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Custom-made graphical user interface (GUI) application written in Python which allows users to view single cell images in a grid layout. Users can label and save a phenotype for each cell and then export the data.

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singlecelltool

Custom-made graphical user interface (GUI) application that allows users to view and label single cell images on a grid layout. Users can save a phenotype for each cell and then export the data.

This tool is used in the paper: "Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability"

Mojca Mattiazzi Usaj, Nil Sahin, Helena Friesen, Carles Pons, Matej Usaj, Myra Paz Masinas, Ermira Shuteriqi, Aleksei Shkurin, Patrick Aloy, Quaid Morris, Charles Boone, and Brenda J. Andrews

Note: If input is a multi-frame image, the tool will display the first frame by default.

OS compatibility

Tested on: Linux, macOS, Windows

Prerequisites

Python 2.7 or 3: https://www.python.org/downloads

Installation and Usage

Clone the repository

git clone https://github.com/BooneAndrewsLab/singlecelltool.git
cd singlecelltool

Install required packages

pip install -r requirements.txt

or if using the Anaconda Python distribution, create a new environment with the dependencies (recommended):

conda create --name singlecelltool_env --file requirements.txt
source activate singlecelltool_env

Run the application

python singlecelltool.py

User input requirements

  • Single cell data file - a spreadsheet containing the single cell information such as cell ID (if available), image path location, cell coordinates, and initial label (if available). The CSV or excel file should strictly follow the order of column information: cell ID (optional), image path, x-coordinate, y-coordinate, and initial label (optional).

  • Phenotype list - a file containing a list of all possible phenotype or label

  • Index minimum - index of the first cell to be included in the analysis. This is optional. By default, the minimum is set to 1 which means it will display cells starting from the first item on the input file.

  • Index maximum - index of the last cell to be included in the analysis. This is optional. By default, the maximum is set to the total number of cells from the input file.

  • Display limit - number of cells to be displayed on a single page. The default is 20.

  • Crop size - Pixel size to be used in cropping single cells from the image. The default is 64.

Note: For big input files (i.e. more than 1000 cells), it is strongly recommended to break up your analysis into batches by setting the index minimum and maximum values. It will also be great to assign unique cell IDs for easy tracking of the single cells

Output

The output is a CSV file containing all the labeled single cells.

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Custom-made graphical user interface (GUI) application written in Python which allows users to view single cell images in a grid layout. Users can label and save a phenotype for each cell and then export the data.

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