diff --git a/jass/models/plots.py b/jass/models/plots.py
index 12a4eb8cec40d45d99ab6aef47cef0ce8cf30837..8f80635d5d8a17b249574b44ae9ac90a735f8073 100644
--- a/jass/models/plots.py
+++ b/jass/models/plots.py
@@ -16,10 +16,11 @@ import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib.patches as mpatches
from scipy.stats import norm, chi2
-
+import seaborn as sns
import os
from pandas import DataFrame, read_hdf
-from scipy.stats import chi2
+import pandas as pd
+
def replaceZeroes(df):
"""
@@ -233,7 +234,7 @@ def create_quadrant_plot(work_file_path: str,
alim = np.ceil(pv_t.JASS_PVAL.loc[pv_t.color == "#f77189"].max() + 2)
if np.isnan(alim):
alim = 10
- plt.axis([0, alim, 0, alim])
+ plt.plot([0, alim, 0, alim])
# légendes abcisse et ordonnee
plt.xlabel('-log10(P) for univariate tests', fontsize=12)
# plt.show()
@@ -251,60 +252,50 @@ def create_quadrant_plot(work_file_path: str,
def create_qq_plot(work_file_path: str, qq_plot_path: str):
- df = read_hdf(work_file_path, "SumStatTab", columns=['JASS_PVAL', 'UNIVARIATE_MIN_PVAL'])
+ df = read_hdf(work_file_path, "SumStatTab", columns=['JASS_PVAL', 'UNIVARIATE_MIN_PVAL', 'UNIVARIATE_MIN_QVAL'])
+
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL', "UNIVARIATE_MIN_QVAL"]] = replaceZeroes(
+ df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL','UNIVARIATE_MIN_QVAL']])
- df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(
- df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
- dr = read_hdf(work_file_path, "Regions")
- #count the number of traits
- Ntrait = len([i for i in dr.columns if i[:2]=='z_'])
pvalue = -np.log10(df.JASS_PVAL)
-<<<<<<< jass/models/plots.py
- # Cast values between 0 and 1, 0 and 1 excluded
- x = -np.log10(np.arange(1, pvalue.shape[0] + 1) / (pvalue.shape[0] + 2))
- y = pvalue.sort_values()
- plt.scatter(x[::-1], y, s=5)
- pval_median = np.nanmedian(df.JASS_PVAL)
- lambda_value_omni = chi2.isf(pval_median, df=Ntrait) / chi2.isf(0.5, df=Ntrait)
- lambda_value_sumZ = chi2.isf(pval_median, df=1) / chi2.isf(0.5, df=1)
- print("Number of trait analyzed = {}".format(Ntrait))
- print("JASS median p-val = {:.3f}".format(pval_median))
- print("Inflation Factor if the test is omnibus= {:.3f}".format(lambda_value_omni))
- print("Inflation Factor if the test is a SumZ= {:.3f}".format(lambda_value_sumZ))
- x_1 = np.linspace(0, 6)
- y_1 = (pval_median/0.5) * x_1
- x_2 = np.linspace(0, 6)
- plt.plot(x_1, y_1, c="red")
- plt.plot(x_2, x_2)
- plt.title("Median p-val = {:.2f}".format(pval_median))
- plt.xlabel("expected quantile of -log10(P)")
- plt.ylabel("observed quantile of -log10(P)")
-
- plt.savefig(qq_plot_path, dpi=600)
-
-=======
pvalue_univ = -np.log10(df.UNIVARIATE_MIN_PVAL)
+ qvalue_univ = -np.log10(df.UNIVARIATE_MIN_QVAL)
# compute_expected pvalue
-
- QQ_pval = pd.DataFrame(index =-np.log10(np.arange(1, pvalue.shape[0] + 1) / (pvalue.shape[0] + 2)),
- {"JASS pvalue" : pvalue.sort_values(),
- "UNIV pvalue" : pvalue_univ.sort_values()})
-
+ pvalue.sort_values().values
+
+ QQ_pval = pd.DataFrame({"JASS p-value" : pvalue.sort_values().values,
+ "Univariate p-value" : pvalue_univ.sort_values().values,
+ "Univariate q-value" : qvalue_univ.sort_values().values,
+ })
+ QQ_pval = QQ_pval.iloc[::20,].dropna()
+ exp_val = np.flip(- np.log10(QQ_pval.index.values / QQ_pval.index.max()))
+
+ exp_val[-1] = QQ_pval.max().max()
+ QQ_pval.index = exp_val
+ QQ_pval = QQ_pval.iloc[:-1,]
pval_median = df.JASS_PVAL.median()
pval_median_univ = df.UNIVARIATE_MIN_PVAL.median()
+ pval_median_quniv = df.UNIVARIATE_MIN_QVAL.median()
+
print("median pval")
print(pval_median)
- lambda_value_jass = chi.sf(pval_median) / chi.sf(0.5)
- lambda_value_univ = chi.sf(pval_median_univ) / chi.sf(0.5)
+ lambda_value_jass = chi2.sf(pval_median, df=1) / chi2.sf(0.5, df=1)
+ lambda_value_jass
+ lambda_value_univ = chi2.sf(pval_median_univ, df=1) / chi2.sf(0.5, df=1)
+ lambda_value_quniv = chi2.sf(pval_median_quniv, df=1) / chi2.sf(0.5, df=1)
p = sns.lineplot(data=QQ_pval)
- p.set("QQ plot")
- p.set_xlabel("Expected p-values", fontsize = 20)
- p.set_ylabel("Observed p-values", fontsize = 20)
+ alim = exp_val[-2]
+ plt.plot([0, alim],[0, alim], c="red", linewidth=0.5)
+ p.set_title("QQ plot\n λ JASS = {:.2f}\n λ univariate p-values = {:.2f} λ univariate q-values = {:.2f}".format(lambda_value_jass, lambda_value_univ, lambda_value_quniv), fontsize = 11)
+ p.set_xlabel("Expected -log10(p-values)", fontsize = 13)
+ p.set_ylabel("Observed -log10(p-values)", fontsize = 13)
+
plt.savefig(qq_plot_path)
->>>>>>> jass/models/plots.py
+ plt.clf()
print("------ QQ plot -----")
+
def create_qq_plot_by_GWAS(init_file_path: str, qq_plot_folder: str):
df = read_hdf(init_file_path, "SumStatTab", where="Region < {0}".format(2))
uni_var = [i for i in df.columns if i[:2]=="z_"]
diff --git a/requirements.txt b/requirements.txt
index a3c65b209fe6a63dd6cfd9fb893bc3cdc6a95d02..cbdbddcbb29c31ca7932c7c96e6fa0f50da3e0d4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,9 +8,10 @@ pandas
tables
scipy
matplotlib
+seaborn
celery
pydantic
fastapi
uvicorn[standard]
typing_extensions; python_version < '3.8'
-requests
\ No newline at end of file
+requests