Performs pathway analysis

1 2 3 4 5 |

`G` |
a list containing the source, title, and probe sets associated with each curated pathway |

`minNPS` |
an integer specifying the minimum number of probe sets
in |

`maxNPS` |
an integer specifying the maximum number of probe sets
in |

`tab` |
a numeric matrix of expression values, with the rows and columns representing probe sets and sample arrays, respectively |

`phenotype` |
a numeric (or character if |

`nsim` |
an integer indicating the number of permutations to use |

`weightType` |
a character string specifying the type of weight to use when calculating NEk statistics |

`ngroups` |
an integer indicating the number of groups in the matrix |

`npath` |
an integer indicating the number of top gene sets to consider from each statistic when ranking the top pathways |

`verbose` |
a boolean to indicate whether to print debugging messages to the R console |

`allpathways` |
a boolean to indicate whether to include the top npath pathways from each statistic or just consider the top npath pathways (sorted by the sum of ranks of both statistics) when generating the summary table |

`annotpkg` |
a character vector specifying the name of the BioConductor annotation package to use to fetch accession numbers, Entrez Gene IDs, gene name, and gene symbols |

`alwaysUseRandomPerm` |
a boolean to indicate whether the algorithm
can use complete permutations for cases where |

`runSigPathway`

is a wrapper function that

(1) Selects the gene sets to analyze using `selectGeneSets`

(2) Calculates NTk and NEk statistics using `calculate.NTk`

and
`calculate.NEK`

(3) Ranks the top `npath`

pathways from each statistic using
`rankPathways`

(4) Summarizes the means, standard deviation, and individual
statistics of each probe set in each of the above pathways using
`getPathwayStatistics`

A list containing

`gsList` |
a list containing three vectors from the output of
the |

`list.NTk` |
a list from the output of calculate.NTk |

`list.NEk` |
a list from the output of calculate.NEk |

`df.pathways` |
a data frame from |

`list.gPS` |
a list from |

`parameters` |
a list of parameters (e.g., |

Lu Tian, Peter Park, and Weil Lai

Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S., Park
P.J. (2005) Discovering statistically significant pathways in
expression profiling studies. *Proceedings of the National
Academy of Sciences of the USA*, **102**, 13544-9.

http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
## Load in filtered, expression data
data(MuscleExample)
## Prepare the pathways to analyze and run analysis with 1 wrapper function
nsim <- 1000
ngroups <- 2
verbose <- TRUE
weightType <- "constant"
npath <- 25
allpathways <- FALSE
annotpkg <- "hgu133a.db"
res.muscle <- runSigPathway(G, 20, 500, tab, phenotype, nsim,
weightType, ngroups, npath, verbose,
allpathways, annotpkg)
## Summarize results
print(res.muscle$df.pathways)
## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways
print(res.muscle$list.gPS[[1]])
## Write table of top-ranked pathways and their associated probe sets to
## HTML files
writeSigPathway(res.muscle, tempdir(), "sigPathway_rSP",
"TopPathwaysTable.html")
``` |

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