View source: R/graphSpatialFDR.R

graphSpatialFDR | R Documentation |

Borrowing heavily from `cydar`

which corrects for multiple-testing
using a weighting scheme based on the volumetric overlap over hyperspheres.
In the instance of graph neighbourhoods this weighting scheme can use graph
connectivity or incorpate different within-neighbourhood distances for the
weighted FDR calculation.

`x.nhoods` |
A list of vertices and the constituent vertices of their neighbourhood |

`graph` |
The kNN graph used to define the neighbourhoods |

`pvalues` |
A vector of p-values calculated from a GLM or other appropriate statistical test for differential neighbourhood abundance |

`k` |
A numeric integer that determines the kth nearest neighbour distance to use for
the weighted FDR. Only applicaple when using |

`weighting` |
A string scalar defining which weighting scheme to use. Choices are: max, k-distance, neighbour-distance or graph-overlap. |

`reduced.dimensions` |
(optional) A |

`distances` |
(optional) A |

`indices` |
(optional) A list of neighbourhood index vertices in the same order as the input neighbourhoods. Only used for the k-distance weighting. |

Each neighbourhood is weighted according to the weighting scheme
defined. k-distance uses the distance to the kth nearest neighbour
of the index vertex, neighbour-distance uses the average within-neighbourhood
Euclidean distance in reduced dimensional space, max uses the largest within-neighbourhood distance
from the index vertex, and graph-overlap uses the total number of cells overlapping between
neighborhoods (distance-independent measure). The frequency-weighted version of the
BH method is then applied to the p-values, as in `cydar`

.

A vector of adjusted p-values

Adapted by Mike Morgan, original function by Aaron Lun

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