inst/doc/segmentedCells.R

In spicyR: Spatial analysis of in situ cytometry data

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(BiocStyle)

## ---- eval = FALSE------------------------------------------------------------
#  if (!require("BiocManager"))
#      install.packages("BiocManager")
#  BiocManager::install("spicyR")

## ----setup, message=FALSE-----------------------------------------------------
library(spicyR)
library(S4Vectors)

## -----------------------------------------------------------------------------

### 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)
cellExp

## -----------------------------------------------------------------------------
cellSum <- cellSummary(cellExp)
head(cellSum)

cellSummary(cellExp) <- cellSum

## -----------------------------------------------------------------------------
markers <- cellMarks(cellExp)
kM <- kmeans(markers,2)
cellType(cellExp) <- paste('cluster',kM$cluster, sep = '')

cellSum <- cellSummary(cellExp)
head(cellSum)

## -----------------------------------------------------------------------------
isletFile <- system.file("extdata","isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header = TRUE)

## -----------------------------------------------------------------------------
cellExp <- SegmentedCells(cells, cellProfiler = TRUE)
cellExp

## -----------------------------------------------------------------------------
markers <- cellMarks(cellExp)
kM <- kmeans(markers,4)
cellType(cellExp) <- paste('cluster',kM$cluster, sep = '')

cellSum <- cellSummary(cellExp)
head(cellSum)

## ---- fig.width=5, fig.height= 6----------------------------------------------
plot(cellExp, imageID=1)

## -----------------------------------------------------------------------------
set.seed(51773)

n = 10

cells <- data.frame(row.names = seq_len(n))
cells$cellID <- seq_len(n)
cells$imageCellID <- rep(seq_len(n/2),2)
cells$imageID <- rep(1:2,c(n/2,n/2))
cells$x <- runif(n)
cells$y <- runif(n)
cells$shape_round <- rexp(n)
cells$shape_diameter <- rexp(n, 2)
cells$intensity_CD8 <- rexp(n, 10)
cells$intensity_CD4 <- rexp(n, 10)
cells$cellType <- paste('cluster',sample(1:2,n,replace = TRUE), sep = '_')


## -----------------------------------------------------------------------------

cellExp <- SegmentedCells(cells, 
                          cellTypeString = 'cellType', 
                          intensityString = 'intensity_', 
                          morphologyString = 'shape_')
cellExp


## -----------------------------------------------------------------------------
morph <- cellMorph(cellExp)
head(morph)


## -----------------------------------------------------------------------------
phenoData <- DataFrame(imageID = c('1','2'), 
                       age = c(21,81), 
                       status = c('dead','alive'))
imagePheno(cellExp) <- phenoData
imagePheno(cellExp)
imagePheno(cellExp, expand = TRUE)

## -----------------------------------------------------------------------------
set.seed(51773)

n = 10

cells <- data.frame(row.names = seq_len(n))
cells$x <- runif(n)
cells$y <- runif(n)
cellExp <- SegmentedCells(cells)
cellExp


## -----------------------------------------------------------------------------
cellSum <- cellSummary(cellExp)
head(cellSum)


## -----------------------------------------------------------------------------
sessionInfo()