Aging Analysis of Cytokine and SNP data

In [1]:

    

from __future__ import division
import os, os.path
import numpy as np
import pandas as pd
from patsy import dmatrices
from patsy.contrasts import Treatment
import statsmodels.api as sm
from statsmodels.stats.multitest import multipletests

sys.path.append('/home/will/PySeqUtils/')
os.chdir('/home/will/AgingAnalysis/')


    

In [2]:

    

norm_cyto_data = pd.read_csv('CytoRawDataNorm.csv',
                             sep='\t')
pat_data = pd.read_csv('CytoPatData.csv',
                       index_col=[0,1],
                       sep='\t')



norm_cyto_data['Th1'] = norm_cyto_data['IFN.gamma'] + \
                            norm_cyto_data['IL.2']+norm_cyto_data['TNF.alpha']
norm_cyto_data['Th2'] = norm_cyto_data['IL.4'] + \
                            norm_cyto_data['IL.5']+norm_cyto_data['IL.10']

raw_cyto_data = pd.read_csv('CytoRawData.csv', 
                            index_col=[0,1,2], 
                            sep = '\t')
raw_cyto_data['Th1'] = raw_cyto_data['IFN.gamma'] + \
                        raw_cyto_data['IL.2'] + \
                        raw_cyto_data['TNF.alpha']
raw_cyto_data['Th2'] = raw_cyto_data['IL.4'] + \
                        raw_cyto_data['IL.5'] + \
                        raw_cyto_data['IL.10']

cytos = sorted(['IL.8','VEGF','IL.1beta',
        'G.CSF','EGF','IL.10','HGF',
        'FGF.basic','IFN.alpha','IL.6',
        'IL.12','Rantes','Eotaxin',
        'GM.CSF','MIP.1beta',
        'MCP.1','IL.5','IL.13', 'IFN.gamma','TNF.alpha',
        'IL.RA','IL.2','IL.7','IP.10',
        'IL.2R','MIG','IL.4','IL.15',
        'IL.17','MIP.1alpha']) + ['Th1', 'Th2']


    

In [3]:

    

pat_cyto_data = pd.merge(pat_data, raw_cyto_data.reset_index(),
                         left_index = True, 
                         right_on = ['Patient ID', 'VisitNum'],
                         how = 'outer')

pat_cyto_data = pat_cyto_data.set_index(['Patient ID', 
                                        'VisitNum', 
                                        'SampleNum'])


    

In [4]:

    

pat_cyto_data['CD4:CD8'] = pat_cyto_data['CD4'] / pat_cyto_data['CD8']
pat_cyto_data['LVL'] = pat_cyto_data['VL'].map(np.log10)
pat_cyto_data['Th1:Th2'] = pat_cyto_data['Th1']/pat_cyto_data['Th2']


    

In [5]:

    

pat_cyto_data['Aged50'] = pat_cyto_data['Age']>50
pat_cyto_data['Younger35'] = pat_cyto_data['Age']<35


    

In [6]:

    

pat_cyto_data['YearsSeropositive'].describe()


    

    
Out[6]:





count    458.000000
mean      11.676856
std        6.552293
min        0.000000
25%        6.000000
50%       11.000000
75%       16.000000
max       27.000000
dtype: float64

In [8]:

    

pd.pivot_table(wanted_cyto, rows='Aged50', values=cytos+['HIVD']).T


    

    
Out[8]:






  

    

      
Aged50
      
False
      
True
    
  
  

    

      
EGF
      
  105.304266
      
  102.379608
    
    

      
Eotaxin
      
   69.884766
      
   86.212417
    
    

      
FGF.basic
      
   41.900928
      
   16.216123
    
    

      
G.CSF
      
   46.383072
      
   65.393837
    
    

      
GM.CSF
      
    5.487571
      
    7.020327
    
    

      
HGF
      
  238.948556
      
  229.572799
    
    

      
HIVD
      
   10.115385
      
    8.000000
    
    

      
IFN.alpha
      
  169.193524
      
  198.998997
    
    

      
IFN.gamma
      
   19.091137
      
   31.253517
    
    

      
IL.10
      
   22.651862
      
   18.166773
    
    

      
IL.12
      
  190.011167
      
  215.944359
    
    

      
IL.13
      
    5.614936
      
   10.333955
    
    

      
IL.15
      
  102.109878
      
   89.736611
    
    

      
IL.17
      
    9.707889
      
   19.249134
    
    

      
IL.1beta
      
   15.471898
      
   18.330460
    
    

      
IL.2
      
   10.178632
      
   13.885858
    
    

      
IL.2R
      
  314.604224
      
  415.379669
    
    

      
IL.4
      
   32.767861
      
   46.257278
    
    

      
IL.5
      
    8.485376
      
   15.243765
    
    

      
IL.6
      
   11.416763
      
   10.492073
    
    

      
IL.7
      
   36.737264
      
   55.111647
    
    

      
IL.8
      
   32.965930
      
   37.040440
    
    

      
IL.RA
      
  307.225613
      
  444.847341
    
    

      
IP.10
      
   27.989538
      
   26.592371
    
    

      
MCP.1
      
  269.889154
      
  289.754384
    
    

      
MIG
      
   45.962837
      
   58.803529
    
    

      
MIP.1alpha
      
   55.558063
      
   70.238739
    
    

      
MIP.1beta
      
  171.869949
      
  171.661786
    
    

      
Rantes
      
 8074.206069
      
 8953.420551
    
    

      
TNF.alpha
      
   12.041324
      
   18.225076
    
    

      
Th1
      
   53.594874
      
   82.594106
    
    

      
Th2
      
   74.966344
      
  103.484261
    
    

      
VEGF
      
    1.481477
      
    1.669128
    
  

In [100]:

    

from statsmodels.graphics.boxplots import beanplot
from itertools import chain, combinations
from statsmodels.graphics.regressionplots import plot_fit

def powerset(iterable):
    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

def unique_powerset(iterable):
    seen = set()
    for tup in powerset(iterable):
        fz = frozenset(tup)
        if fz not in seen:
            yield tup
            seen.add(fz)

def most_common(inser):
    try:
        return inser.value_counts().index[0]
    except IndexError:
        return np.nan

def make_agg(indata, confounders):
    agg_dict = {}
    for col in confounders:
        if indata[col].dtype == 'O':
            agg_dict[col] = most_common
        else:
            agg_dict[col] = 'mean'
    return agg_dict

def extract_cols(indata, cyto, cols):
    
    extract_cols = [cyto]+cols
    tmp_data = indata[extract_cols]
    agg_dict = make_agg(indata, extract_cols)
    tmp_agg = tmp_data.groupby(level=[0,1]).agg(agg_dict).dropna()
    tmp_agg = tmp_agg.rename(columns={cyto:'y'})
    eqn = 'y ~ ' + ' + '.join(cols)
    return eqn, tmp_agg
    


def make_50_binary(indata, cyto, ax, confounders):
    eqn, tmp_cyto = extract_cols(indata, cyto, ['Aged50']+confounders)
    y, X = dmatrices(eqn, tmp_cyto, return_type='dataframe')
    model = sm.OLS(y, X).fit()
    
    if ax is not None:
        boxes = [tmp_cyto['y'][tmp_cyto['Aged50']], tmp_cyto['y'][~tmp_cyto['Aged50']]]
        try:
            beanplot(boxes, labels=['>50', '<50'], ax=ax)
        except:
            ax.boxplot(boxes)
    
    return model, model.f_pvalue, model.pvalues['Aged50[T.True]']


def make_35_binary(indata, cyto, ax, confounders):
    
    valid_mask = (indata['Age'] >= 50) | (indata['Age'] <= 35)
    
    eqn, tmp_cyto = extract_cols(indata[valid_mask], cyto, 
                                     ['Aged50']+confounders)
    y, X = dmatrices(eqn, tmp_cyto, return_type='dataframe')
    model = sm.OLS(y, X).fit()
    boxes = [tmp_cyto['y'][tmp_cyto['Aged50']], tmp_cyto['y'][~tmp_cyto['Aged50']]]
    
    if ax is not None:
        try:
            beanplot(boxes, labels=['>50', '<35'], ax=ax)
        except:
            ax.boxplot(boxes)
    
    return model, model.f_pvalue, model.pvalues['Aged50[T.True]']


def make_age_linear(indata, cyto, ax, confounders):
    eqn, tmp_cyto = extract_cols(indata, cyto, 
                                     ['Age']+confounders)
    y, X = dmatrices(eqn, tmp_cyto, return_type='dataframe')
    model = sm.OLS(y, X).fit()
    num = (num for num, col in enumerate(X.columns) if col=='Age').next()
    
    if ax is not None:
        plot_fit(model, num, ax=ax)
    
    return model, model.f_pvalue, model.pvalues['Age']


def check_more_data(indata, check_func, cyto, confounders, num_extras = [0.5, 1.0, 2.0, 5.0]):
    
    for num in num_extras:
        num_choose = int(num*len(indata))
        pats = [p for p, v, s in indata.index]
        tdata = indata.copy()
        for e in range(num_choose):
            rep_pat = np.random.choice(pats)
            pat = indata.ix[rep_pat]
            pat.index = pd.MultiIndex.from_tuples([(rep_pat+str(e), v, s) for v, s in pat.index])
            tdata = pd.concat([tdata, pat], axis=0)
        _, model_p, age_p = check_func(tdata, cyto, None, confounders)
        yield num_choose, model_p, age_p


    

In [101]:

    

all_selectors = set(['HCV', 'VL', 'HAART', 'Race', 'Grouping'])
restrict_dict = {
                 'HCV':pat_cyto_data['HCV']==False,
                 'VL':pat_cyto_data['VL']<=100,
                 'HAART':pat_cyto_data['HAART']=='cH',
                 'Race':pat_cyto_data['Race']=='Black/AA',
                 'Grouping':pat_cyto_data['Grouping']=='PN'
                 }
anal_methods = [('50|50 split', make_50_binary),
                ('35|50 split', make_35_binary),
                ('Linear', make_age_linear)]
checks = cytos+['CD4', 'LVL', 'HIVD']

num_subsets = len(list(unique_powerset(all_selectors)))
print num_subsets


    

    




32

In [102]:

    

base_confounders = ['YearsSeropositive']


results = []
for cyto in checks:
    print cyto
    max_val = pat_cyto_data[cyto].max()*1.1
    for row_num, selectors in enumerate(unique_powerset(all_selectors)):
        print selectors
        fig, axs = plt.subplots(1, 3, figsize=(15,5), sharey=True)
        set_sels = set(selectors)
        confounders = base_confounders + list(all_selectors-set_sels)
        wanted_mask = pat_cyto_data[cyto].notnull()
        for sel in selectors:
            wanted_mask &= restrict_dict[sel]
        wanted_pats = pat_cyto_data[wanted_mask]
        sel_bool = [s in set_sels for s in sorted(all_selectors)]
        for ax, (pname, pfunc) in zip(axs.flatten(), anal_methods):
            model, model_p, age_p = pfunc(wanted_pats, cyto, ax, confounders)
            results.append(sel_bool+[cyto, pname, model_p, age_p, 0])
            ax.set_title(pname + ' p=%f' % age_p)
            if ax.is_first_col():
                ax.set_ylabel(cyto)
            if ax.is_last_col():
                ax.yaxis.set_label_position("right")
                ax.set_ylabel(', '.join(confounders))
            for num_extra, model_p, age_p in check_more_data(wanted_pats, pfunc, cyto, confounders):
                results.append(sel_bool+[cyto, pname, model_p, age_p, num_extra])
    
        if len(selectors):
            sels = '_'.join(sorted(selectors))
        else:
            sels = 'None'
        fname = cyto + '_' + sels + '.png'
        fig.savefig('draft_figures/aging_cyto_dump/'+fname)
        plt.close(fig)


    

    




EGF
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
Eotaxin
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
FGF.basic
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
G.CSF
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
GM.CSF
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
HGF
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IFN.alpha
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IFN.gamma
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.10
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.12
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.13
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.15
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.17
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.1beta
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.2
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.2R
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.4
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.5
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.6
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.7
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.8
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IL.RA
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
IP.10
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
MCP.1
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
MIG
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
MIP.1alpha
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
MIP.1beta
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
Rantes
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
TNF.alpha
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
VEGF
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
Th1
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
Th2
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
CD4
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
LVL
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')
HIVD
()
('VL',)
('Race',)
('HCV',)
('HAART',)
('Grouping',)
('VL', 'Race')
('VL', 'HCV')
('VL', 'HAART')
('VL', 'Grouping')
('Race', 'HCV')
('Race', 'HAART')
('Race', 'Grouping')
('HCV', 'HAART')
('HCV', 'Grouping')
('HAART', 'Grouping')
('VL', 'Race', 'HCV')
('VL', 'Race', 'HAART')
('VL', 'Race', 'Grouping')
('VL', 'HCV', 'HAART')
('VL', 'HCV', 'Grouping')
('VL', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART')
('Race', 'HCV', 'Grouping')
('Race', 'HAART', 'Grouping')
('HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART')
('VL', 'Race', 'HCV', 'Grouping')
('VL', 'Race', 'HAART', 'Grouping')
('VL', 'HCV', 'HAART', 'Grouping')
('Race', 'HCV', 'HAART', 'Grouping')
('VL', 'Race', 'HCV', 'HAART', 'Grouping')

In [103]:

    

res = pd.DataFrame(results, columns = sorted(all_selectors)+['Cyto', 'AnalName', 'ModelP', 'AgeP', 'NumExtra'])
res.groupby(['Cyto', 'AnalName', 'NumExtra'])['AgeP'].min()


    

    
Out[103]:





Cyto  AnalName     NumExtra
CD4   35|50 split  0           0.094724
                   36          0.036132
                   40          0.000629
                   42          0.000762
                   46          0.099160
                   48          0.006785
                   52          0.183773
                   54          0.185530
                   56          0.139904
                   60          0.016820
                   61          0.002920
                   62          0.008057
                   64          0.111937
                   65          0.001428
                   70          0.000305
...
VEGF  Linear    708         9.436011e-01
                710         3.984176e-14
                764         3.521564e-01
                836         8.524784e-01
                916         6.200478e-01
                950         5.594901e-08
                990         2.462396e-05
                1070        2.630029e-02
                1110        2.657404e-04
                1170        2.137366e-07
                1250        2.297957e-07
                1770        5.566825e-01
                1910        5.182707e-01
                2090        4.183624e-01
                2290        5.706108e-01
Name: AgeP, Length: 10755, dtype: float64

In [107]:

    

mask = (res['AgeP']<0.05) & (res['ModelP']<0.05)
more_counts = pd.pivot_table(res[mask], rows='Cyto', cols='AnalName', 
                             values='NumExtra', aggfunc='min')
more_counts.to_excel('needed_pats.xlsx')


    

In [170]:

    

import statsmodels.formula.api as smi
from scipy.stats import ttest_ind, ks_2samp
from patsy import dmatrices
from statsmodels.graphics.boxplots import beanplot, violinplot

fig = make_50_binary(wanted_cyto)
fig.savefig('draft_figures/Binary_50_cytos.png', dpi=500)


    

In [171]:

    

fig = make_35_binary(wanted_cyto)
fig.savefig('draft_figures/Binary_35_cytos.png', dpi=500)


    

In [172]:

    

fig = make_age_linear(wanted_cyto)
fig.savefig('draft_figures/Age_cytos.png', dpi=500)


    

In [173]:

    

larger_wanted_pats = pat_cyto_data['HCV']==False
#larger_wanted_pats &= pat_cyto_data['VL']<=100
larger_wanted_pats &= pat_cyto_data['HAART']=='cH'
larger_wanted_pats &= pat_cyto_data['Race']=='Black/AA'
larger_wanted_pats &= (pat_cyto_data['Grouping']=='PN')|(pat_cyto_data['Grouping']=='PC')
print larger_wanted_pats.sum()/4
larger_wanted_cyto = pat_cyto_data[larger_wanted_pats]


    

    




33.0

In [174]:

    

fig = make_50_binary(larger_wanted_cyto, confounders=['YearsSeropositive', 'Grouping'])
fig.savefig('draft_figures/Binary_50_cytos_withPC.png', dpi=500)


    

In [175]:

    

fig = make_35_binary(larger_wanted_cyto, confounders=['YearsSeropositive', 'Grouping'])
fig.savefig('draft_figures/Binary_35_cytos_withPC.png', dpi=500)


    

In [176]:

    

fig = make_age_linear(larger_wanted_cyto, confounders=['YearsSeropositive', 'Grouping'])
#fig.savefig('draft_figures/Age_cytos_withPC.png', dpi=500)


    

In [ ]: