Last updated: 2022-09-20
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Knit directory: Immunue_Cell_Study/
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confounder=read.csv("//dartfs-hpc.dartmouth.edu/rc/lab/M/MRKepistor7/collab/JieZhou/SourceFiles/req19jul2019_ym_06jul2020.csv")
linkage=read.csv("//dartfs-hpc.dartmouth.edu/rc/lab/M/MRKepistor7/collab/JieZhou/SourceFiles/MBLID_linkage.csv")colnames(data6w)[1:2]=c("subject","time")
colnames(data12m)[1:2]=c("subject","time")
name6w=colnames(data6w)[-c(1,2)]
name12m=colnames(data12m)[-c(1,2)]
jointname=unique(c(name6w, name12m))
n1=nrow(data6w)
n2=nrow(data12m)
data6w_swell=data.frame(matrix(nrow = n1,ncol = length(jointname)))
data12m_swell=data.frame(matrix(nrow = n2,ncol = length(jointname)))
for (j in 1:length(jointname)) {
index1=which(name6w==jointname[j])
index2=which(name12m==jointname[j])
if (length(index1)==1){
data6w_swell[,j]=data6w[,index1+2]
}else{
data6w_swell[,j]=0
}
if (length(index2)==1){
data12m_swell[,j]=data12m[,index2+2]
}else{
data12m_swell[,j]=0
}
}
data6w_swell=cbind(data6w$subject,data6w$time, data6w_swell)
rownames(data6w_swell)=rownames(data6w)
colnames(data6w_swell)=c("subject","time",jointname)
data12m_swell=cbind(data12m$subject,data12m$time, data12m_swell)
rownames(data12m_swell)=rownames(data12m)
colnames(data12m_swell)=c("subject","time",jointname)
data6w12m=rbind(data6w_swell,data12m_swell)
#which(apply(data6w12m[,-c(1,2)], 2, sum)==0)
data6w12m=data6w12m[,-207]
index=order(data6w12m$subject,data6w12m$time)
data6w12m=data6w12m[index,]
save(data6w12m,file = "data6w12m.Rdata")overlap=intersect(data6w_swell$subject,data12m_swell$subject)
##overlapping data
data12msub=data.frame()
df.bcelladj_o=data.frame()
df.cd4tadj_o=data.frame()
df.cd8tadj_o=data.frame()
df.cd8tcd4tadj_o=data.frame()
df.monoadj_o=data.frame()
df.nkadj_o=data.frame()
df.nrbcadj_o=data.frame()
df.granadj_o=data.frame()
for (i in 1:length(overlap)) {
index1=which(data12m_swell$subject==overlap[i])
a=data12m_swell[index1,]
data12msub=rbind(data12msub,a)
index1=which(df.bcelladj6w$subject6w==overlap[i])
a=df.bcelladj6w[index1,]
df.bcelladj_o=rbind(df.bcelladj_o,a)
a=df.cd4tadj6w[index1,]
df.cd4tadj_o=rbind(df.cd4tadj_o,a)
a=df.cd8tadj6w[index1,]
df.cd8tadj_o=rbind(df.cd8tadj_o,a)
a=df.cd8tcd4tadj6w[index1,]
df.cd8tcd4tadj_o=rbind(df.cd8tcd4tadj_o,a)
a=df.monoadj6w[index1,]
df.monoadj_o=rbind(df.monoadj_o,a)
a=df.nkadj6w[index1,]
df.nkadj_o=rbind(df.nkadj_o,a)
a=df.nrbcadj6w[index1,]
df.nrbcadj_o=rbind(df.nrbcadj_o,a)
a=df.granadj6w[index1,]
df.granadj_o=rbind(df.granadj_o,a)
}
longidata=rbind(data6w_swell,data12msub)
longibcell=rbind(df.bcelladj6w,df.bcelladj_o)
longicd4=rbind(df.cd4tadj6w,df.cd4tadj_o)
longicd8=rbind(df.cd8tadj6w, df.cd8tadj_o)
longicd8cd4=rbind(df.cd8tcd4tadj6w,df.cd8tcd4tadj_o)
longimono=rbind(df.monoadj6w,df.monoadj_o)
longink=rbind(df.nkadj6w,df.nkadj_o)
longinrbc=rbind(df.nrbcadj6w,df.nrbcadj_o)
longigran=rbind(df.granadj6w,df.granadj_o)library(vegan)Warning: package 'vegan' was built under R version 4.1.3Loading required package: permuteWarning: package 'permute' was built under R version 4.1.3Loading required package: latticeThis is vegan 2.6-2index=order(longidata[,1],longidata[,2])
longidata=longidata[index,]
alpha6w=diversity(data6w_swell[,-c(1,2)],index = "shannon")
alpha12m=diversity(data12msub[,-c(1,2)],index = "shannon")
alpha=diversity(longidata[,-c(1,2)],index = "shannon")
longibcell=cbind(longibcell[index,],alpha)
longicd4=cbind(longicd4[index,],alpha)
longicd8=cbind(longicd8[index,],alpha)
longicd8cd4=cbind(longicd8cd4[index,],alpha)
longigran=cbind(longigran[index,],alpha)
longimono=cbind(longimono[index,],alpha)
longink=cbind(longink[index,],alpha)
longinrbc=cbind(longinrbc[index,],alpha)exmblid=rownames(longidata)
deliver=c()
for (i in 1:length(exmblid)) {
index2=which(linkage[,2]==exmblid[i])
unq_id=linkage[index2,1]
index3=which(confounder[,1]==unq_id)
a=confounder$deliverytype[index3]
deliver=c(deliver,a)
}longibcell=cbind(longibcell,deliver)
longicd4=cbind(longicd4,deliver)
longicd8=cbind(longicd8,deliver)
longimono=cbind(longimono,deliver)
longink=cbind(longink,deliver)
longinrbc=cbind(longinrbc,deliver)
longigran=cbind(longigran,deliver)ss=df.granadj6w$subject6w
dd=c()
for (i in 1:73) {
index=which(longibcell$subject6w==ss[i])[1]
dd=c(dd,longibcell$deliver[index])
}library(gee)Warning: package 'gee' was built under R version 4.1.3 microbe coef pvalue lower upper fdr
8 Enterobacter 0.1595854 0.002074279 0.05801078 0.2611599 0.0394113
1 Anaerostipes 0.1358712 0.014665627 0.02675257 0.2449898 0.1230639
18 Streptococcus 0.1153356 0.019431135 0.01861212 0.2120592 0.1230639For CD4, GEE method does not yield any significant results.
For CD8, GEE method does not yield any significant results.
microbe coef pvalue lower upper fdr
3 Bifidobacterium -0.13236 0.01525392 -0.2392826 -0.02543741 0.2898245 microbe coef pvalue lower upper
4 Bifidobacterium -0.1434283 1.650503e-05 -0.20868993 -0.07816674
12 F__Enterobacteriaceae 0.2416072 7.318915e-03 0.06503946 0.41817499
fdr
4 0.0003301007
12 0.0731891467 microbe coef pvalue lower upper fdr
3 Blautia 0.04587232 0.01207582 0.01005058 0.08169407 0.105
13 Flavonifractor 0.07556633 0.01721470 0.01338983 0.13774283 0.105
11 F__Lachnospiraceae 0.08422010 0.01966937 0.01345285 0.15498735 0.105 microbe coef pvalue lower upper fdr
4 Bifidobacterium 0.029 0.002 0.010 0.048 0.040
2 Anaerostipes -0.050 0.020 -0.093 -0.008 0.200
15 Flavonifractor -0.065 0.037 -0.127 -0.004 0.247
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] gee_4.13-23 vegan_2.6-2 lattice_0.20-45 permute_0.9-7
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 highr_0.9
[9] workflowr_1.7.0 tools_4.1.2 digest_0.6.29 nlme_3.1-153
[13] jsonlite_1.8.0 evaluate_0.15 lifecycle_1.0.1 tibble_3.1.7
[17] mgcv_1.8-38 pkgconfig_2.0.3 rlang_1.0.4 Matrix_1.3-4
[21] cli_3.3.0 rstudioapi_0.13 parallel_4.1.2 yaml_2.3.5
[25] xfun_0.31 fastmap_1.1.0 cluster_2.1.2 stringr_1.4.0
[29] knitr_1.39 fs_1.5.2 vctrs_0.4.1 sass_0.4.2
[33] grid_4.1.2 rprojroot_2.0.3 glue_1.6.2 R6_2.5.1
[37] fansi_1.0.3 rmarkdown_2.14 magrittr_2.0.3 whisker_0.4
[41] splines_4.1.2 MASS_7.3-54 promises_1.2.0.1 ellipsis_0.3.2
[45] htmltools_0.5.2 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6
[49] cachem_1.0.6