sjanpy

Subjacent Analysis Toolkits for Single-Cell Omics in Python

sjanpy extends the Scanpy / AnnData ecosystem with publication-quality visualizations, fast differential expression analysis, and preprocessing utilities for single-cell RNA-seq.

Package Structure

sjanpy follows the Scanpy subpackage convention:

Installation

git clone https://github.com/chansigit/sjanpy.git
cd sjanpy
pip install .

Quick Start

Embedding visualization

import scanpy as sc
from sjanpy.pl import fancy_embedding_pro

adata = sc.datasets.pbmc3k_processed()
fancy_embedding_pro(adata, basis='umap', color='louvain')

Differential expression

from sjanpy.tl import fast_two_group_deg
from sjanpy.pl import plot_volcano

deg = fast_two_group_deg(adata, label_col='louvain', lst1=['B cells'], lst2=['CD4 T cells'])
plot_volcano(deg, logfc_col='log2FC', padj_col='padj')

Nebulosa density

Traditional scatter plots obscure gene expression patterns due to point overlap. Nebulosa uses weighted kernel density estimation to reveal true expression distributions:

from sjanpy.pl import nebulosa_density

nebulosa_density(adata, coord_key='X_umap', gene='CD3D', show=True)

Standard scatter	Nebulosa density

Gene filtering

from sjanpy.pp import filter_human_sc_genes

# Mask artifact genes from HVG selection (predicted, non-coding, IG variable, etc.)
adata = filter_human_sc_genes(adata, mask_hvg_only=True)

Complex dot plot

from sjanpy.pl import complex_dotplot

complex_dotplot(
    adata,
    genes=marker_genes,
    groupby='cell_type',
    z_score=True,
    cluster_rows=True,
    cmap='RdBu_r',
)

Module Reference

sjanpy.pl — Plotting

sjanpy.tl — Tools

sjanpy.pp — Preprocessing

sjanpy.ml — Machine Learning

Dependencies

Core: numpy, pandas, scipy, matplotlib, seaborn, scanpy, anndata, adjustText, statsmodels, scikit-learn

Optional: plotly (3D visualization), torch / h5py (ML dataset building)

License

MIT