HIV Methylation Age: Cell Composition Coorelates of Age Advancement

Imports and Helper Functions



In [1]:

    
import NotebookImport
from Model_Comparison_MF import *









    




importing IPython notebook from Model_Comparison_MF






    




importing IPython notebook from Age_Models_All_Patients






    




importing IPython notebook from Setup/Imports






    



Populating the interactive namespace from numpy and matplotlib






    




importing IPython notebook from Setup/MethylationAgeModels






    




importing IPython notebook from Setup/Read_HIV_Data



In [2]:

    
pts = ti(labels.isin(['s1','s3']))
pts = pts.intersection(mc_adj_c.index)
age = age.ix[pts].dropna()
pred_c = mc_adj_c.ix[pts].dropna()

Cell Composition



In [3]:

    
screen_feature(age, pearson_pandas, cell_counts.T, align=False)



In [4]:

    
screen_feature(pred_c, pearson_pandas, cell_counts.T, align=False)



In [5]:

    
screen_feature((pred_c - age), pearson_pandas, cell_counts.T, align=False)



In [6]:

    
residual = (pred_c - age)
residual.name = 'residual'

df = process_factors([residual, cell_counts.NK, cell_counts.CD4T, cell_counts.CD8T,
                      cell_counts.Bcell, cell_counts.Mono, cell_counts.Gran],
                     standardize=False)
fmla = robjects.Formula('residual ~ NK + CD4T + CD8T + Bcell + Mono + Gran')
m = robjects.r.lm(fmla, df)
s = robjects.r.summary(m)
print '\n\n'.join(str(s).split('\n\n')[-3:])









    



Coefficients: (1 not defined because of singularities)
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.4800     0.8599   1.721  0.08550 .  
NK            8.9736     3.3440   2.683  0.00739 ** 
CD4T         -5.6350     2.8913  -1.949  0.05155 .  
CD8T         -6.0203     3.9882  -1.510  0.13144    
Bcell       -18.8118     2.9826  -6.307 4.07e-10 ***
Mono          4.0888     7.0568   0.579  0.56243    
Gran              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.104 on 1128 degrees of freedom
Multiple R-squared:  0.0537,	Adjusted R-squared:  0.0495 
F-statistic:  12.8 on 5 and 1128 DF,  p-value: 3.986e-12



In [7]:

    
age.name = 'age'
pred_c.name = 'pred_age'

df = process_factors([age.ix[ti(in_set==True)], cell_counts.NK, cell_counts.CD4T, cell_counts.CD8T,
                      cell_counts.Bcell, cell_counts.Mono, cell_counts.Gran,
                      pred_c], standardize=False)
fmla = robjects.Formula('age ~ pred_age + NK + CD4T + CD8T + Bcell + Mono + Gran')
m = robjects.r.lm(fmla, df)
s = robjects.r.summary(m)
print '\n\n'.join(str(s).split('\n\n')[-3:])









    



Coefficients: (1 not defined because of singularities)
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 13.31146    1.02701  12.961  < 2e-16 ***
pred_age     0.72329    0.01349  53.621  < 2e-16 ***
NK           0.14139    2.88338   0.049  0.96090    
CD4T         5.17463    2.59690   1.993  0.04655 *  
CD8T        -5.48321    3.54943  -1.545  0.12267    
Bcell       16.04825    4.91224   3.267  0.00112 ** 
Mono         3.60495    6.01347   0.599  0.54897    
Gran              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.338 on 1124 degrees of freedom
Multiple R-squared:  0.7309,	Adjusted R-squared:  0.7294 
F-statistic: 508.8 on 6 and 1124 DF,  p-value: < 2.2e-16



In [8]:

    
df = process_factors([age.ix[ti(in_set==True)], cell_counts.NK, cell_counts.CD4T, cell_counts.CD8T,
                      cell_counts.Bcell, cell_counts.Mono, cell_counts.Gran,
                      pred_c], standardize=False)
fmla = robjects.Formula('pred_age ~  age + NK + CD4T + CD8T + Bcell + Mono + Gran')
m = robjects.r.lm(fmla, df)
s = robjects.r.summary(m)
print '\n\n'.join(str(s).split('\n\n')[-3:])









    



Coefficients: (1 not defined because of singularities)
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.76024    1.28973   1.365 0.172585    
age           0.99400    0.01854  53.621  < 2e-16 ***
NK            9.15425    3.36913   2.717 0.006687 ** 
CD4T         -5.46345    3.04535  -1.794 0.073077 .  
CD8T         -6.20882    4.16128  -1.492 0.135968    
Bcell       -19.21910    5.75740  -3.338 0.000871 ***
Mono          4.72759    7.04928   0.671 0.502582    
Gran               NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.086 on 1124 degrees of freedom
Multiple R-squared:  0.7346,	Adjusted R-squared:  0.7332 
F-statistic: 518.6 on 6 and 1124 DF,  p-value: < 2.2e-16



In [9]:

    
pred.name = 'pred_age'



In [10]:

    
df = process_factors([age.ix[ti(in_set==True)], cell_counts.NK, cell_counts.CD4T, cell_counts.CD8T,
                      cell_counts.Bcell, cell_counts.Mono, cell_counts.Gran,
                      pred_c], standardize=False)
fmla = robjects.Formula('pred_age ~  age + CD8T')
m = robjects.r.lm(fmla, df)
s = robjects.r.summary(m)
print '\n\n'.join(str(s).split('\n\n')[-3:])









    



Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.71727    1.09054   0.658   0.5109    
age          0.99978    0.01855  53.901   <2e-16 ***
CD8T        -9.06228    3.96132  -2.288   0.0223 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.147 on 1128 degrees of freedom
Multiple R-squared:  0.7273,	Adjusted R-squared:  0.7268 
F-statistic:  1504 on 2 and 1128 DF,  p-value: < 2.2e-16

	rho	p	q
CD8T	-0.15	9.25e-07	5.55e-06
NK	0.10	5.38e-04	1.61e-03
Mono	0.06	3.19e-02	6.37e-02
Bcell	0.04	1.36e-01	2.05e-01
Gran	-0.02	4.39e-01	5.27e-01
CD4T	-0.01	7.16e-01	7.16e-01

	rho	p	q
CD8T	-0.16	1.35e-07	8.13e-07
NK	0.13	1.30e-05	3.91e-05
Mono	0.10	1.01e-03	2.02e-03
Bcell	-0.07	1.78e-02	2.67e-02
CD4T	-0.05	7.35e-02	8.82e-02
Gran	0.05	1.25e-01	1.25e-01

	rho	p	q
Bcell	-0.20	5.23e-12	3.14e-11
Gran	0.12	3.59e-05	1.08e-04
CD4T	-0.08	5.25e-03	8.70e-03
Mono	0.08	5.80e-03	8.70e-03
NK	0.08	7.57e-03	9.08e-03
CD8T	-0.06	3.75e-02	3.75e-02