clustermolepy 0.3.1

Cluster annotation helper for single-cell RNA-seq data in Python

🧬 clustermolepy: Fast Enrichment for Annotating Single-Cell Clusters

clustermolepy is a light weight Python package inspired by the original clustermole R package. It's designed to help you annotate cell clusters from single-cell RNA-seq data using powerful gene set enrichment analysis.

🚀 Key Features

• Enrichr Integration :
  • Direct query of the Enrichr API for gene set enrichment analysis.
  • Multi-threaded get_cell_type_enrichment() for fast cell type enrichment using curated gene set libraries.
• Scanpy Integration :
  • Designed to work seamlessly with Scanpy AnnData objects.
  • Example workflow uses Scanpy for data loading, clustering, and marker gene identification.
• Biomart Integration :
  • Easily convert gene symbols across species using the Ensembl Biomart API.

📦 Installation

You can install the stable release from PyPI using pip:

pip install clustermolepy

Once installed, you can import and use clustermolepy in your Python environment.

🕹️ Quick Usage Example

Here's a simplified example of how to use clustermolepy to annotate cell clusters. For a more detailed walkthrough, check out the Jupyter Notebook in the examples directory!

Example: Interpreting a PBMC Cluster

import scanpy as sc
from clustermolepy.enrichr import Enrichr

# Load PBMC data and cluster
adata = sc.datasets.pbmc3k_processed()
sc.tl.leiden(adata, resolution=0.5, n_iterations=2, flavor='igraph')

# Get top marker genes for a cluster
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
markers = sc.get.rank_genes_groups_df(adata, group='1')
top_genes = markers['names'][:25].tolist()

# Run enrichment
enr = Enrichr(gene_list=top_genes)
df = enr.get_cell_type_enrichment()
print(df.head(3))

This will return a table with top matching cell types across multiple reference libraries like CellMarker, PanglaoDB, Azimuth, and more.

(Example Output - first 3 rows of the table):

|    | term name               |     p-value |   odds ratio |   combined score | overlapping genes                                                                                            |   adjusted p-value |   old p-value |   old adjusted p-value | gene_set                 |
|---:|:------------------------|------------:|-------------:|-----------------:|:-------------------------------------------------------------------------------------------------------------|-------------------:|--------------:|-----------------------:|:-------------------------|
|  0 | B Cells Naive           | 9.79493e-24 |      571.108 |          30257.4 | ['CD79B', 'VPREB3', 'CD74', 'CD79A', 'FCER2', 'TCL1A', 'BANK1', 'LINC00926', 'LY86', 'CD37', 'LTB', 'MS4A1'] |        3.82002e-22 |             0 |                      0 | PanglaoDB_Augmented_2021 |
|  1 | B Cells                 | 9.91341e-23 |      466.235 |          23622.1 | ['CD79B', 'VPREB3', 'CD74', 'CD79A', 'FCER2', 'BANK1', 'HVCN1', 'LY86', 'CXCR4', 'CD37', 'LTB', 'MS4A1']     |        1.93312e-21 |             0 |                      0 | PanglaoDB_Augmented_2021 |
|  2 | B Cell Liver CL:0000236 | 7.54304e-22 |      622.531 |          30277.6 | ['CD79B', 'VPREB3', 'CD74', 'CD79A', 'BANK1', 'HVCN1', 'CXCR4', 'CD37', 'LTB', 'MS4A1']                      |        8.80014e-21 |             0 |                      0 | Tabula_Muris             |

Example: Cross-Species Gene Mapping

from clustermolepy.utils import Biomart

bm = Biomart()
result = bm.convert_gene_names(
    genes=["TP53", "CD4", "FOXP3"],
    from_organism="hsapiens",
    to_organism="mmusculus"
)
print(result)

(Example Output)

{
  'TP53': ['Trp53'],
  'CD4': ['Cd4'],
  'FOXP3': ['Foxp3']
}

📚 Documentation

Check out the Example Notebook for more information!