Description Usage Arguments Value

View source: R/clinical.ranksum.R

Using ranksums to capture gene set/module expression, we can ask how gene sets/modules in each tissue are differentially expressed according to patient<e2><80><99>s clinicopathological variables. The type of the clinicopathological attribute (categorical or continuous) determines the underlying statistical test. Pearson correlation (Student asymptotic p-value) is used to test association between a given module and continuous patient attributes (eg. age). Analysis of Variance (ANOVA) is used to test association between a given module and categorical patient attributes (eg. ER status).

1 2 3 | ```
clinical.ranksum(mixt.dat, mixt.ranksum, tissue, cohort.name = "all",
corType = "p", nRuns = 10000, randomSeed = 12345, mc.cores = 2,
verbose = 2)
``` |

`mixt.dat` |
Data object from matched tissues. |

`mixt.ranksum` |
output of sig.ranksum() |

`tissue` |
character string that provides the name of tissue we want to look at |

`cohort.name` |
character string that provides the name of the patient cohort to analyze, default is set to 'all' |

`corType` |
a character string indicating which correlation coefficient is to be computed. Default 'p' for pearson |

`nRuns` |
number of permutations |

`randomSeed` |
seed number for random number generation. Default set as '1234' |

`mc.cores` |
number of cores |

`verbose` |
numerical. default > 0 show informational text on progress |

matrix of p-values determining significance of association between clinical variables (in row) and gene sets/modules (in column)

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