scPDA: denoising protein expression in droplet-based single-cell data
scPDA: denoising protein expression in droplet-based single-cell data
This folder contains the scPDA package and all the code needed to reproduce the results and figures in the scPDA manuscript. Folder Explanation: data: Four datasets (in .rds format) used in the Results section of the manuscript. code: Scripts that apply various protein‐count denoising methods (GMM, DSB, scAR, DecontPro, scPDA) to each dataset. Results are saved under the “results” folder. results: Denoised count outputs for each dataset and each method. fig_reprod: Scripts to regenerate figures in the manuscript and supplementary materials. scPDA: The development version of the scPDA Python package (installable via “pip install -e scPDA”)
- University of Notre Dame United States
Computational Biology, Multiomics, Unsupervised Machine Learning
Computational Biology, Multiomics, Unsupervised Machine Learning
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!