SpicyResults-class | R Documentation |

Performs spatial tests on spatial cytometry data.

spicy( cells, condition = NULL, subject = NULL, covariates = NULL, from = NULL, to = NULL, dist = NULL, integrate = TRUE, nsim = NULL, verbose = TRUE, weights = TRUE, weightsByPair = FALSE, weightFactor = 1, window = "convex", window.length = NULL, BPPARAM = BiocParallel::SerialParam(), sigma = NULL, Rs = NULL, minLambda = 0.05, fast = TRUE, edgeCorrect = TRUE, includeZeroCells = FALSE, imageID = "imageID", cellType = "cellType", spatialCoords = c("x", "y"), ... )

`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. |

`weightsByPair` |
logical indicating whether weights should be calculated for each cell type pair. |

`weightFactor` |
numeric that controls the convexity of the weight function. |

`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. |

`edgeCorrect` |
A logical indicating whether to perform edge correction. |

`includeZeroCells` |
A logical indicating whether to include cells with zero counts in the pairwise association calculation. |

`imageID` |
The image ID if using SingleCellExperiment. |

`cellType` |
The cell type if using SingleCellExperiment. |

`spatialCoords` |
The spatial coordinates if using a SingleCellExperiment. |

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

Data frame of p-values.

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)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.