create_eset_r

 An object of class <code><a rd-options="Biobase:class.ExpressionSet" href="/link/ExpressionSet?package=puma&version=3.12.0&to=Biobase%3Aclass.ExpressionSet" data-mini-rdoc="Biobase:class.ExpressionSet::ExpressionSet">ExpressionSet</a></code>. The phenotype information from this is used as the phenotype information of the returned object 

eset

 A data frame with components named 'M1', 'M2', and so on, which represent the mean expression values for condition 1, condition 2, and so on. It also has components named 'Std1', 'Std2', and so on, which represent the standard deviation of the gene expression values for condition 1, condtion 2, and so on. This type of data frame is output by function <code><a rd-options="" href="/link/bcomb?package=puma&version=3.12.0" data-mini-rdoc="puma::bcomb">bcomb</a></code> and <code><a rd-options="" href="/link/hcomb?package=puma&version=3.12.0" data-mini-rdoc="puma::hcomb">hcomb</a></code> 

design.matrix


This is really an internal function called from pumaComb. It is used to create an <code><a rd-options="Biobase:class.ExpressionSet" href="/link/ExpressionSet?package=puma&version=3.12.0&to=Biobase%3Aclass.ExpressionSet" data-mini-rdoc="Biobase:class.ExpressionSet::ExpressionSet">ExpressionSet</a></code> object from the output of the bcomb function (which was originally part of the <span class="pkg">pplr</span> package. Don't worry about it!


manip

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.

Xuejun Liu

puma

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

create_eset_r function

 An object of class <code><a rd-options='Biobase:class.ExpressionSet' href='ExpressionSet'>ExpressionSet</a></code>. The phenotype information from this is used as the phenotype information of the returned object 

 A data frame with components named 'M1', 'M2', and so on, which represent the mean expression values for condition 1, condition 2, and so on. It also has components named 'Std1', 'Std2', and so on, which represent the standard deviation of the gene expression values for condition 1, condtion 2, and so on. This type of data frame is output by function <code><a rd-options='' href='bcomb'>bcomb</a></code> and <code><a rd-options='' href='hcomb'>hcomb</a></code> 


This is really an internal function called from pumaComb. It is used to create an <code><a rd-options='Biobase:class.ExpressionSet' href='ExpressionSet'>ExpressionSet</a></code> object from the output of the bcomb function (which was originally part of the <span class = "pkg">pplr</span> package. Don't worry about it!


create_eset_r: Create an ExpressionSet from a PPLR matrix

Description

Usage

Arguments

Value

See Also