File size: 6,042 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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import logging
from typing import Any
import anndata as ad
import numpy as np
import polars as pl
from numpy.typing import NDArray
from pdex import parallel_differential_expression
from scipy.sparse import issparse
from ._evaluator import _build_pdex_kwargs, _convert_to_normlog
logger = logging.getLogger(__name__)
def build_base_mean_adata(
adata: ad.AnnData | str,
counts_df: pl.DataFrame | str | None = None,
pert_col: str = "target_gene",
control_pert: str = "non-targeting",
counts_col: str = "n_cells",
as_delta: bool = False,
allow_discrete: bool = False,
output_path: str | None = None,
output_de_path: str | None = None,
batch_size: int = 1000,
num_threads: int = 1,
de_method: str = "wilcoxon",
pdex_kwargs: dict[str, Any] = {},
) -> ad.AnnData:
if isinstance(adata, str):
logger.info(f"Reading adata from path: {adata}")
adata = ad.read_h5ad(adata)
# Convert to normalized log space if necessary
_convert_to_normlog(adata=adata, allow_discrete=allow_discrete)
counts = (
_load_counts_df(
counts_df=counts_df,
pert_col=pert_col,
control_pert=control_pert,
counts_col=counts_col,
)
if counts_df is not None
else _build_counts_df_from_adata(
adata=adata,
pert_col=pert_col,
control_pert=control_pert,
counts_col=counts_col,
)
)
baseline = _build_pert_baseline(
adata=adata, pert_col=pert_col, control_pert=control_pert, as_delta=as_delta
)
obs = (
counts.select([pl.col(pert_col).repeat_by(counts_col)])
.explode(pert_col)
.to_pandas()
)
obs.index = obs.index.astype(str).str.replace("^", "p.", regex=True)
logger.info("Assembling baseline adata from perturbation mean")
baseline_adata = ad.AnnData(
X=np.full(
(int(counts[counts_col].sum()), baseline.size),
baseline,
),
var=adata.var,
obs=obs,
)
logger.info("Concatenating baseline adata with controls from original adata")
baseline_adata = ad.concat(
[baseline_adata, adata[adata.obs[pert_col] == control_pert]]
)
if output_path is not None:
logger.info(f"Saving baseline data to {output_path}")
baseline_adata.write_h5ad(output_path)
if output_de_path is not None:
logger.info("Calculating differential expression")
pdex_kwargs = _build_pdex_kwargs(
groupby_key=pert_col,
reference=control_pert,
num_workers=num_threads,
metric=de_method,
batch_size=batch_size,
allow_discrete=allow_discrete,
pdex_kwargs=pdex_kwargs,
)
frame = parallel_differential_expression(
adata=baseline_adata,
**pdex_kwargs,
)
logger.info(f"Saving differential expression results to {output_de_path}")
frame.write_csv(output_de_path)
return baseline_adata
def _load_counts_df(
counts_df: pl.DataFrame | str,
pert_col: str = "target_gene",
control_pert: str = "non-targeting",
counts_col: str = "n_cells",
) -> pl.DataFrame:
if isinstance(counts_df, str):
logger.info(f"Loading counts from {counts_df}")
counts_df = pl.read_csv(counts_df)
if pert_col not in counts_df.columns:
raise ValueError(
f"Column '{pert_col}' not found in counts_df: {counts_df.columns}"
)
if counts_col not in counts_df.columns:
raise ValueError(
f"Column '{counts_col}' not found in counts_df: {counts_df.columns}"
)
logger.info(f"Filtering out counts from {control_pert}")
return counts_df.filter(
pl.col(pert_col) != control_pert # drop control pert
)
def _build_counts_df_from_adata(
adata: ad.AnnData,
pert_col: str = "target_gene",
control_pert: str = "non-targeting",
counts_col: str = "n_cells",
) -> pl.DataFrame:
if pert_col not in adata.obs.columns:
raise ValueError(
f"Column '{pert_col}' not found in adata.obs: {adata.obs.columns}"
)
if control_pert not in adata.obs[pert_col].unique():
raise ValueError(
f"Control pert '{control_pert}' not found in adata.obs[{pert_col}]: {adata.obs[pert_col].unique()}"
)
logger.info("Building counts DataFrame from adata")
return (
pl.DataFrame(adata.obs)
.group_by(pert_col)
.len()
.rename({"len": counts_col})
.filter(pl.col(pert_col) != control_pert)
)
def _build_pert_baseline(
adata: ad.AnnData,
pert_col: str = "target_gene",
control_pert: str = "non-targeting",
as_delta: bool = False,
) -> NDArray[np.float64]:
if pert_col not in adata.obs.columns:
raise ValueError(
f"Column '{pert_col}' not found in adata.obs: {adata.obs.columns}"
)
unique_perts = adata.obs[pert_col].unique()
if control_pert not in unique_perts:
raise ValueError(
f"Control pert '{control_pert}' not found in unique_perts: {unique_perts}"
)
logger.info("Building perturbation-level means")
pert_means = (
pl.DataFrame(
adata.X if not issparse(adata.X) else adata.X.toarray() # type: ignore
)
.with_columns(pl.Series(pert_col, adata.obs[pert_col]))
.group_by(pert_col)
.mean()
)
names = pert_means.drop_in_place(pert_col).to_numpy()
pert_mask = names != control_pert
pert_matrix = pert_means.to_numpy()
if as_delta:
logger.info("Calculating delta from control means")
delta = pert_matrix[pert_mask] - pert_matrix[~pert_mask]
logger.info("Calculating mean delta")
mean_delta = delta.mean(axis=0)
return mean_delta
else:
logger.info("Calculating mean of perturbation-level means")
mean_pert = pert_matrix.mean(axis=0)
return mean_pert
|