scpda 0.3.0

Single Cell Protein Counts Denoising

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

Main API

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

from scPDA import model

# please prepare the protein counts dsb_counts_tensor (torch.tensor) and the estimated background mean dsb_mu1_tensor (torch.tensor)
scPDA = model(raw_counts=dsb_counts_tensor, bg_mean=dsb_mu1_tensor)
scPDA.train()
scPDA.inference()

# The estimated mu1, mu2, theta1, theta2, pi (background probability) are returned
mu1 = scPDA.mu1
mu2 = scPDA.mu1 * scPDA.alpha
theta1 = scPDA.theta1
theta2 = scPDA.theta2
pi = scPDA.pi