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puma (version 3.12.0)

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

Description

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.

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Version

Version

3.12.0

License

LGPL

Maintainer

Xuejun Liu

Last Published

February 15th, 2017

Functions in puma (3.12.0)

calculateLimma

Calculate differential expression between conditions using limma
calcAUC

Calculate Area Under Curve (AUC) for a standard ROC plot.
Clust.exampleStd

The example data of the standard deviation for gene expression levels
bcomb

Combining replicates for each condition
clusterNormE

Zero-centered normalisation
calculateTtest

Calculate differential expression between conditions using T-test
calculateFC

Calculate differential expression between conditions using FC
Clust.exampleE

The example data of the mean gene expression levels
clusterApplyLBDots

clusterApplyLB with dots to indicate progress
clusterNormVar

Adjusting expression variance for zero-centered normalisation
erfc

The complementary error function
compareLimmapumaDE

Compare pumaDE with a default Limma model
Clustii.exampleE

The example data of the mean gene expression levels
create_eset_r

Create an ExpressionSet from a PPLR matrix
createContrastMatrix

Automatically create a contrast matrix from an ExpressionSet and optional design matrix
Clustii.exampleStd

The example data of the standard deviation for gene expression levels
createDesignMatrix

Automatically create a design matrix from an ExpressionSet
DEResult

Class DEResult
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
exprReslt-class

Class exprReslt
numOfFactorsToUse

Determine number of factors to use from an ExpressionSet
pplrUnsorted

Return an unsorted matrix of PPLR values
puma-package

puma - Propagating Uncertainty in Microarray Analysis
numTP

Number of True Positives for a given proportion of False Positives.
justmgMOS

Compute mgmos Directly from CEL Files
igmoExon

Separately Compute gene and transcript expression values and standard deviatons from exon CEL Files by the conditions.
numFP

Number of False Positives for a given proportion of True Positives.
normalisation.gs

Global scaling normalisation
pumaClust

Propagate probe-level uncertainty in model-based clustering on gene expression data
pumaClustii

Propagate probe-level uncertainty in robust t mixture clustering on replicated gene expression data
pumaFull

Perform a full PUMA analysis
pumaNormalize

Normalize an ExpressionSet
justmmgMOS

Compute mmgmos Directly from CEL Files
plotErrorBars

Plot mean expression levels and error bars for one or more probesets
legend2

A legend which allows longer lines
pumaDE

Calculate differential expression between conditions
plotHistTwoClasses

Stacked histogram plot of two different classes
pumaDEUnsorted

Return an unsorted matrix of PPLR values
gmhta

Compute gene and transcript expression values and standard deviatons from hta2.0 CEL Files
gmoExon

Compute gene and transcript expression values and standard deviatons from exon CEL Files
mgmos

modified gamma Model for Oligonucleotide Signal
mmgmos

Multi-chip modified gamma Model for Oligonucleotide Signal
plotROC

Receiver Operator Characteristic (ROC) plot
license.puma

Print puma license
plotWhiskers

Standard errors whiskers plot
PMmmgmos

Multi-chip modified gamma Model for Oligonucleotide Signal using only PM probe intensities
matrixDistance

Calculate distance between two matrices
pplr

Probability of positive log-ratio
pumaPCARes-class

Class pumaPCARes
pumaPCAModel-class

Class pumaPCAModel
pumaPCAExpectations-class

Class pumaPCAExpectations
pumaPCA

PUMA Principal Components Analysis
hcomb

Combining replicates for each condition with the true gene expression
removeUninformativeFactors

Remove uninformative factors from the phenotype data of an ExpressionSet
hgu95aphis

Estimated parameters of the distribution of phi
plot-methods

Plot method for pumaPCARes objects
orig_pplr

Probability of positive log-ratio
pumaComb

Combining replicates for each condition
pumaCombImproved

Combining replicates for each condition with the true gene expression