methods plot.pumaPCARes
Plot mean expression levels and error bars for one or more probesetsplotErrorBars
Stacked histogram plot of two different classesplotHistTwoClasses
Receiver Operator Characteristic (ROC) plotplotROC
Standard errors whiskers plotplotWhiskers
Multi-chip modified gamma Model for Oligonucleotide Signal using only PM probe intensitiesPMmmgmos
Probability of positive log-ratiopplr
Return an unsorted matrix of PPLR valuespplrUnsorted
Propagate probe-level uncertainty in model-based clustering on gene expression datapumaClust
Propagate probe-level uncertainty in robust t mixture clustering on replicated gene expression datapumaClustii
Combining replicates for each conditionpumaComb
Combining replicates for each condition with the true gene expressionpumaCombImproved
Calculate differential expression between conditionspumaDE
Return an unsorted matrix of PPLR valuespumaDEUnsorted
Perform a full PUMA analysispumaFull
Normalize an ExpressionSetpumaNormalize
PUMA Principal Components AnalysisnewtonStep pumaPCA pumaPCAEstep pumaPCALikelihoodBound pumaPCALikelihoodCheck pumaPCANewtonUpdateLogSigma pumaPCARemoveRedundancy pumaPCASigmaGradient pumaPCASigmaObjective pumaPCAUpdateCinv pumaPCAUpdateM pumaPCAUpdateMu pumaPCAUpdateW
Class pumaPCAExpectationspumaPCAExpectations pumaPCAExpectations-class
Class pumaPCAModelpumaPCAModel pumaPCAModel-class
Class pumaPCAResclass:pumaPCARes pumaPCARes pumaPCARes-class write.reslts,pumaPCARes-method
Remove uninformative factors from the phenotype data of an ExpressionSetremoveUninformativeFactors