Accessors {spicyR} | R Documentation

## Accessors for SegmentedCells

### 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)

[Package spicyR version 1.4.0 Index]