pcm-penne 1.0.0

A PyTorch implementation of the PENNE model for inferring gene expression from phase-contrast microscopy images

PENNE - Phase-to-Expression Neural Network Estimator

Implementation of PENNE, a method for inferring transcriptome from phase-contrast microscopy images.

Read the paper and the documentations

Installing PENNE

Installing using PyPI

PENNE is available on the Python Package Index (PyPI) to be installed with pip directly. It is strongly recommended to create a virtual environment before installing, as this is built on numpy 1.x, which means it will probably fail if you try to install it in an environment that is already built with numpy 2.x due to the massive architecture change for numpy.

To install, run:

virtualenv --no-download penne
source penne/bin/activate 
pip install pcm-penne

Installing Locally

Alternatively, you may also install PENNE from the GitHub repository directly. To do that, first create a virtual Python environment and install PENNE locally.

virtualenv --no-download penne
source penne/bin/activate 
git clone https://github.com/schwartzlab-methods/penne
cd penne
pip install .

Inferences using PENNE

An example workflow of how to use PENNE to infer gene expression from your phase-contrast microscopy images can be found at ./tutorials/inference.ipynb. You may supply your own checkpoint files, but if none is supplied, PENNE will automatically download it from the official GitHub repository.

Inferences with the CLI tool

Alternatively, you can also run inferences using the CLI interface to perform quick inferences. To do this, after you have installed PENNE, run:

python3 penne --input path_to_directory_with_your_images \
--output path_to_directory_to_save_the_images

You can also optionally use --penne_checkpoint, --spaghetti_checkpoint, and --gene_names if you are not using the default pre-trained model.

Inferences with Docker

For a dependency-free and reproducible environment, the CLI inference tool of PENNE is available as a Docker image. To use it, ensure you have Docker installed, then run:

Option 1: Use the Pre-built Image from Docker Hub

The official image is hosted on Docker Hub.

1. Pull the latest image:

   docker pull yinnikun/penne:latest
   

2. Run Inference:

   To run inference, you need to mount a local directory into the container. This directory should contain your input images and the model checkpoint. The container will write the output images back to this same directory.

   Let's say your local data is organized as follows:

   /path/to/your/data/
   ├── inputs/
   │   ├── image1.tif
   │   └── image2.tif
   ├── penne.ckpt <-- optional, if not supplied it will be downloaded
   ├── spaghetti.ckpt <-- optional, if not supplied it will be downloaded
   └── outputs/  <-- This will be created
   

   Execute the following command:

   docker run --rm -v "/path/to/your/data:/data" yinnikun/penne:latest \
     --input /data/inputs \
     --penne_checkpoint /data/penne.ckpt \
     --spaghetti_checkpoint /data/penne.ckpt
   

   • --rm: Automatically removes the container when it exits.
   • -v "/path/to/your/data:/data": Mounts your local data directory into the /data directory inside the container. Remember to use absolute paths.

Option 2: Build the Image Locally

You can also build the Docker image directly from the dockerfile in this repository.

1. Build the image:

   docker build -t penne:latest .
   

2. Run Inference: The docker run command is the same as above, just replace the image name:

   docker run --rm -v "/path/to/your/data:/data" penne:latest \
     --input /data/inputs \
     --output /data/outputs \
     --penne_checkpoint /data/penne.ckpt \
     --spaghetti_checkpoint /data/penne.ckpt
   

Training your own model

You can also train your own model to perform the inferences. See the documentations for the details on how to use the TrainPenne class.