File size: 3,565 Bytes
638ea38 fe11dd9 638ea38 | 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 | from functools import lru_cache
from pathlib import Path
import numpy as np
import pandas as pd
ROOT = Path(__file__).resolve().parents[1]
RAW = ROOT / "data" / "raw"
EXT = ROOT / "data" / "external"
RES = ROOT / "results"
RES.mkdir(exist_ok=True)
OX = {"h2o2", "paraquat", "4nqo"}
OS = {"sorbitol", "nacl"}
AGENTS = ["h2o2", "paraquat", "4nqo", "nacl", "sorbitol"]
MIN_NONMISSING = 350
SEED = 0
TERMS = {"oxidative": ("oxidative stress resistance",),
"osmotic": ("hyperosmotic stress resistance", "osmotic stress resistance")}
SOURCE_PMIDS = {"18420932", "12140549"}
def agent(label):
parts = label.split(":")
return parts[1].strip().lower() if len(parts) > 1 else ""
@lru_cache(maxsize=None)
def _parse(name):
with open(RAW / name) as fh:
conds = fh.readline().rstrip("\n").split("\t")[1:]
genes, rows = [], []
for line in fh:
f = line.rstrip("\n").split("\t")
genes.append(f[0].split(":")[0])
rows.append([np.nan if x in ("", "NA") else float(x) for x in f[1:]])
return pd.DataFrame(rows, index=genes, columns=conds, dtype=float)
def read_matrix(name, min_nonmissing=0):
Z = _parse(name)
rank = pd.DataFrame({"n": Z.notna().sum(axis=1).values, "i": np.arange(len(Z))}, index=Z.index)
keep = rank.sort_values(["n", "i"]).groupby(level=0).tail(1).i.sort_values().values
Z = Z.iloc[keep]
return Z[Z.notna().sum(axis=1) >= min_nonmissing].copy()
def axes(conds):
ag = np.array([agent(c) for c in conds])
ox = np.isin(ag, list(OX))
os_ = np.isin(ag, list(OS))
return ag, ox, os_, ~(ox | os_)
def hom(filtered=True, min_nonmissing=MIN_NONMISSING):
Z = read_matrix("hom.z_result_nm.pub")
if filtered:
Z = Z[Z.notna().sum(1) >= min_nonmissing]
return Z
def centre(Z, how="median"):
if how == "median":
m = Z.median(axis=1)
elif how == "mean":
m = Z.mean(axis=1)
else:
from scipy.stats import trim_mean
m = Z.apply(lambda r: trim_mean(r.dropna(), 0.1), axis=1)
return Z.sub(m, axis=0)
def effects(Zc, ox, os_):
return Zc.loc[:, ox].mean(axis=1), Zc.loc[:, os_].mean(axis=1)
def agent_effects(Zc, ag):
return pd.DataFrame({a: Zc.loc[:, ag == a].mean(axis=1) for a in AGENTS})
def abc():
return pd.read_csv(RAW / "abc_transporters.csv")
@lru_cache(maxsize=None)
def phenotypes():
rows = [l.rstrip("\n").split("\t") for l in open(RAW / "phenotype_data.tab", encoding="utf-8", errors="ignore")]
df = pd.DataFrame([r[:10] for r in rows if len(r) >= 10],
columns=["orf", "type", "gene", "sgdid", "ref", "exptype", "mutant", "allele", "strain", "phenotype"])
df["pmid"] = df.ref.str.extract(r"PMID:(\d+)", expand=False)
return df
def sgd_labels(stress=None, term=None, exptypes=None, exclude_pmids=SOURCE_PMIDS):
df = phenotypes()
df = df[df.mutant == "null"]
if exptypes is not None:
df = df[df.exptype.isin(exptypes)]
if exclude_pmids:
df = df[~df.pmid.isin(exclude_pmids)]
head = df.phenotype.str.rsplit(": ", n=1).str[0]
tail = df.phenotype.str.rsplit(": ", n=1).str[-1]
keep = head.isin(TERMS[stress]) if stress else head == term
d = np.where(tail == "increased", 1, np.where(tail == "decreased", -1, 0))
v = pd.Series(d[keep.values], index=df.orf[keep.values].values)
v = v[v != 0].groupby(level=0).sum()
return np.sign(v[v != 0]).astype(int)
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