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Auto-Plot_Raw_dCTs.py
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Auto-Plot_Raw_dCTs.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from scipy import stats
from scipy.stats import f_oneway
from statsmodels.stats.multicomp import pairwise_tukeyhsd
testdf=pd.read_csv('test_qrt_pcr_2.csv')
groups=testdf.Group.unique()
group1=groups[0]
group2=groups[1]
meancontrol=(testdf['dCT'].where(testdf['Group']==group1))
meancontrol = [meancontrol_i for meancontrol_i in meancontrol if str(meancontrol_i) != 'nan']
meancontrol
controlsem=stats.sem(meancontrol)
meancontrol=sum(meancontrol)/len(meancontrol)
meancontrol
testdf['Power']=2 ** -(testdf['dCT']-meancontrol)
i=len(groups)
experimental_rqs=[]
for x in range(1,i):
group=groups[x]
experimental=(testdf['dCT'].where(testdf['Group']==group))
experimental = [experimental_i for experimental_i in experimental if str(experimental_i) != 'nan']
experimentalsem=stats.sem(experimental)
experimental=sum(experimental)/len(experimental)
experimental_ddCT=experimental-meancontrol
control_ddCT=meancontrol-meancontrol
control_RQ=2 ** -control_ddCT
experimental_RQ=2** -experimental_ddCT
experimental_rqs.append(experimental_RQ)
RQs=[control_RQ]+experimental_rqs
def create_sems():
i=len(groups)
experimental_rqs=[]
experimental_dCTs=[]
experimental_sems=[]
for x in range(1,i):
group=groups[x]
experimental_dCTs=(testdf['dCT'].where(testdf['Group']==group)).values.tolist()
experimental_dCTs = [experimental_dCTs_i for experimental_dCTs_i in experimental_dCTs if str(experimental_dCTs_i) != 'nan']
experimental_RQs=(testdf['Power'].where(testdf['Group']==group)).values.tolist()
experimental_RQs = [experimental_RQs_i for experimental_RQs_i in experimental_RQs if str(experimental_RQs_i) != 'nan']
experimental_RQs_sem=stats.sem(experimental_RQs)
experimental_sems.append(experimental_RQs_sem)
return experimental_sems
def two_sample_test():
i=len(groups)
dCTs_ttest=[]
for x in range(0,i):
group=groups[x]
dCTs=(testdf['dCT'].where(testdf['Group']==group)).values.tolist()
dCTs = [dCTs_i for dCTs_i in dCTs if str(dCTs_i) != 'nan']
dCTs_ttest.append(dCTs)
ttest=round(stats.ttest_ind(dCTs_ttest[0],dCTs_ttest[1])[1],10)
d = {'group1': group1, 'group2': group2, 'p-adj': ttest}
tukeydf = pd.DataFrame(data=d,index=[1])
return tukeydf
def multiple_sample_test():
tukey=pairwise_tukeyhsd(testdf['dCT'],testdf['Group'],0.05)
tukeydf = pd.DataFrame(data=tukey._results_table.data[1:], columns=tukey._results_table.data[0])
return tukeydf
def assign_stars():
if len(groups)>2:
tukeydf['SE']=(tukeydf['upper']-tukeydf['lower'])/(2*1.96)
tukeydf['z']=(tukeydf['meandiff']/tukeydf['SE'])
tukeydf['p_actual']=np.exp((-0.717*tukeydf['z'])-0.416*(tukeydf['z']**2))
tukeydf_fig=tukeydf.where(tukeydf['p_actual']<0.05)
tukeydf_fig=tukeydf_fig.dropna()
else:
tukeydf['p_actual']=tukeydf['p-adj']
tukeydf_fig=tukeydf.where(tukeydf['p_actual']<0.05)
tukeydf_fig=tukeydf_fig.dropna()
def func(x):
if x < 0.05 and x >= 0.01:
return "*"
elif x < 0.01 and x >= 0.001:
return "**"
elif x < 0.001 and x >= 0.0001:
return "***"
else:
return "****"
tukeydf_fig['Stars'] = tukeydf_fig['p_actual'].apply(func)
i=0
group_dict={}
for item in groups:
x={item: i}
group_dict.update(x)
i+=1
tukey_test= tukeydf_fig
tukey_test=tukey_test.replace({"group1": group_dict})
tukey_test=tukey_test.replace({"group2": group_dict})
tukey_test=tukey_test[['group1','group2','Stars']]
tukey_test['ind']=[np.arange(len(groups))]* len(tukey_test)
tukey_test['menMeans']=[RQs]* len(tukey_test)
tukey_test
return tukey_test
sems=[controlsem]+create_sems()[:len(group)]
if len(groups)==2:
tukeydf=two_sample_test()
if len(groups)>2:
tukeydf=multiple_sample_test()
starsdf=assign_stars()
fig = plt.figure(figsize=(9, 7))
sns.barplot(x=groups,y=RQs,color='lightgrey',edgecolor='black')
sns.swarmplot(x='Group',y='Power',data=testdf,size=8/np.log(len(groups)))
plt.errorbar(groups, RQs, yerr=sems, fmt=' ',color='black', zorder=-1, capsize=10)
plt.ylabel('RQ')
ylim_max=(max(RQs))+(len(starsdf))
ylim_min=0
plt.ylim(ylim_min,ylim_max)
def label_diff(i,j,text,X,Y):
x = ((X[i]+X[j])/2)-(len(text)/50)
differential= abs(Y[i]/Y[j])
#y = 1.1*(Y[i] + Y[j])-(max(Y[i], Y[j])/10)
y = max([Y[i],Y[j]])+differential*5
#y= max(Y[i], Y[j])+ylim/5
dx = abs(X[i]-X[j])
props = {'connectionstyle':'arc','arrowstyle':'-',\
'shrinkA':10,'shrinkB':10,'linewidth':1}
plt.annotate(text, xy=(x,y+(ylim/8)), zorder=10, size=15, bbox=dict(boxstyle='square,pad=-.1',facecolor='white', edgecolor='none', alpha=1))
plt.annotate('', xy=(X[i],y+(ylim/8)), xytext=(X[j],y+(ylim/8)), arrowprops=props)
for n in range (0,len(starsdf)):
label_diff(starsdf.iloc[n].values.tolist()[0],
starsdf.iloc[n].values.tolist()[1],
starsdf.iloc[n].values.tolist()[2],
starsdf.iloc[n].values.tolist()[3],
starsdf.iloc[n].values.tolist()[4])
#plt.title('Unpaired t-test p= {}'.format(ttest))