Package 'Statial' reference manual

Title:	A package to identify changes in cell state relative to spatial associations
Description:	Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
Authors:	Farhan Ameen [aut, cre], Sourish Iyengar [aut], Shila Ghazanfar [aut], Ellis Patrick [aut]
Maintainer:	Farhan Ameen <[email protected]>
License:	GPL-3
Version:	1.9.0
Built:	2024-11-18 05:05:21 UTC
Source:	https://github.com/bioc/Statial

Calculate the level of marker contamination of each cell

Description

Calculates contamination scores using a random forest classification

Usage

calcContamination(
  cells,
  markers = NULL,
  num.trees = 100,
  verbose = FALSE,
  missingReplacement = 0,
  assay = "intensities",
  cellType = "cellType",
  redDimName = "contaminations"
)
calcContamination(
  cells,
  markers = NULL,
  num.trees = 100,
  verbose = FALSE,
  missingReplacement = 0,
  assay = "intensities",
  cellType = "cellType",
  redDimName = "contaminations"
)

Arguments

`cells`	A SingleCellExperiment or SpatialExperiment with a cellType column as well as marker intensity information corresponding to each cell.
`markers`	A vector of markers that proxy a cell's state. If NULL, all markers will be used.
`num.trees`	Number of trees to be used in the random forest classifier
`verbose`	A logical indicating whether information about the final random forest model should be outputted.
`missingReplacement`	A default value to replace missing marker intensities for classification.
`assay`	The assay in the SingleCellExperiment object that contains the desired marker expressions.
`cellType`	The name of the column in colData that stores the cell types.
`redDimName`	The redDimName to store the output in the sce.

Examples

data("kerenSCE")

singleCellDataDistancesContam <- calcContamination(
  kerenSCE
)

data("kerenSCE")

singleCellDataDistancesContam <- calcContamination(
  kerenSCE
)

First layer wrapper function to build linear models measuring state changes

Description

Builds linear models measuring marker based state changes in a cell type based of the proximity or abundance of another cell type. The function provides the option to build robust and mixed linear model variants

Usage

calcStateChanges(
  cells,
  marker = NULL,
  from = NULL,
  to = NULL,
  image = NULL,
  type = "distances",
  assay = 1,
  cellType = "cellType",
  imageID = "imageID",
  contamination = NULL,
  minCells = 20,
  verbose = FALSE,
  timeout = 10,
  nCores = 1
)
calcStateChanges(
  cells,
  marker = NULL,
  from = NULL,
  to = NULL,
  image = NULL,
  type = "distances",
  assay = 1,
  cellType = "cellType",
  imageID = "imageID",
  contamination = NULL,
  minCells = 20,
  verbose = FALSE,
  timeout = 10,
  nCores = 1
)

Arguments

`cells`	A dataframe with a imageID, cellType, and marker intensity column along with covariates (e.g. distance or abundance of the nearest cell type) to model cell state changes
`marker`	A vector of markers that proxy a cell's state. If NULL, all markers will be used.
`from`	A vector of cell types to use as the primary cells. If NULL, all cell types will be used.
`to`	A vector of cell types to use as the interacting cells. If NULL, all cell types will be used.
`image`	A vector of images to filter to. If null all images will be used.
`type`	What type of state change. This value should be in reduced dimensions.
`assay`	The assay in the SingleCellExperiment object that contains the marker expressions.
`cellType`	The column in colData that stores the cell types.
`imageID`	The column in colData that stores the image ids.
`contamination`	If TRUE, use the contamination scores that have previously been calculate. Otherwise a name of which reduced dimension contains the scores.
`minCells`	The minimum number of cells required to fit a model.
`verbose`	A logical indicating if messages should be printed
`timeout`	A maximum time allowed to build each model. Setting this may be important when building rlm mixed linear models
`nCores`	Number of cores for parallel processing

Examples

library(dplyr)
data("kerenSCE")

kerenSCE <- kerenSCE[, kerenSCE$imageID %in% c(5, 6)]

kerenSCE <- getDistances(kerenSCE,
  maxDist = 200,
)

imageModels <- calcStateChanges(
  cells = kerenSCE,
  from = "Macrophages",
  to = "Tumour"
)

library(dplyr)
data("kerenSCE")

kerenSCE <- kerenSCE[, kerenSCE$imageID %in% c(5, 6)]

kerenSCE <- getDistances(kerenSCE,
  maxDist = 200,
)

imageModels <- calcStateChanges(
  cells = kerenSCE,
  from = "Macrophages",
  to = "Tumour"
)

Calculate pairwise distance between cell types

Description

Calculates the euclidean distance from each cell to the nearest cell of each type for a single image

Usage

distanceCalculator(data, maxDist = 200, distFun = "min")
distanceCalculator(data, maxDist = 200, distFun = "min")

Arguments

`data`	the single cell data of interest
`maxDist`	Maximum distance between pairs of points to be counted as close pairs.
`distFun`	How to merge duplicate entries.

Wrapper to calculate imhomogenous K function between a cell and surrounding types on each image

Description

Calculate the imhomogenous K function (a measure of cell type abundance) for each cell to other cell types

Usage

getAbundances(
  cells,
  r = 200,
  distFun = "abundance",
  redDimName = "abundances",
  cellType = "cellType",
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  nCores = 1
)
getAbundances(
  cells,
  r = 200,
  distFun = "abundance",
  redDimName = "abundances",
  cellType = "cellType",
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  nCores = 1
)

Arguments

`cells`	A dataframe with a cellType column as well as x and y spatial coordinates. The dataframe must contain a imageID column and cellID (unique cell identifier's) column as well
`r`	Radius to include in that calculation of pairwise abundance (K-function) between cells (can be a numeric or vector of radii)
`distFun`	What distance function to use.
`redDimName`	Name of the reduced dimension to store in sce.
`cellType`	The name of the column in colData that stores the cell types.
`imageID`	The name of the column in colData that Stores the image ids.
`spatialCoords`	The names of the columns in colData that store the spatial coordinates.
`nCores`	Number of cores for parallel processing

Examples

library(dplyr)
data("kerenSCE")

singleCellDataCounts <- getAbundances(kerenSCE,
  r = 200,
)

library(dplyr)
data("kerenSCE")

singleCellDataCounts <- getAbundances(kerenSCE,
  r = 200,
)

Wrapper to calculate pairwise distance between cell types by image

Description

Calculates the euclidean distance from each cell to the nearest cell of each type

Usage

getDistances(
  cells,
  maxDist = NULL,
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  redDimName = "distances",
  distFun = "min",
  nCores = 1
)
getDistances(
  cells,
  maxDist = NULL,
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  redDimName = "distances",
  distFun = "min",
  nCores = 1
)

Arguments

`cells`	A dataframe with a cellType column as well as x and y spatial coordinates. The dataframe must contain a imageID column and cellID (unique cell identifier's) column as well
`maxDist`	The maximum distance considered.
`imageID`	The name of the colData column that stores in the image ID.
`spatialCoords`	The columns that store the spatial coordinates.
`cellType`	The name of the colData column that stores the cell types.
`redDimName`	The name of the reduced dimension to store the distances in.
`distFun`	What distance function to use. Can be min or abundance.
`nCores`	Number of cores for parallel processing.

Examples

data("kerenSCE")

kerenSCE <- getDistances(kerenSCE,
  maxDist = 200
)

data("kerenSCE")

kerenSCE <- getDistances(kerenSCE,
  maxDist = 200
)

Extract the average expression for all markers for each cell type in each region defined by lisaClust

Description

Takes a SingleCellExperiment and outputs a dataframe in a convenient format for cross validation

Usage

getMarkerMeans(
  data,
  imageID = NULL,
  cellType = NULL,
  region = NULL,
  markers = NULL,
  assay = 1,
  replaceVal = 0
)
getMarkerMeans(
  data,
  imageID = NULL,
  cellType = NULL,
  region = NULL,
  markers = NULL,
  assay = 1,
  replaceVal = 0
)

Arguments

`data`	A SingleCellExperiment object with intensities data in the assays slot and regions information in colData generated by lisaClust.
`imageID`	The colData column that stores the image IDs.
`cellType`	The colData column that store the cell types.
`region`	The colData column that stores the regions.
`markers`	A string vector of markers that proxy a cell's state. If NULL, all markers will be used.
`assay`	Which assay do you want to use for the expression data.
`replaceVal`	A value to replace missing values with.

Examples

data(kerenSCE)

kerenSCE <- kerenSCE[, kerenSCE$imageID %in% c("5", "6")]

regionSCE <- lisaClust::lisaClust(kerenSCE, k = 5)

lisaClustOutput <- getMarkerMeans(regionSCE)
data(kerenSCE)

kerenSCE <- kerenSCE[, kerenSCE$imageID %in% c("5", "6")]

regionSCE <- lisaClust::lisaClust(kerenSCE, k = 5)

lisaClustOutput <- getMarkerMeans(regionSCE)

Extract parent and all children from a Phylo object

Description

This function takes in a 'phylo' object or a 'treekoR' result from the getClusterTree function, and converts its into a named list of each and children to input into parentCombinations.

Note: Parent populations with one child will be pruned. Make sure to include this cell type in the 'all' vector when using parentCombinations to ensure this cell type is included in pairwise calculations.

Usage

getParentPhylo(phylo_tree)
getParentPhylo(phylo_tree)

Arguments

phlyo_tree

a phylo object or a treekoR result.

Value

A named list of parents and their respective children.

Test whether an object is a kontextualResult

Description

Test whether an object is a kontextualResult

Usage

isKontextual(kontextualResult)
isKontextual(kontextualResult)

Arguments

kontextualResult

a object to test

Examples

data <- data.frame()
if (!isKontextual(data)) print("Not a kontextualResult")

data <- data.frame()
if (!isKontextual(data)) print("Not a kontextualResult")

Kontextual results from kerenSCE

Description

This is a kontextual results data.frame created using Kontextual on the kerenSCE dataset.

Usage

data(kerenKontextual)
data(kerenKontextual)

Format

kerenKontextual a kontextual results object.

MIBI-TOF Breast cancer intensities

Description

This is a single MIBI-TOF data of breast cancer from patient 6 of the Keren et al 2018 dataset.

Usage

data(kerenSCE)
data(kerenSCE)

Format

kerenSCE a SingleCellExperiment object

References

Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S. R., Kurian, A., Van Valen, D., West, R., Bendall, S. C., & Angelo, M. (2018). A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell, 174(6), 1373-1387.e1319. ([DOI](https://doi.org/10.1016/j.cell.2018.08.039))

Evaluation of Kontextual over a range of radii.

Description

This function obtains 'Kontextual' values over a range of radii, standard deviations for each value can be obtained using permutation for significance testing. To obtain estimates for standard deviations specify 'se = TRUE'.

Usage

kontextCurve(
  cells,
  from,
  to,
  parent,
  image = NULL,
  rs = seq(10, 100, 10),
  inhom = FALSE,
  edge = TRUE,
  se = FALSE,
  nSim = 20,
  cores = 1,
  imageID = "imageID",
  cellType = "cellType",
  ...
)
kontextCurve(
  cells,
  from,
  to,
  parent,
  image = NULL,
  rs = seq(10, 100, 10),
  inhom = FALSE,
  edge = TRUE,
  se = FALSE,
  nSim = 20,
  cores = 1,
  imageID = "imageID",
  cellType = "cellType",
  ...
)

Arguments

`cells`	A single image from a SingleCellExperiment object
`from`	The first cell type to be evaluated in the pairwise relationship.
`to`	The second cell type to be evaluated in the pairwise relationship.
`parent`	The parent population of the from cell type (must include from cell type).
`image`	A vector of images to subset the results to. If NULL we default to all images.
`rs`	A vector of radii to evaluate kontextual over.
`inhom`	A logical value indicating whether to perform an inhomogeneous L function.
`edge`	A logical value indicating whether to perform edge correction.
`se`	A logical value to indicate if the standard deviation of kontextual should be calculated to construct error bars.
`nSim`	Number of randomisations to perform using `relabelKontextual`, which will be used to calculated the SE.
`cores`	Number of cores for parallel processing.
`imageID`	The column in colData that stores the image ids.
`cellType`	The column in colData that stores the cell types.
`...`	Any arguments passed into `Kontextual`.

Value

A data frame of original L values and Kontextual values evaluated over a range of radii.

Examples


data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

rsDf <- kontextCurve(
  cells = kerenSCE,
  from = "CD4_Cell",
  to = "Keratin_Tumour",
  parent = c("CD4_Cell", "Macrophages"),
  rs = seq(10, 510, 100),
  cores = 2
)

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

rsDf <- kontextCurve(
  cells = kerenSCE,
  from = "CD4_Cell",
  to = "Keratin_Tumour",
  parent = c("CD4_Cell", "Macrophages"),
  rs = seq(10, 510, 100),
  cores = 2
)

Plotting the original and kontextual L values over a range of radii.

Description

This function takes outputs from rsCurve and plots them in ggplot. If standard deviation is estimated in rsCurve, then confidence intervals will be constructed based on the standard deviation. If the confidence interval overlaps with 0, then the relationship is insignificant for that radius.

Usage

kontextPlot(rsDf)
kontextPlot(rsDf)

Arguments

rsDf

A data frame from kontextCurve.

Value

A ggplotly object showing the original and kontextual L function values over a range of radii

Examples

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

rsDf <- kontextCurve(
  cells = kerenImage6,
  from = "p53",
  to = "Immune",
  parent = c("p53", "Keratin+Tumour"),
  rs = seq(10, 510, 100),
  cores = 2
)

kontextPlot(rsDf)

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

rsDf <- kontextCurve(
  cells = kerenImage6,
  from = "p53",
  to = "Immune",
  parent = c("p53", "Keratin+Tumour"),
  rs = seq(10, 510, 100),
  cores = 2
)

kontextPlot(rsDf)

Evaluation of pairwise cell relationships, conditional on a 3rd population.

Description

Kontextual identifies the relationship between two cell types which are conditional on the spatial behaviour of a 3rd cell population, for a particular radius (r).

Usage

Kontextual(
  cells,
  r,
  parentDf = NULL,
  from = NULL,
  to = NULL,
  parent = NULL,
  image = NULL,
  inhom = FALSE,
  edgeCorrect = TRUE,
  window = "convex",
  window.length = NA,
  includeOriginal = TRUE,
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  imageID = "imageID",
  cores = 1
)
Kontextual(
  cells,
  r,
  parentDf = NULL,
  from = NULL,
  to = NULL,
  parent = NULL,
  image = NULL,
  inhom = FALSE,
  edgeCorrect = TRUE,
  window = "convex",
  window.length = NA,
  includeOriginal = TRUE,
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  imageID = "imageID",
  cores = 1
)

Arguments

`cells`	A SingleCellExperiment, SpatialExperiment or a list of data.frames containing columns specifying the imageID, cellType, and x and y spatial coordinates.
`r`	Radii to evaluated pairwise relationships between from and to cells.
`parentDf`	A data frame from `parentCombinations`
`from`	The first cell type to be evaluated in the pairwise relationship.
`to`	The second cell type to be evaluated in the pairwise relationship.
`parent`	The parent population of the from cell type (must include from cell type).
`image`	A vector of images to subset the results to. If NULL we default to all images.
`inhom`	A logical value indicating whether to account for inhomogeneity.
`edgeCorrect`	A logical value indicating whether to perform edge correction.
`window`	Type of window for data, either 'square', 'convex' or 'concave', passed into `makeWindow`
`window.length`	A tuning parameter for controlling the level of concavity when estimating concave windows. Passed into `makeWindow`
`includeOriginal`	A logical value to return the original L function values along with the kontextual values.
`spatialCoords`	The columns which contain the x and y spatial coordinates.
`cellType`	The column which contains the cell types.
`imageID`	The column which contains image identifiers.
`cores`	Number of cores for parallel processing.

Value

A kontextualResult object

Examples

# Load data
data("kerenSCE")


CD4_Kontextual <- Kontextual(
  cells = kerenSCE,
  r = 50,
  from = "Macrophages",
  to = "Keratin_Tumour",
  parent = c("Macrophages", "CD4_Cell"),
  image = "6"
)


head(CD4_Kontextual)

# Load data
data("kerenSCE")


CD4_Kontextual <- Kontextual(
  cells = kerenSCE,
  r = 50,
  from = "Macrophages",
  to = "Keratin_Tumour",
  parent = c("Macrophages", "CD4_Cell"),
  image = "6"
)


head(CD4_Kontextual)

Creates a window for a PPP object

Description

This function creates a window for a 'spatstat::ppp' object, the type of window can be specified using the 'window' argument.

Usage

makeWindow(data, window = "square", window.length = NULL)
makeWindow(data, window = "square", window.length = NULL)

Arguments

`data`	A single image data frame from a SingleCellExperiment object or PPP object.
`window`	The shape of window around the regions, can be 'square', 'convex' or 'concave'
`window.length`	A tuning parameter for controlling the level of concavity when estimating concave windows.

Value

Creates an 'owin' class, representing the observation window for the image.

Examples

data <- data.frame(x = rnorm(10), y = rnorm(10))
ow <- makeWindow(data, window = "square")

spatstat.geom::ppp(x = data$x, y = data$y, window = ow)

data <- data.frame(x = rnorm(10), y = rnorm(10))
ow <- makeWindow(data, window = "square")

spatstat.geom::ppp(x = data$x, y = data$y, window = ow)

Create all combinations of cell type relationships from a list of parents

Description

This function takes in named vectors of all the parent populations in the dataset, and creates a data frame containing all pairwise cell relationships, this data frame can be inputed into the 'parentDf' argument in 'Kontextual'.

Usage

parentCombinations(all, ..., parentList = NULL)
parentCombinations(all, ..., parentList = NULL)

Arguments

`all`	A list of all the 'to' cell types Kontextual is evaluated over.
`...`	Vectors of each parent population.
`parentList`	a named list where the names correspond to parent names and the values contain a vector of children for that parent. Note: If parentList is specified the '...' argument will be ignored, see examples.

Value

A data frame containing all pairwise cell relationships and their corresponding parent

Examples

# Example 1, using `parentList`

parentList <- list(
  "tcells" = c("CD4", "CD8"),
  "tissue" = c("epithelial", "stromal")
)

allCells <- c("tumour", "CD4", "CD8", "epithelial", "stromal")

parentCombinations(all = allCells, parentList = parentList)


# Example 2, with `...` operator
tcells <- c("CD4", "CD8")
tissue <- c("epithelial", "stromal")
allCells <- c("tumour", tissue, tcells)

parentCombinations(all = allCells, tcells, tissue)

# Example 1, using `parentList`

parentList <- list(
  "tcells" = c("CD4", "CD8"),
  "tissue" = c("epithelial", "stromal")
)

allCells <- c("tumour", "CD4", "CD8", "epithelial", "stromal")

parentCombinations(all = allCells, parentList = parentList)


# Example 2, with `...` operator
tcells <- c("CD4", "CD8")
tissue <- c("epithelial", "stromal")
allCells <- c("tumour", tissue, tcells)

parentCombinations(all = allCells, tcells, tissue)

Visualise Cell-Cell Marker Relationships

Description

Helper functions to visualise OLS model fits for image based state models

Usage

plotStateChanges(
  cells,
  image,
  from,
  to,
  marker,
  type = "distances",
  assay = 1,
  cellType = "cellType",
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  size = 1,
  shape = 19,
  interactive = FALSE,
  plotModelFit = FALSE,
  method = "lm"
)
plotStateChanges(
  cells,
  image,
  from,
  to,
  marker,
  type = "distances",
  assay = 1,
  cellType = "cellType",
  imageID = "imageID",
  spatialCoords = c("x", "y"),
  size = 1,
  shape = 19,
  interactive = FALSE,
  plotModelFit = FALSE,
  method = "lm"
)

Arguments

`cells`	A SingleCellExperiment that has had distances already calculated.
`image`	An image to subset to.
`from`	A character indicating the name of the cell type (from the cellType column) whose cell state is being investigated in
`to`	A character indicating the name of the cell type (from the cellType column) who may be influencing the cell state of another cell type
`marker`	The marker of interest.
`type`	The name of the reduced dimension to use for the x-axis.
`assay`	Name of the assay that stores the marker expression.
`cellType`	The name of the column in colData that stores the cell types.
`imageID`	The name of the column in colData that stores the image ids.
`spatialCoords`	The names of the columns in colData that store the spatial coordinates.
`size`	Aesthetic numerical variable determining the size of the displayed cells
`shape`	Aesthetic variable determining the shape grouping of the displayed cells
`interactive`	Logical indicating if the output visualisation should be a interactive (plotly)
`plotModelFit`	Logical indicating if fitted values should be plotted or actual intensities for marker specified. The default is to plot actual intensities
`method`	The method to build the model with. Currently the only option is "lm". However, capabilities may be expanded in the future

Details

image,

Examples

library(dplyr)
data("kerenSCE")

kerenSCE <- getDistances(kerenSCE)

p <- plotStateChanges(
  cells = kerenSCE,
  type = "distances",
  image = "6",
  from = "Keratin_Tumour",
  to = "Macrophages",
  marker = "p53",
  size = 1,
  shape = 19,
  interactive = FALSE,
  plotModelFit = FALSE,
  method = "lm"
)

p

library(dplyr)
data("kerenSCE")

kerenSCE <- getDistances(kerenSCE)

p <- plotStateChanges(
  cells = kerenSCE,
  type = "distances",
  image = "6",
  from = "Keratin_Tumour",
  to = "Macrophages",
  marker = "p53",
  size = 1,
  shape = 19,
  interactive = FALSE,
  plotModelFit = FALSE,
  method = "lm"
)

p

Convert Kontextual or state changes result to a matrix for classification

Description

Convert Kontextual or state changes result to a matrix for classification

Usage

prepMatrix(result, replaceVal = 0, column = NULL, test = NULL)
prepMatrix(result, replaceVal = 0, column = NULL, test = NULL)

Arguments

`result`	a kontextual or state changes result data.frame.
`replaceVal`	value which NAs are replaced with.
`column`	The column which contains the scores that you want to select.
`test`	A column containing which will be the column names of the expanded matrix.

Examples

data("kerenSCE")


CD4_Kontextual <- Kontextual(
  cells = kerenSCE,
  r = 50,
  from = "Macrophages",
  to = "Keratin_Tumour",
  parent = c("Macrophages", "CD4_Cell"),
  image = "6"
)


kontextMat <- prepMatrix(CD4_Kontextual)

data("kerenSCE")


CD4_Kontextual <- Kontextual(
  cells = kerenSCE,
  r = 50,
  from = "Macrophages",
  to = "Keratin_Tumour",
  parent = c("Macrophages", "CD4_Cell"),
  image = "6"
)


kontextMat <- prepMatrix(CD4_Kontextual)

Cell permutation for Kontextual

Description

Function which randomises specified cells in an image and calculates the 'Kontextual' value. This can be used to estimate the null distribution, of the parent cell population for significance testing.

This function relabels all specified cells within a single image, to estimate the null distribution of cell population specified.

Usage

relabelKontextual(
  cells,
  nSim = 1,
  r,
  from,
  to,
  parent,
  image = NULL,
  returnImages = FALSE,
  inhom = TRUE,
  edge = FALSE,
  cores = 1,
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  imageID = "imageID",
  ...
)

relabel(image, labels = NULL)
relabelKontextual(
  cells,
  nSim = 1,
  r,
  from,
  to,
  parent,
  image = NULL,
  returnImages = FALSE,
  inhom = TRUE,
  edge = FALSE,
  cores = 1,
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  imageID = "imageID",
  ...
)

relabel(image, labels = NULL)

Arguments

`cells`	A single image data frame from a SingleCellExperiment object
`nSim`	Number of randomisations which will be calculated.
`r`	Radius to evaluated pairwise relationships between from and to cells.
`from`	The first cell type to be evaluated in the pairwise relationship.
`to`	The second cell type to be evaluated in the pairwise relationship.
`parent`	The parent population of the from cell type (must include from cell type).
`image`	A single image from a Single Cell Experiment object.
`returnImages`	A logical value to indicate whether the function should return the randomised images along with the Kontextual values.
`inhom`	A logical value indicating whether to account for inhomogeneity.
`edge`	A logical value indicating whether to perform edge correction.
`cores`	Number of cores for parallel processing.
`spatialCoords`	A character vector containing the names of the two spatial dimansions in the data. Defaults to 'c("x", "y")'.
`cellType`	The name of the cell type field in the data. Defualts to "cellType".
`imageID`	The name of the image ID field in the data. Defualts to "imageID".
`...`	Any arguments passed into `Kontextual`
`labels`	A vector of CellTypes labels to be permuted If NULL all cells labels will be radomised.

Value

A data frame containing Kontextual value for each randomised image. If 'returnImages = TRUE' function will return a list with Kontextual values and the randomised images.

A data frame containing all pairwise cell relationships and their corresponding parent

Examples

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

relabelResult <- relabelKontextual(
  cells = kerenImage6,
  nSim = 5,
  r = 250,
  from = "CD4_Cell",
  to = "Keratin_Tumour",
  parent = c("CD4_Cell", "Macrophages"),
  cores = 2
)

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

kerenImage6 <- kerenImage6 |>
  SingleCellExperiment::colData() |>
  data.frame()

# Permute CD8 T cells and T cell labels in the image
relabeledImage <- relabel(kerenImage6, labels = c("p53", "Keratin+Tumour"))
plot(relabeledImage)

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

relabelResult <- relabelKontextual(
  cells = kerenImage6,
  nSim = 5,
  r = 250,
  from = "CD4_Cell",
  to = "Keratin_Tumour",
  parent = c("CD4_Cell", "Macrophages"),
  cores = 2
)

data("kerenSCE")

kerenImage6 <- kerenSCE[, kerenSCE$imageID == "6"]

kerenImage6 <- kerenImage6 |>
  SingleCellExperiment::colData() |>
  data.frame()

# Permute CD8 T cells and T cell labels in the image
relabeledImage <- relabel(kerenImage6, labels = c("p53", "Keratin+Tumour"))
plot(relabeledImage)

Package 'Statial'

Help Index

Calculate the level of marker contamination of each cell

Description

Usage

Arguments

Examples

First layer wrapper function to build linear models measuring state changes

Description

Usage

Arguments

Examples

Calculate pairwise distance between cell types

Description

Usage

Arguments

Wrapper to calculate imhomogenous K function between a cell and surrounding types on each image

Description

Usage

Arguments

Examples

Wrapper to calculate pairwise distance between cell types by image

Description

Usage

Arguments

Examples

Extract the average expression for all markers for each cell type in each region defined by lisaClust

Description

Usage

Arguments

Examples

Extract parent and all children from a Phylo object

Description

Usage

Arguments

Value

Test whether an object is a kontextualResult

Description

Usage

Arguments

Examples

Kontextual results from kerenSCE

Description

Usage

Format

MIBI-TOF Breast cancer intensities

Description

Usage

Format

References

Evaluation of Kontextual over a range of radii.

Description

Usage

Arguments

Value

Examples

Plotting the original and kontextual L values over a range of radii.

Description

Usage

Arguments

Value

Examples

Evaluation of pairwise cell relationships, conditional on a 3rd population.

Description

Usage

Arguments

Value

Examples

Creates a window for a PPP object

Description

Usage

Arguments

Value

Examples

Create all combinations of cell type relationships from a list of parents

Description

Usage

Arguments

Value

Examples