| from functools import lru_cache
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| from pathlib import Path
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| import numpy as np
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| import pandas as pd
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|
|
| ROOT = Path(__file__).resolve().parents[1]
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| RAW = ROOT / "data" / "raw"
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| EXT = ROOT / "data" / "external"
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| RES = ROOT / "results"
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| RES.mkdir(exist_ok=True)
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|
|
| OX = {"h2o2", "paraquat", "4nqo"}
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| OS = {"sorbitol", "nacl"}
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| AGENTS = ["h2o2", "paraquat", "4nqo", "nacl", "sorbitol"]
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| MIN_NONMISSING = 350
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| SEED = 0
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| TERMS = {"oxidative": ("oxidative stress resistance",),
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| "osmotic": ("hyperosmotic stress resistance", "osmotic stress resistance")}
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| SOURCE_PMIDS = {"18420932", "12140549"}
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|
|
|
|
| def agent(label):
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| parts = label.split(":")
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| return parts[1].strip().lower() if len(parts) > 1 else ""
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|
|
|
|
| @lru_cache(maxsize=None)
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| def _parse(name):
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| with open(RAW / name) as fh:
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| conds = fh.readline().rstrip("\n").split("\t")[1:]
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| genes, rows = [], []
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| for line in fh:
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| f = line.rstrip("\n").split("\t")
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| genes.append(f[0].split(":")[0])
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| rows.append([np.nan if x in ("", "NA") else float(x) for x in f[1:]])
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| return pd.DataFrame(rows, index=genes, columns=conds, dtype=float)
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|
|
|
|
| def read_matrix(name, min_nonmissing=0):
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| Z = _parse(name)
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| rank = pd.DataFrame({"n": Z.notna().sum(axis=1).values, "i": np.arange(len(Z))}, index=Z.index)
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| keep = rank.sort_values(["n", "i"]).groupby(level=0).tail(1).i.sort_values().values
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| Z = Z.iloc[keep]
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| return Z[Z.notna().sum(axis=1) >= min_nonmissing].copy()
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|
|
|
|
| def axes(conds):
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| ag = np.array([agent(c) for c in conds])
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| ox = np.isin(ag, list(OX))
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| os_ = np.isin(ag, list(OS))
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| return ag, ox, os_, ~(ox | os_)
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|
|
|
|
| def hom(filtered=True, min_nonmissing=MIN_NONMISSING):
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| Z = read_matrix("hom.z_result_nm.pub")
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| if filtered:
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| Z = Z[Z.notna().sum(1) >= min_nonmissing]
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| return Z
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|
|
|
|
| def centre(Z, how="median"):
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| if how == "median":
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| m = Z.median(axis=1)
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| elif how == "mean":
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| m = Z.mean(axis=1)
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| else:
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| from scipy.stats import trim_mean
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| m = Z.apply(lambda r: trim_mean(r.dropna(), 0.1), axis=1)
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| return Z.sub(m, axis=0)
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|
|
|
|
| def effects(Zc, ox, os_):
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| return Zc.loc[:, ox].mean(axis=1), Zc.loc[:, os_].mean(axis=1)
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|
|
|
|
| def agent_effects(Zc, ag):
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| return pd.DataFrame({a: Zc.loc[:, ag == a].mean(axis=1) for a in AGENTS})
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|
|
|
|
| def abc():
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| return pd.read_csv(RAW / "abc_transporters.csv")
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|
|
|
|
| @lru_cache(maxsize=None)
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| def phenotypes():
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| rows = [l.rstrip("\n").split("\t") for l in open(RAW / "phenotype_data.tab", encoding="utf-8", errors="ignore")]
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| df = pd.DataFrame([r[:10] for r in rows if len(r) >= 10],
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| columns=["orf", "type", "gene", "sgdid", "ref", "exptype", "mutant", "allele", "strain", "phenotype"])
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| df["pmid"] = df.ref.str.extract(r"PMID:(\d+)", expand=False)
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| return df
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|
|
|
|
| def sgd_labels(stress=None, term=None, exptypes=None, exclude_pmids=SOURCE_PMIDS):
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| df = phenotypes()
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| df = df[df.mutant == "null"]
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| if exptypes is not None:
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| df = df[df.exptype.isin(exptypes)]
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| if exclude_pmids:
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| df = df[~df.pmid.isin(exclude_pmids)]
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| head = df.phenotype.str.rsplit(": ", n=1).str[0]
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| tail = df.phenotype.str.rsplit(": ", n=1).str[-1]
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| keep = head.isin(TERMS[stress]) if stress else head == term
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| d = np.where(tail == "increased", 1, np.where(tail == "decreased", -1, 0))
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| v = pd.Series(d[keep.values], index=df.orf[keep.values].values)
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| v = v[v != 0].groupby(level=0).sum()
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| return np.sign(v[v != 0]).astype(int)
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|
|
