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config.py
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config.py
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#!/usr/bin/env python
"""
A module to provide the boilerplate needed for all the analysis.
"""
import json
import re
from functools import partial
import numpy as np # type: ignore[import]
import pandas as pd # type: ignore[import]
import matplotlib # type: ignore[import]
import matplotlib.pyplot as plt # type: ignore[import]
import seaborn as sns # type: ignore[import]
from imc import Project
from imc.types import Path
from seaborn_extensions import swarmboxenplot # , activate_annotated_clustermap
# activate_annotated_clustermap()
swarmboxenplot = partial(swarmboxenplot, test_kws=dict(parametric=False))
def set_prj_clusters(
prefix="roi_zscored.filtered.", cluster_str="cluster_1.0", aggregated=True
):
# remove any existing assignment
# and revert to what's on disk (clusters labeled by integers)
prj._clusters = None
new_labels = json.load(open("metadata/cluster_names.json"))[
f"{prefix};{cluster_str}"
]
new_labels = {int(k): v for k, v in new_labels.items()}
for k in prj.clusters.unique():
if k not in new_labels:
new_labels[k] = "999 - ?()"
new_labels_agg = {
k: "".join(re.findall(r"\d+ - (.*) \(", v))
for k, v in new_labels.items()
}
# add numbering for consistent plotting across ROIs
ll = pd.Series(sorted(np.unique(list(new_labels_agg.values()))))
lll = ll.index.astype(str).str.zfill(2) + " - " + ll
lll.index = ll
new_labels_agg = pd.Series(new_labels_agg).replace(lll.to_dict()).to_dict()
# prj.clusters
prj.set_clusters(
prj.clusters.replace(new_labels_agg if aggregated else new_labels),
write_to_disk=False,
)
# constants
channels_exclude_strings = [
"<EMPTY>",
"190BCKG",
"80ArAr",
"129Xe",
] # "DNA", "CD11b"]
roi_exclude_strings = [
"20200701_COVID_11_LATE-01",
"20200701_COVID_11_LATE-09",
"20200701_COVID_11_LATE-10",
]
attributes = [
"name",
"disease",
"phenotypes",
"acquisition_id",
"acquisition_date",
]
figkws = dict(dpi=300, bbox_inches="tight", pad_inches=0, transparent=False)
# directories
metadata_dir = Path("metadata")
data_dir = Path("data")
processed_dir = Path("processed")
results_dir = Path("results")
qc_dir = results_dir / "qc"
# lists of channels
panel_markers = pd.read_csv("metadata/panel_markers.COVID19-2.csv", index_col=0)
illustration_channel_list = json.load(
open(metadata_dir / "illustration_markers.json")
)
# Initialize project
prj = Project(metadata_dir / "samples.csv", name="COVID19-2")
# Filter channels and ROIs
channels = (
prj.channel_labels.stack().drop_duplicates().reset_index(level=1, drop=True)
)
channels_exclude = channels.loc[
channels.str.contains(r"^\d")
| channels.str.contains("|".join(channels_exclude_strings))
].tolist() + ["<EMPTY>(Sm152-In115)"]
channels_include = channels[~channels.isin(channels_exclude)]
cell_type_channels = panel_markers.query("cell_type == 1").index.tolist()
for roi in prj.rois:
roi.set_channel_exclude(channels_exclude)
for s in prj:
s.rois = [r for r in s if r.name not in roi_exclude_strings]
# # ROIs
roi_names = [x.name for x in prj.rois]
roi_attributes = (
pd.DataFrame(
np.asarray(
[getattr(r.sample, attr) for r in prj.rois for attr in attributes]
).reshape((-1, len(attributes))),
index=roi_names,
columns=attributes,
)
.rename_axis(index="roi")
.rename(columns={"name": "sample"})
)
# # Samples
sample_names = [x.name for x in prj.samples]
sample_attributes = (
pd.DataFrame(
np.asarray(
[getattr(s, attr) for s in prj.samples for attr in attributes]
).reshape((-1, len(attributes))),
index=sample_names,
columns=attributes,
)
.rename_axis(index="sample")
.drop(["name"], axis=1)
)
cat_order = {
"disease": ["Healthy", "FLU", "ARDS", "COVID19"],
"phenotypes": [
"Healthy",
"Flu",
"ARDS",
"Pneumonia",
"COVID19_early",
"COVID19_late",
],
# "acquisition_date": sorted(list(set([str(r.sample.acquisition_date) for r in prj.rois]))),
}
for df in [roi_attributes, sample_attributes]:
for cat, order in cat_order.items():
df[cat] = pd.Categorical(df[cat], categories=order, ordered=True)
df["acquisition_date"] = df["acquisition_date"].astype(int)
df["acquisition_date"] -= df["acquisition_date"].min()
# Color codes
colors = dict()
# # Diseases
colors["disease"] = np.asarray(sns.color_palette())[[2, 0, 1, 3]]
["Healthy", "Flu", "ARDS", "COVID"]
# # Phenotypes
colors["phenotypes"] = np.asarray(sns.color_palette())[[2, 0, 1, 5, 4, 3]]
# Output files
metadata_file = metadata_dir / "clinical_annotation.pq"
quantification_file = results_dir / "cell_type" / "quantification.pq"
quantification_file_sum = results_dir / "cell_type" / "quantification.sum.pq"
gating_file = results_dir / "cell_type" / "gating.pq"
positive_file = results_dir / "cell_type" / "gating.positive.pq"
positive_count_file = results_dir / "cell_type" / "gating.positive.count.pq"
quantification_file = results_dir / "cell_type" / "quantification.pq"
h5ad_file = results_dir / "cell_type" / "anndata.all_cells.processed.h5ad"
counts_file = results_dir / "cell_type" / "cell_type_counts.pq"
counts_agg_file = results_dir / "cell_type" / "cell_type_counts.aggregated_pq"
roi_areas_file = results_dir / "roi_areas.csv"
sample_areas_file = results_dir / "sample_areas.csv"