Description Usage Arguments Value Examples

Performs spatial tests on spatial cytometry data.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
spicy(
cells,
condition = NULL,
subject = NULL,
covariates = NULL,
from = NULL,
to = NULL,
dist = NULL,
integrate = TRUE,
nsim = NULL,
verbose = TRUE,
weights = TRUE,
window = "convex",
window.length = NULL,
BPPARAM = BiocParallel::SerialParam(),
sigma = NULL,
Rs = NULL,
minLambda = 0.05,
fast = TRUE,
...
)
``` |

`cells` |
A SegmentedCells or data frame that contains at least the variables x and y, giving the location coordinates of each cell, and cellType. |

`condition` |
Vector of conditions to be tested corresponding to each image if cells is a data frame. |

`subject` |
Vector of subject IDs corresponding to each image if cells is a data frame. |

`covariates` |
Vector of covariate names that should be included in the mixed effects model as fixed effects. |

`from` |
vector of cell types which you would like to compare to the to vector |

`to` |
vector of cell types which you would like to compare to the from vector |

`dist` |
The distance at which the statistic is obtained. |

`integrate` |
Should the statistic be the integral from 0 to dist, or the value of the L curve at dist. |

`nsim` |
Number of simulations to perform. If empty, the p-value from lmerTest is used. |

`verbose` |
logical indicating whether to output messages. |

`weights` |
logical indicating whether to include weights based on cell counts. |

`window` |
Should the window around the regions be 'square', 'convex' or 'concave'. |

`window.length` |
A tuning parameter for controlling the level of concavity when estimating concave windows. |

`BPPARAM` |
A BiocParallelParam object. |

`sigma` |
A numeric variable used for scaling when fitting inhomogeneous L-curves. |

`Rs` |
A vector of the radii that the measures of association should be calculated. |

`minLambda` |
Minimum value for density for scaling when fitting inhomogeneous L-curves. |

`fast` |
A logical describing whether to use a fast approximation of the inhomogeneous L-curves. |

`...` |
Other options to pass to bootstrap. |

Data frame of p-values.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data("diabetesData")
# Test with random effect for patient on only one pairwise combination of cell types.
spicy(diabetesData, condition = "stage", subject = "case",
from = "Tc", to = "Th")
# Test all pairwise combination of cell types without random effect of patient.
#spicyTest <- spicy(diabetesData, condition = "stage", subject = "case")
# Test all pairwise combination of cell types with random effect of patient.
#spicy(diabetesData, condition = "condition", subject = "subject")
# Test all pairwise combination of cell types with random effect of patient using
# a bootstrap to calculate significance.
#spicy(diabetesData, condition = "stage", subject = "case", nsim = 10000)
``` |

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