# Simpleseg

> simpleSeg

- Skill: `biomate-ai/simpleseg` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/simpleseg`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/simpleseg/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/simpleseg

---


# simpleSeg

## Workflows

### Standard Workflow

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

```r
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 `Image`s, 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