File size: 2,112 Bytes
0161e74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import anndata as ad
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
PERT_COL = "perturbation"
CELLTYPE_COL = "celltype"
CONTROL_VAR = "control"
N_CELLS = 1000
N_GENES = 100
N_PERTS = 10
N_CELLTYPES = 3
MAX_UMI = 1e6
NORM_TOTAL = 1e4
RANDOM_SEED = 42
OUTDIR = "TEST_OUTPUT_DIRECTORY"
def build_random_anndata(
n_cells: int = N_CELLS,
n_genes: int = N_GENES,
n_perts: int = N_PERTS,
n_celltypes: int = N_CELLTYPES,
pert_col: str = PERT_COL,
celltype_col: str = CELLTYPE_COL,
control_var: str = CONTROL_VAR,
random_state: int = RANDOM_SEED,
as_sparse: bool = False,
normlog: bool = True,
normtotal: int | float = NORM_TOTAL,
) -> ad.AnnData:
"""Sample a random AnnData object."""
if random_state is not None:
np.random.seed(random_state)
# Randomly sample a matrix
matrix = np.random.randint(0, int(MAX_UMI), size=(n_cells, n_genes))
# Normalize and log transform if required
if normlog:
matrix = matrix / matrix.sum(axis=1, keepdims=True) * normtotal
matrix = np.log1p(matrix)
# Convert to sparse if required
if as_sparse:
matrix = csr_matrix(matrix)
return ad.AnnData(
X=matrix,
obs=pd.DataFrame(
{
pert_col: np.random.choice(
[f"pert_{i}" for i in range(n_perts)] + [control_var],
size=n_cells,
replace=True,
),
celltype_col: np.random.choice(
[f"celltype_{i}" for i in range(n_celltypes)],
size=n_cells,
replace=True,
),
},
index=np.arange(n_cells).astype(str),
),
)
def downsample_cells(
adata: ad.AnnData,
fraction: float = 0.5,
) -> ad.AnnData:
"""Downsample cells in an AnnData object.
Copies the output to avoid memory overlaps.
"""
assert 0 <= fraction <= 1, "Fraction must be between 0 and 1"
mask = np.random.rand(adata.shape[0]) < fraction
return adata[mask, :].copy()
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