Description Usage Arguments Details Value Note Author(s) References Examples

This package contains functions for an exploratory parametric analysis of gene expression data. This type of analysis can assist in determining of lists of genes, such as those deregulated in defined experimental systems, are similarly deregulated in other data sets.

This function subsets the data based on lists of genes, computes a summary statistic for each gene list, and returns the results in a convenient form.

1 |

`exprs` |
matrix expression data, a numeric matrix, eSet, or ExpressionSet |

`cl` |
gene set list - "GeneSetCollection" or list of "SMC" objects |

`range` |
a 2 element vector describing the min and max length of concepts to analyze |

`enforceRange` |
boolean - if TRUE, the expression matrix must contain data for the proper number of genes as set by the range argument to return a significant result. (this argument is used for data that contains NA's...) |

`ref` |
a vector containing the index of reference samples from which to make comparisons. Defaults to NULL (internally referenced samples) |

`center` |
boolean - median center gene expression matrix columns prior to analysis. Can be helpful if 'ref' is used |

`p.value` |
numeric p.value threshold or NA to return all data or TRUE to return a matrix of p.values |

`weighted` |
boolean - weight results by the size of each gene list |

`...` |
extra arguments passed along to FUN |

Gene expression values are separated into subsets based on the lists
of genes contained in the cl argument. This can be a "GeneSetCollection" or a list of "SMC" (Simple Molecular Concept) objects.
For example, `readGmt`

can be used to produce a 'smc'
object list from a simple tab-delimited text file. The gene
expression values from each of these gene lists is extracted and a
summary statistic is computed for each subset (or region in the case of chromosomal bands/arms).

The expression data must have the same identifiers as the list of
genes being tested. If they are not, the expression data can be
converted using the `aggregateExprs`

function, that can use a
current annotation environment to convert and condense the gene
expression data.

By default the method set out by Kim and Volsky http://www.biomedcentral.com/1471-2105/6/144 is applied to the gene set.
If weighted==FALSE than the default `t.test`

function is used.

The function is set up to perform the analysis on individual
samples. For convenient method to analyze groups of samples, see the
"Limma User's Guide" for more information on how to see up a contrast
matrix and perform a linear model fit. The coefficients of the fit
can then be used a input into the `PGSEA`

function.

This package has not been extensively tested beyond a set of well defined curated pathways using the Affymetrix platform and significance values represent approximations. Any results should be confirmed by additional gene set testing methodologies.

If p.value is set to a number, a matrix of results that pass at that significance is returned, of size <number of samples> x <number of molecular concepts>.

If p.value is set to NA, all results are returned.

If p.value is set to TRUE, then a list is returned that consists of the PGSEA results as well as their p.values.

http://www.biomedcentral.com/1471-2105/6/144

Kim SY, Volsky DJ., kyle.furge@vai.org and karl.dykema@vai.org

PGSEA: Parametric Analysis of Gene Set Enrichment

1 2 3 4 5 6 |

```
Loading required package: GO.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: KEGG.db
KEGG.db contains mappings based on older data because the original
resource was removed from the the public domain before the most
recent update was produced. This package should now be considered
deprecated and future versions of Bioconductor may not have it
available. Users who want more current data are encouraged to look
at the KEGGREST or reactome.db packages
Loading required package: annaffy
GSM90306.CEL GSM90307.CEL GSM90308.CEL
ras UP - pmid: 16273092 NA NA NA NA
ras DN - pmid: 16273092 NA NA NA NA
myc UP - pmid: 16273092 NA NA NA NA
myc DN - pmid: 16273092 NA NA NA NA
5p NA NA NA NA
5q NA NA 3.142932 NA
GSM90309.CEL GSM90310.CEL GSM90387.CEL
ras UP - pmid: 16273092 NA NA NA NA
ras DN - pmid: 16273092 NA NA NA NA
myc UP - pmid: 16273092 NA NA NA NA
myc DN - pmid: 16273092 NA NA NA NA
5p NA NA NA NA
5q NA NA 4.78358 NA
GSM90388.CEL GSM90389.CEL GSM90390.CEL
ras UP - pmid: 16273092 NA NA NA -2.925284
ras DN - pmid: 16273092 NA NA NA NA
myc UP - pmid: 16273092 NA 4.114215 3.910187 5.732666
myc DN - pmid: 16273092 NA NA NA -2.936305
5p NA NA NA NA
5q NA NA NA NA
GSM90391.CEL
ras UP - pmid: 16273092 NA NA
ras DN - pmid: 16273092 NA NA
myc UP - pmid: 16273092 NA NA
myc DN - pmid: 16273092 NA NA
5p NA NA
5q NA NA
```

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