cellColor 0.1.2

An app for exploring spatial transcriptomics and cell segmentations by overlaying data

Gene Visualization Tool (cellColor)

An interactive desktop application to visualize spatial transcriptomics data, cell segmentation masks, and microscopy images.
Ideal for verifying cell segmentation accuracy and exploring spatial gene expression patterns.

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👥 Credits & Project Team

Developer: Anthea Guo

Mentor: Kushal Nimkar

Principal Investigator (PI): Prof. Karthik Shekhar

✨ Features

• Image Loading & Zooming

  • Load tissue/microscopy images.
  • Zoom into image regions by clicking and dragging.
  • Reset zoom to full view.

• Cellpose Segmentation Overlay

  • Load and overlay Cellpose-generated segmentation masks (colored regions) or outlines (borders).
  • Smooth zooming with cached mask resizing.

• Transcript Visualization

  • Import detected transcript coordinates (e.g., CSV/TSV with x, y, gene fields).
  • Apply transformation matrices to align transcript data with image.
  • Select genes from a dropdown to overlay their transcript locations.
  • Manage multiple gene overlays via a scrollable panel.

• Single-Cell Integration

  • Load AnnData cell center positions (.h5ad files).
  • Toggle cell centers on/off with customizable color and size.

• User-Friendly Toolbar

  • Intuitive toggle buttons to control overlays and zoom actions.
  • Status bar for live feedback.
  • Organized layout with collapsible frames for easy navigation.

• Data Alignment

  • Supports loading transformation matrices to align transcript data accurately with images.

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🚀 Installation

Option 1: Local Development (Editable Mode)

Clone the repo and install in dev mode:

git clone https://github.com/crocodile27/cellColor.git
cd cellColor
conda create -n cellcolor python=3.10
conda activate cellcolor
pip install -e .

Run the app locally:

cellColor

Option 2: Install via PyPI (v0.1.0)

Install directly from PyPI:

pip install cellColor

This corresponds to the release published on September 1, 2025, version 0.1.0 (pypi.org ).

Launching the App cellColor

📂 Supported Data Formats

Image: .png, .jpg, .tif, etc.

Cellpose Masks: .npy arrays or image mask formats.

Detected Transcripts: CSV/TSV containing x, y, gene columns.

Transformation Matrix: CSV/TSV defining alignment matrix.

AnnData: .h5ad format with cell coordinate metadata.

🧪 Example Workflow (Happy Path)

Open the app: cellColor.

File → Load Image to display your tissue section.

File → Load Detected Transcripts and Transformation Matrix.

File → Load Cellpose Masks, then enable Show Cellpose Masks.

Choose a gene from the dropdown; its transcript spots should appear.

Optionally, File → Load AnnData Cell Centers and enable Show Cell Centers.

Zoom in on interesting areas; use Reset Zoom to go back.