Last updated: 2022-09-23

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Knit directory: Immunue_Cell_Study/

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clean the data

library(gee)
source("./code/datacleaning.r")
index=which(longidata$time==12)

Regression analysis

Microbes associated with Bcell

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~bcell + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longibcell[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    next()
  }else{
    fm=gee(cbind(y1,y2)~bcell + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longibcell[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
colnames(rrbcell)=c("coef","pvalue","lower","upper")
fdr=p.adjust(rrbcell[,2],method = "BH")
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.3),]
                microbe       coef       pvalue       lower       upper
2          Anaerostipes  0.1297091 0.0007157138  0.05457072  0.20484753
12 Escherichia/Shigella -0.1928308 0.0206614514 -0.35615391 -0.02950768
          fdr
2  0.01502999
12 0.21694524

Microbes associated with CD4

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~cd4t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longicd4t[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    print(k)
    next()
  }else{
    fm=gee(cbind(y1,y2)~cd4t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longicd4t[index],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
For CD4, GEE method does not yield any significant results.

Microbes associated with CD8

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~cd8t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longicd8t[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    print(k)
    next()
  }else{
    fm=gee(cbind(y1,y2)~cd8t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longicd8t[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}

For CD8, GEE method does not yield any significant results. <>

Microbes associated with Mono

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~mono + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longimono[index],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    next()
  }else{
    fm=gee(cbind(y1,y2)~mono + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longimono[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
data frame with 0 columns and 0 rows

Microbes associated with NK

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~nk + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longink[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    next()
  }else{
    fm=gee(cbind(y1,y2)~nk + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longink[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
colnames(rrbcell)=c("coef","pvalue","lower","upper")
fdr=p.adjust(rrbcell[,2],method = "BH")
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.3),]
         microbe       coef      pvalue      lower       upper       fdr
21 Streptococcus -0.1438155 0.006659592 -0.2476992 -0.03993182 0.1465110
20 Ruminococcus2 -0.1306459 0.019678153 -0.2404310 -0.02086078 0.2164597

Microbes associated with NRBC

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~nrbc + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longinrbc[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    next()
  }else{
    fm=gee(cbind(y1,y2)~nrbc + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longinrbc[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=c(r1,r2,r3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
colnames(rrbcell)=c("coef","pvalue","lower","upper")
fdr=round(p.adjust(rrbcell[,2],method = "BH"),3)
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.3),]
                     microbe        coef     pvalue       lower         upper
4                    Blautia  0.06434720 0.00100589  0.02599957  0.1026948212
8  Clostridium_sensu_stricto -0.10239862 0.01786226 -0.18713773 -0.0176595073
10      Escherichia/Shigella -0.04778838 0.04803196 -0.09516377 -0.0004129773
1               Anaerostipes  0.05023228 0.06564783 -0.00325216  0.1037167135
6          Clostridium_XVIII -0.02894647 0.07578439 -0.06089751  0.0030045755
     fdr
4  0.018
8  0.161
10 0.273
1  0.273
6  0.273

Microbes associated with Gran

Click me 
options(warn = 2)
m=ncol(longidata)-2
n=nrow(longidata)
name=colnames(longidata)[-c(1,2)]
microbe=c()                            
rrbcell=data.frame()
for (k in 1:m) {
  y1=longidata[index,k+2]
  y2=apply(longidata[index,-c(1,2,k+2)],1,sum)
  fm=try({gee(cbind(y1,y2)~gran + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longigran[index,],family="binomial",corstr="independence")},silent = T)
  if (inherits(fm,"try-error")){
    next()
  }else{
    fm=gee(cbind(y1,y2)~gran + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,id=subject6w, data=longigran[index,],family="binomial",corstr="independence")   
   a=summary(fm)
   r1=a$coefficients[2,1]
   r2=2*(1-pnorm(abs(a$coefficients[2,5])))
   r3= c(r1-1.96*a$coefficients[2,4],r1+ 1.96*a$coefficients[2,4])
   rr=round(c(r1,r2,r3),3)
   microbe=c(microbe,name[k])
    rrbcell=rbind(rrbcell,rr)
     }

}
colnames(rrbcell)=c("coef","pvalue","lower","upper")
fdr=round(p.adjust(rrbcell[,2],method = "BH"),3)
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.4),]
       microbe   coef pvalue  lower  upper   fdr
2 Anaerostipes -0.044  0.014 -0.079 -0.009 0.322

<>


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] vegan_2.6-2     lattice_0.20-45 permute_0.9-7   gee_4.13-23    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9       pillar_1.8.0     compiler_4.1.2   bslib_0.4.0     
 [5] later_1.3.0      jquerylib_0.1.4  git2r_0.30.1     workflowr_1.7.0 
 [9] tools_4.1.2      digest_0.6.29    nlme_3.1-153     jsonlite_1.8.0  
[13] evaluate_0.15    lifecycle_1.0.1  tibble_3.1.7     mgcv_1.8-38     
[17] pkgconfig_2.0.3  rlang_1.0.4      Matrix_1.3-4     cli_3.3.0       
[21] rstudioapi_0.13  parallel_4.1.2   yaml_2.3.5       xfun_0.31       
[25] fastmap_1.1.0    cluster_2.1.2    stringr_1.4.0    knitr_1.39      
[29] fs_1.5.2         vctrs_0.4.1      sass_0.4.2       grid_4.1.2      
[33] rprojroot_2.0.3  glue_1.6.2       R6_2.5.1         fansi_1.0.3     
[37] rmarkdown_2.14   magrittr_2.0.3   splines_4.1.2    MASS_7.3-54     
[41] promises_1.2.0.1 ellipsis_0.3.2   htmltools_0.5.2  httpuv_1.6.5    
[45] utf8_1.2.2       stringi_1.7.6    cachem_1.0.6