cenfind 0.9.3

Score cells for centrioles in IF data

cenfind

A command line interface to score cells for centrioles.

Introduction

cenfind is a command line interface to detect and assign centrioles in immunofluorescence images of human cells. Specifically, it orchestrates:

• the z-max projection of the raw files;
• the detection of centrioles;
• the detection of the nuclei;
• the assignment of the centrioles to the nearest nucleus.

Installation

1. Download cenfind with:

python -m venv test_cenfind
source test_cenfind/bin/activate
pip install cenfind
pip install tensorflow
pip install -e projects/spotipy

5. As of now, you need to install the spotipy package from the git repository https://github.com/maweigert/spotipy:

git clone [e-mail]:maweigert/spotipy.git

6. Add manually the package spotipy

pip install -e ../spotipy/

7. Check that cenfind's programs are correctly installed by running:

squash --help

Basic usage

Before scoring the cells, you need to prepare the dataset folder. cenfind assumes a fixed folder structure. In the following we will assume that the .ome.tif files are all immediately in raw/. Each field of view is a z-stack containing 4 channels (0, 1, 2, 3). The channel 0 contains the nuclei and the channels 1-3 contains centriolar markers.

<project_name>/
└── raw/

2. Run prepare to initialise the folder with a list of fields and output folders:

prepare /path/to/dataset 1 2 3 [--pixel_size, [--projection_suffix]]
prepare /path/to/dataset <list channels of centrioles, like 1 2 3, (0 should be the nucleus channel)>

2. Run squash with the argument of the path to the project folder and the suffix of the raw files. projections/ is populated with the max-projections *_max.tif files.

squash path/to/dataset

3. Run score with the arguments source and the index of the nuclei channel (usually 0 or 3).

score path model channel_nuclei channels factor

4. Check that the predictions are satisfactory by looking at the folder outlines and at the results/scores.csv.

API

cenfind consists of two core classes: Dataset and Field. The class Dataset represents a collection of fields with identical pixel size, channels, and cell type. The class Field represents a field of view and is responsible for calling the detection procedure and for managing (storing and writing) the predictions.

Using those two objects, cenfind should

• detect centrioles (data, model) => points,
• extract nuclei (data, model) => contours,
• assign centrioles to nuclei (contours, points) => pairs
• outline centrioles and nuclei (data, points) => image
• create composite vignettes (data) => composite_image
• flag partial nuclei (contours, tolerance) => contours
• compare predictions with annotation (points, points) => metrics_namespace