pyNebulosa

Single-cell data visualization using kernel gene-weighted density estimation.

Due to the sparsity observed in single-cell data (e.g. RNA-seq, ATAC-seq), the visualization of cell features (e.g. gene, peak) is frequently affected and unclear, especially when overlaid with clustering to annotate cell types. pyNebulosa recovers the signal from dropped-out features by incorporating the similarity between cells, allowing a “convolution” of the cell features via weighted kernel density estimation.

Quick start

import scanpy as sc
import pynebulosa as nb

adata = sc.datasets.pbmc3k_processed()

# Single gene density plot
nb.plot_density(adata, "NKG7")

# Multiple genes
nb.plot_density(adata, ["MS4A1", "NKG7", "CST3"])

# Joint density to identify co-expressing cells
nb.plot_density(adata, ["NKG7", "GNLY"], joint=True)

Contents

• Installation
  • From PyPI
  • With scanpy (recommended)
  • Development install
  • Requirements
• Tutorial
  • pyNebulosa: Gene-Weighted Density Estimation for Single-Cell Data
• API Reference
  • Plotting
  • Density estimation
• Comparison with other tools
  • Overview
  • scanpy.tl.embedding_density
  • pyUCell
  • When to use pyNebulosa

Citation

If you use pyNebulosa, please cite:

Alquicira-Hernandez, J., Powell, J.E. Nebulosa recovers single-cell gene expression signals by kernel density estimation. Bioinformatics, 37(16), 2485-2487, 2021. doi:10.1093/bioinformatics/btab003