simpleSeg

Workflows

Standard Workflow

Segment multiplexed images to identify cell boundaries, extract single-cell features, and normalize marker intensities.

library(simpleSeg)
library(cytomapper)
library(EBImage)

# Load example data path
pathToImages <- system.file("extdata", package = "simpleSeg")
imageDirs <- dir(pathToImages, "Point", full.names = TRUE)
names(imageDirs) <- dir(pathToImages, "Point", full.names = FALSE)
files <- lapply(imageDirs, list.files, pattern = "tif", full.names = TRUE)
images <- lapply(files, EBImage::readImage, as.is = TRUE)
images <- cytomapper::CytoImageList(images)
mcols(images)$imageID <- names(images)

# Segment images
masks <- simpleSeg::simpleSeg(images, nucleus = "HH3", transform = "sqrt")

# Summarise cell features
cellSCE <- cytomapper::measureObjects(masks, images, img_id = "imageID")

# Normalize cells
cellSCE <- normalizeCells(
  cellSCE,
  assayIn = "counts",
  assayOut = "norm",
  imageID = "imageID",
  transformation = "sqrt",
  method = c("trim99", "minMax")
)

Input: Multiplexed TIFF images (as CytoImageList); Output: Segmented masks (CytoImageList) and normalized single-cell feature matrices (SingleCellExperiment).

When to Use

• Segmenting multiplexed cellular images (e.g., MIBI-TOF) using a watershed-based approach with simpleSeg::simpleSeg().
• Extracting single-cell marker intensities and morphological features into a SingleCellExperiment using cytomapper::measureObjects().
• Normalizing and transforming extracted single-cell marker intensities using normalizeCells().

When NOT to Use

• For segmenting non-imaging single-cell data (like standard scRNA-seq), use scran or Seurat because simpleSeg is specifically designed for multiplexed imaging data.
• For highly complex deep-learning-based cell segmentation, use external tools like Cellpose or Mesmer because simpleSeg relies on classical watershed algorithms.

Data Requirements

• Multiplexed cellular images loaded as an Image, list of Images, or CytoImageList (from cytomapper).
• Images should contain a nuclear marker channel (e.g., "HH3") for watershed seeding.

Key Parameters

• nucleus: The name or index of the channel to use as the nuclear marker.
• transform (NULL): Image transformation to apply before segmentation (e.g., "sqrt").
• cores (1): Number of cores to use for parallel processing.
• watershed ("combine"): Method for watershedding ("distance", "intensity", or "combine").
• cellBody ("dilation"): Method for cell body identification ("dilation", "discModel", "marker", or "None").
• assayIn ("counts"): Input assay name in normalizeCells().
• assayOut ("norm"): Output assay name in normalizeCells().

Best Practices

• Visualise segmentation performance using EBImage::display(colorLabels(masks[[1]])) or cytomapper::plotPixels() to verify cell boundaries.
• Apply a transformation like "sqrt" to the nuclear channel to improve watershed segmentation on highly skewed intensity distributions.
• Perform quality control on normalization by plotting marker density distributions (e.g., using ggplot2 and geom_density()) before and after running normalizeCells().

Common Pitfalls

• Highly skewed marker intensities: Raw intensities can be extremely skewed, making clustering difficult; fix this by applying normalizeCells() with method = c("trim99", "minMax").
• Incorrect nuclear channel name: Specifying a nuclear marker that does not exist in the image channels will cause simpleSeg to fail; verify channel names in the CytoImageList first.

Alternatives

• EBImage: For low-level, highly flexible image processing and manual watershedding.
• cytomapper: For pixel-level visualization and measuring objects.
• Seurat: For downstream clustering and classification of the extracted single-cell features.

Citations

• Nicholls et al. (2026), simpleSeg package vignette.
• Risom et al. (2022), Cell (for the example DCIS MIBI-TOF dataset).

References

• Homepage: bioconductor.org/packages/simpleSeg
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/simpleSeg/inst/doc/simpleSeg.html