parallel differential expression for single-cell perturbation sequencing
Add to your pyproject.toml file with uv
uv add pdexThis is a python package for performing parallel differential expression between multiple groups and a control.
It is optimized for very large datasets and very large numbers of perturbations.
It makes use of shared memory to parallelize the computation to a high number of threads and minimizes the IPC between processes to reduce overhead.
It supports the following metrics:
- Wilcoxon Rank Sum
- Anderson-Darling
- T-Test
import anndata as ad
import numpy as np
import pandas as pd
from pdex import parallel_differential_expression
PERT_COL = "perturbation"
CONTROL_VAR = "control"
N_CELLS = 1000
N_GENES = 100
N_PERTS = 10
MAX_UMI = 1e6
def build_random_anndata(
n_cells: int = N_CELLS,
n_genes: int = N_GENES,
n_perts: int = N_PERTS,
pert_col: str = PERT_COL,
control_var: str = CONTROL_VAR,
) -> ad.AnnData:
"""Sample a random AnnData object."""
return ad.AnnData(
X=np.random.randint(0, MAX_UMI, size=(n_cells, n_genes)),
obs=pd.DataFrame(
{
pert_col: np.random.choice(
[f"pert_{i}" for i in range(n_perts)] + [control_var],
size=n_cells,
replace=True,
),
}
),
)
def main():
adata = build_random_anndata()
# Run pdex with default metric (wilcoxon)
results = parallel_differential_expression(
adata,
reference=CONTROL_VAR,
groupby_key=PERT_COL,
)
assert results.shape[0] == N_GENES * N_PERTS
# Run pdex with alt metric (anderson)
results = parallel_differential_expression(
adata,
reference=CONTROL_VAR,
groupby_key=PERT_COL,
metric="anderson"
)
assert results.shape[0] == N_GENES * N_PERTS