DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
conda >= v22 through Anaconda or miniconda.
## Installation
# Create and activate virtual environment
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab
conda install -y jupyter
## Tutorials to get started
# Go to tutorials directory within DISSECT
cd DISSECT/tutorials
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number = 0 # Using only the first GPU
tf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Interactive tutorials including required data are available as part of this repository at Tutorials.
Below are the static versions of these tutorials.To get answers quickly for a problem or feature request, please open an issue on GitHub.