| import numpy as np |
| import pandas as pd |
| from scipy import stats |
| from common import RES, hom, axes, centre, effects, abc |
|
|
| TARGET = "YPL270W" |
|
|
| Z = hom() |
| _, ox, os_, _ = axes(Z.columns) |
| e_ox, e_os = effects(centre(Z), ox, os_) |
| cd = (e_ox - e_os).abs().dropna() |
|
|
| tail = (cd >= cd[TARGET]).mean() |
| pct = stats.percentileofscore(cd.values, cd[TARGET]) |
|
|
| A = abc() |
| abc_cd = cd.reindex(A.systematic).dropna() |
| others = cd.drop(abc_cd.index) |
| mw = stats.mannwhitneyu(abc_cd.values, others.values, alternative="greater") |
|
|
| pd.DataFrame({"gene": A.set_index("systematic").gene.reindex(abc_cd.index).values, |
| "cd": abc_cd.round(3).values}).to_csv(RES / "abc_cd.csv", index=False) |
| pd.DataFrame({"what": ["MDL2_cd", "reference_p90"], |
| "value": [round(cd[TARGET], 3), round(np.percentile(cd.values, 90), 3)]} |
| ).to_csv(RES / "mdl2_reference_markers.csv", index=False) |
| pd.DataFrame({"cd": cd.values}, index=cd.index).rename_axis("gene").to_csv(RES / "null_cd.csv") |
|
|
| pd.DataFrame([("MDL2 |cd|", cd[TARGET]), ("reference tail fraction (all analysed genes)", tail), |
| ("reference percentile (all analysed genes)", pct), ("reference size", len(cd)), |
| ("ABC vs other genes Mann-Whitney U", mw.statistic), ("ABC vs other genes p (one-sided)", mw.pvalue), |
| ("ABC transporters", len(abc_cd)), ("other genes", len(others))], |
| columns=["quantity", "value"]).to_csv(RES / "calibration_summary.csv", index=False) |
|
|
