Description Usage Arguments Value Examples

View source: R/fgseaMultilevel.R

This feature is based on the adaptive multilevel splitting Monte Carlo approach. This allows us to exceed the results of simple sampling and calculate arbitrarily small P-values.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`pathways` |
List of gene sets to check. |

`stats` |
Named vector of gene-level stats. Names should be the same as in 'pathways' |

`sampleSize` |
The size of a random set of genes which in turn has size = pathwaySize |

`minSize` |
Minimal size of a gene set to test. All pathways below the threshold are excluded. |

`maxSize` |
Maximal size of a gene set to test. All pathways above the threshold are excluded. |

`eps` |
This parameter sets the boundary for calculating the p value. |

`scoreType` |
This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg") |

`nproc` |
If not equal to zero sets BPPARAM to use nproc workers (default = 0). |

`gseaParam` |
GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores. |

`BPPARAM` |
Parallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting 'nproc' default value 'bpparam()' is used. |

`nPermSimple` |
Number of permutations in the simple fgsea implementation for preliminary estimation of P-values. |

`absEps` |
deprecated, use 'eps' parameter instead |

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following

pathway – name of the pathway as in 'names(pathway)';

pval – an enrichment p-value;

padj – a BH-adjusted p-value;

log2err – the expected error for the standard deviation of the P-value logarithm.

ES – enrichment score, same as in Broad GSEA implementation;

NES – enrichment score normalized to mean enrichment of random samples of the same size;

size – size of the pathway after removing genes not present in 'names(stats)'.

leadingEdge – vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading.

1 2 3 | ```
data(examplePathways)
data(exampleRanks)
fgseaMultilevelRes <- fgseaMultilevel(examplePathways, exampleRanks, maxSize=500)
``` |

```
Warning message:
In fgseaMultilevel(examplePathways, exampleRanks, maxSize = 500) :
For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
```

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