README.md

In ellispatrick/SegmentedCellExperiment: S4 Class for segmented spatial 'omics data

SegmentedCellExperiment

A SegmentedCellExperiment is an object designed to store data from imaging cytometry (FISH, IMC, CycIF, spatial transcriptomics, ... ) that has already been segmented and reduced to individual cells. A SegmentedCellExperiment extends DataFrame and defines methods that take advantage of DataFrame nesting to represent various elements of cell-based experiments with spatial orientation that are commonly encountered. This object is able to store information on a cell's spatial location, cellType, morphology, intensity of gene/protein marks as well as image level phenotype information. Ideally this type of data can be used for cell clustering, point process models or nearest neighbour analysis.

Installation

You can install the released version of SegmentedCellExperiment from GitHub with:

devtools::install_github("ellispatrick/SegmentedCellExperiment")

Example

Here we create toy data that can be used to make a SegmentedCellExperiment object

set.seed(51773)

n = 10

cells <- data.frame(row.names = seq_len(n))
cells$cellID <- seq_len(n)
cells$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 = '_')

We can then create a SegmentedCellExperiment object.

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

Extract location information

loc <- location(cellExp)
head(loc)

Extract morphology information

morph <- morphology(cellExp)
head(morph)

ellispatrick/SegmentedCellExperiment documentation built on Feb. 24, 2020, 3:14 p.m.