scanpy_plus

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Intro scanpy_plus is a lightweight Python package providing structured functionality through several submodules organized by purpose: preprocessing, tools, plotting, data access, and I/O.

🔧 Features

• 🧬 Additional tools for downstream analysis including SCVI integration, GSEA, and custom UMAP handling.
• ⚙️ Preprocessing helpers for tasks like sex assignment and cell cycle correction.
• 📊 Plotting enhancements with Plotly for interactive UMAPs, ridgeplots, dotplots, and more.
• 🧭 Annotation support with marker-based labeling and mapping to external references (e.g., Dahlin).
• 📂 Access to curated marker sets and reference datasets.
• 💾 Simplified input/output operations for AnnData and SCVI workflows.

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🛠️ Installation

To set up scanpy_plus in a clean environment, follow these steps:

1. Create a new conda environment

conda create -n scanpyp_env python=3.12
conda activate scanpyp_env

2. Install core dependencies

Install scanpy and plotly:

python -m pip install scanpy plotly

3. Install scanpy_plus

Once dependencies are installed, install scanpy_plus:

python -m pip install git+https://github.com/vjbaskar/scanpy_plus.git

Usage Example (optional — will add later)

import scanpy_plus as sp

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Module Overview

Module	Description
scanpy_plus.tl	Tools — custom analysis functions that extend or modify typical Scanpy workflows (e.g., clustering, annotation utilities).
scanpy_plus.pp	Preprocessing tools — extended filtering, normalization, or transformation methods to prepare AnnData objects.
scanpy_plus.pl	Plotting — enhanced or interactive visualizations using Plotly and Matplotlib, including marker plots and UMAPs.
scanpy_plus.io	I/O utilities — simplified functions for reading/writing AnnData or SCVI models, handling metadata, or reloading raw counts.
scanpy_plus.data	Reference data — built-in markers, gene sets, or sample data used in plotting and annotation workflows.

Submodule Overview

🔧 scanpy_plus.tl – Tools

• gsea – Gene set enrichment analysis.
• map_to_dahlin – Cell type mapping to Dahlin reference.
• rankobs – Rank-based observation scoring utilities.
• run_scvi – SCVI model setup and execution.
• tryumap – Custom UMAP wrapper with smart defaults.

⚙️ scanpy_plus.pp – Preprocessing

• assign_sex – Estimate sample sex based on gene expression.
• cellcycle_corr – Correct for cell cycle-related effects.

💾 scanpy_plus.io – Input/Output

• boot – Load/save AnnData or SCVI-related objects with version tracking.

📊 scanpy_plus.pl – Plotting

• haem_markers – Dotplots and expression views of hematopoietic markers.
• histplot – Histogram plotting utilities.
• pca_correlation – Correlate PCA axes with metadata or gene sets.
• perma_plot – Persistent/scaled expression plot handling.
• plotly_umap – Interactive UMAP visualizations with Plotly.
• ridgeplot – Ridge-style density plots.
• splitplot – Grouped faceted plots for categorical comparisons.
• splitscatter – Split scatter plots by group or condition.
• umap3dPlotting – 3D UMAP visualization.
• umap_allobs – Combined UMAP plot of all observations.
• umap_grid – Grid layout of multiple UMAPs for comparison.

📂 scanpy_plus.data – Reference Data

• load – Access reference datasets.

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Example Use Case

import scanpy as sc
import scanpy_plus as sp

adata = sc.read_h5ad("sample.h5ad")

# Run SCVI and visualize
sp.tl.run_scvi(adata)
sp.pl.plotly_umap(adata)

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Resources

• API reference are available in the documentation
• Please use the issues to submit bug reports.
• If you would like to contribute, check out our contributing guide

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License

MIT License. df