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puma (version 3.14.0)

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

3.14.0

License

LGPL

Maintainer

Xuejun Liu

Last Published

February 15th, 2017

Functions in puma (3.14.0)

Combining replicates for each condition

Clustii.exampleE

The example data of the mean gene expression levels

Estimated parameters of the distribution of phi

normalisation.gs

Global scaling normalisation

Adjusting expression variance for zero-centered normalisation

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

An example ExpressionSet created from the Dilution data with mmgmos

PUMA Principal Components Analysis

pumaPCARes-class

Class pumaPCARes

Clustii.exampleStd

The example data of the standard deviation for gene expression levels

Plot method for pumaPCARes objects

puma - Propagating Uncertainty in Microarray Analysis

The example data of the mean gene expression levels

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

The example data of the standard deviation for gene expression levels

Perform a full PUMA analysis

Zero-centered normalisation

Return an unsorted matrix of PPLR values

pumaPCAExpectations-class

Class pumaPCAExpectations

The complementary error function

numOfFactorsToUse

Determine number of factors to use from an ExpressionSet

Calculate differential expression between conditions using limma

Calculate differential expression between conditions

pumaCombImproved

Combining replicates for each condition with the true gene expression

createContrastMatrix

Automatically create a contrast matrix from an ExpressionSet and optional design matrix

Standard errors whiskers plot

Combining replicates for each condition with the true gene expression

Compute mgmos Directly from CEL Files

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

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

Normalize an ExpressionSet

Create an ExpressionSet from a PPLR matrix

Calculate differential expression between conditions using T-test

Receiver Operator Characteristic (ROC) plot

Return an unsorted matrix of PPLR values

Probability of positive log-ratio

Probability of positive log-ratio

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

Calculate differential expression between conditions using FC

pumaPCAModel-class

Class pumaPCAModel

clusterApplyLBDots

clusterApplyLB with dots to indicate progress

Multi-chip modified gamma Model for Oligonucleotide Signal

Print puma license

plotHistTwoClasses

Stacked histogram plot of two different classes

Clust.exampleStd

The example data of the standard deviation for gene expression levels

createDesignMatrix

Automatically create a design matrix from an ExpressionSet

exprReslt-class

Class exprReslt

Calculate distance between two matrices

modified gamma Model for Oligonucleotide Signal

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

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

The example data of the mean gene expression levels

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

A legend which allows longer lines

compareLimmapumaDE

Compare pumaDE with a default Limma model

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

Compute mmgmos Directly from CEL Files

Combining replicates for each condition

removeUninformativeFactors

Remove uninformative factors from the phenotype data of an ExpressionSet

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