scPDA: Denoising Protein Expression in Droplet-Based Single-Cell Data

scPDA is a VAE-based neural network for the task of denoising single-cell surface protein abundance measured by droplet-based technologies such as CITE-seq.

Unlike most currently established methods, scPDA does not require empty droplets. scPDA establishes a probabilistic model for raw count data, and shows a great computational efficiency.

For more details read our manuscript

Installation

pip install scpda

Demo

A demo is available in the demo folder, along with the example data.

Main API

Below is an example that includes main APIs to train scPDA.

from scPDA import Denoiser

# please prepare the protein counts raw_counts and the estimated background mean mu1 (torch.tensor)
model = Denoiser(raw_counts=raw_counts_tensor,  bg_mean=mu1_tensor)
model.train()
model.inference()

# The background probability (model.pi) and denoised counts (model.denoised_counts) are returned
pi = model.pi
denoised_cts = model.denoised_counts