Accessors: Accessors for SegmentedCells

AccessorsR Documentation

Accessors for SegmentedCells


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


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



A 'SegmentedCells' object.


A vector of imageIDs to specifically extract.


When false outputs a list of DataFrames split by imageID


Used to expand the phenotype information from per image to per cell.


The relevant information used to replace.


A logical vector of the cells to be kept.


A variable to add or retrieve from cellSummary.


DataFrame or a list of DataFrames



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.


Retrieves the DataFrame containing morphology information.


Retrieves the DataFrame containing intensity of gene or protein markers.


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.


### Something that resembles cellProfiler data


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)

ellispatrick/spicyR documentation built on Nov. 22, 2022, 10:45 p.m.