recursieve 1.0.0

RecurSieve is an iterative gene selection algorithm that identifies discriminative genes between two biological groups in single-cell datasets. It uses an ensemble learning approach with a blend of random forest classifiers and gaussian mixture models to progressively select genes that best separate the groups, collapsing selected genes into a lower dimensional representational axis at each iteration.

Installation

Install from PyPI:

pip install recursieve

RecurSieve requires Python 3.12 or newer.

To install the development version from GitHub:

pip install git+https://github.com/ArpiarSaundersLab/RecurSieve.git

Usage

import anndata as ad
from recursieve import recursieve

adata = ad.read_h5ad("data/alz.h5ad")
model = recursieve(
    adata=adata,
    group1="control",
    group2="disease",
    field_name="condition",
    max_iterations=100,
)

# all genes found
panel = model.genes

# genes unique to RecurSieve
non_de_genes = model.unique_gene_panel 

Parameters

Robustness checks

seed_stability checks whether the same genes are selected with different random seeds. coexpression_null checks whether the selected genes are found more often than by chance, by rerunning on randomized data.

Each seed and each shuffle is a full RecurSieve run, so these checks take much longer than a single run. On a 10-core laptop, 25 shuffles took about 1.5 hours for 25,000 cells and about 3.5 hours for 50,000 cells.

Both methods return a dictionary of pandas DataFrames and do not write files. The example below runs both checks and saves the main tables as CSV files.

seeds = model.seed_stability(n_seeds=10)
null = model.coexpression_null(n_shuffles=25)

seeds["gene_frequency"].to_csv("seed_gene_frequency.csv", index=False)
null["pvalues"].to_csv("null_pvalues.csv", index=False)
null["gene_frequency"].to_csv("null_gene_frequency.csv", index=False)

License

The RecurSieve software is released under the MIT License. The data, figures, and tables in manuscript/ are dedicated to the public domain under CC0 1.0.

Citation

RecurSieve: An Ensemble Learning Algorithm for Single-Cell Feature Selection Captures Subtle Gene Expression Patterns
bioRxiv link coming soon