Accessors: Accessors for SegmentedCells

In spicyR: Spatial analysis of in situ cytometry data

Description

Methods to access various components of the 'SegmentedCells' object.

Usage

cellSummary(x, imageID = NULL, bind = TRUE)

cellSummary(x, imageID = NULL) <- value

cellMarks(x, imageID = NULL, bind = TRUE)

cellMarks(x, imageID = NULL) <- value

cellMorph(x, imageID = NULL, bind = TRUE)

cellMorph(x, imageID = NULL) <- value

imagePheno(x, imageID = NULL, bind = TRUE, expand = FALSE)

imagePheno(x, imageID = NULL) <- value

imageID(x, imageID = NULL)

cellID(x, imageID = NULL)

cellID(x) <- value

imageCellID(x, imageID = NULL)

imageCellID(x) <- value

cellType(x, imageID = NULL)

cellType(x, imageID = NULL) <- value

filterCells(x, select)

cellAnnotation(x, variable, imageID = NULL)

cellAnnotation(x, variable, imageID = NULL) <- value

Arguments

x	A 'SegmentedCells' object.
imageID	A vector of imageIDs to specifically extract.
bind	When false outputs a list of DataFrames split by imageID
expand	Used to expand the phenotype information from per image to per cell.
value	The relevant information used to replace.
select	A logical vector of the cells to be kept.
variable	A variable to add or retrieve from cellSummary.

Value

DataFrame or a list of DataFrames

Descriptions

'cellSummary':

Retrieves the DataFrame containing 'x' and 'y' coordinates of each cell as well as 'cellID', 'imageID' and 'cellType'. imageID can be used to select specific images and bind=FALSE outputs the information as a list split by imageID.

'cellMorph':

Retrieves the DataFrame containing morphology information.

'cellMarks':

Retrieves the DataFrame containing intensity of gene or protein markers.

'imagePheno':

Retrieves the DataFrame containing the phenotype information for each image. Using expand = TRUE will produce a DataFrame with the number of rows equal to the number of cells.

Examples

### Something that resembles cellProfiler data

set.seed(51773)

n = 10

cells <- data.frame(row.names = seq_len(n))
cells$ObjectNumber <- seq_len(n)
cells$ImageNumber <- rep(1:2,c(n/2,n/2))
cells$AreaShape_Center_X <- runif(n)
cells$AreaShape_Center_Y <- runif(n)
cells$AreaShape_round <- rexp(n)
cells$AreaShape_diameter <- rexp(n, 2)
cells$Intensity_Mean_CD8 <- rexp(n, 10)
cells$Intensity_Mean_CD4 <- rexp(n, 10)

cellExp <- SegmentedCells(cells, cellProfiler = TRUE)

### Cluster cell types
intensities <- cellMarks(cellExp)
kM <- kmeans(intensities,2)
cellType(cellExp) <- paste('cluster',kM$cluster, sep = '')

cellSummary(cellExp, imageID = 1)

spicyR documentation built on March 17, 2021, 6:01 p.m.