Last updated: 2022-09-20
Checks: 5 2
Knit directory: Immunue_Cell_Study/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish
to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20220920)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
absolute | relative |
---|---|
C:/Users/Jie Zhou/Documents/paper02052022/immune cell study/Immunue_Cell_Study/data/inputdataMZILN.Rdata | data/inputdataMZILN.Rdata |
C:/Users/Jie Zhou/Documents/paper02052022/immune cell study/Immunue_Cell_Study/data/SILVAtaxtab_G_12M.RDS | data/SILVAtaxtab_G_12M.rds |
C:/Users/Jie Zhou/Documents/paper02052022/immune cell study/Immunue_Cell_Study/data/NHBCSreq19SEP2018ym_deided.csv | data/NHBCSreq19SEP2018ym_deided.csv |
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 546725f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Unstaged changes:
Modified: analysis/12month_analysis.Rmd
Modified: analysis/6week_analysis.Rmd
Modified: analysis/marginal_model_complete.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/12month_analysis.Rmd
) and HTML (docs/12month_analysis.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 5bf2b3b | Jie Zhou | 2022-09-20 | add the remote repo |
html | 5bf2b3b | Jie Zhou | 2022-09-20 | add the remote repo |
html | d93bb2c | Jie Zhou | 2022-09-20 | add the remote repo |
Rmd | 098d058 | Jie Zhou | 2022-09-20 | immune cell |
html | 098d058 | Jie Zhou | 2022-09-20 | immune cell |
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,]
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 4.1.3
Loading required package: permute
Warning: package 'permute' was built under R version 4.1.3
Loading required package: lattice
This is vegan 2.6-2
index=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])
}
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~bcell + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver, data=longibcell[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~bcell + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver ,data=longibcell[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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]<0.1),]
rrbcellfdr
rrbcellfdr
microbe coef pvalue lower
2 Actinomyces -0.39434632 0.000000e+00 -0.406528015
3 Akkermansia 1.46135411 0.000000e+00 1.386121333
9 Bacteroides -0.04966677 0.000000e+00 -0.050982064
10 Bifidobacterium -0.07954183 0.000000e+00 -0.080833875
11 Blautia 0.13841508 0.000000e+00 0.136442246
16 Clostridium_XVIII 0.11857880 0.000000e+00 0.116691161
17 Clostridium_XlVa -0.15657996 0.000000e+00 -0.160371854
18 Clostridium_sensu_stricto 0.38921222 0.000000e+00 0.385873265
24 Enterobacter -0.76004419 0.000000e+00 -0.771088796
25 Enterococcus -0.49547437 0.000000e+00 -0.498030873
26 Escherichia/Shigella -0.24844840 0.000000e+00 -0.250815216
28 F__Erysipelotrichaceae 0.49057518 0.000000e+00 0.477177111
29 F__Lachnospiraceae -0.09299865 0.000000e+00 -0.096049157
30 F__Ruminococcaceae 1.00441507 0.000000e+00 0.973822936
31 Faecalibacterium 0.28707159 0.000000e+00 0.282063481
34 Fusicatenibacter -0.65870179 0.000000e+00 -0.668134702
40 Intestinibacter -0.40433353 0.000000e+00 -0.419099669
41 Klebsiella 0.22761828 0.000000e+00 0.221285984
45 Prevotella -0.60385712 0.000000e+00 -0.618500057
48 Roseburia -0.55473766 0.000000e+00 -0.573899521
52 Streptococcus 0.35403331 0.000000e+00 0.351524069
39 Hungatella -0.41317077 8.378552e-306 -0.434835380
19 Coprobacillus -0.45560443 1.643196e-271 -0.480969304
14 Clostridium_IV -0.32732773 4.096707e-260 -0.345949243
22 Eggerthella 0.33774304 1.339391e-243 0.317882803
38 Haemophilus 0.29050859 3.353858e-230 0.272931264
4 Alistipes 0.28641565 4.151235e-229 0.269044303
33 Flavonifractor 0.09169816 2.524485e-157 0.084972622
20 Corynebacterium -0.99767822 1.846661e-143 -1.074354819
50 Ruminococcus2 0.10468130 1.547901e-119 0.095855179
44 Peptoniphilus 0.51667364 8.736602e-101 0.469157320
7 Anaerotruncus -0.52961769 2.599846e-97 -0.579203424
54 Veillonella 0.04037813 4.056500e-93 0.036511764
51 Staphylococcus -0.10977691 3.938436e-67 -0.122206716
23 Eisenbergiella 0.13158439 1.388663e-52 0.114684872
21 Dialister -0.43641858 2.833673e-44 -0.497703418
56 Dorea 0.25307339 2.309591e-35 0.213103357
57 F__Peptostreptococcaceae -0.29110355 2.877232e-34 -0.337846922
35 Fusobacterium 0.28085291 5.490321e-34 0.235559967
47 Romboutsia -0.19136030 2.451915e-30 -0.224127548
15 Clostridium_XI -0.09034930 2.211555e-29 -0.106084467
43 Parasutterella 1.43931175 3.087444e-26 1.173091495
12 Buttiauxella -1.30494791 4.254659e-26 -1.547001954
8 Atopobium -0.31690269 1.783988e-25 -0.376447353
58 Turicibacter -0.43466432 1.039776e-22 -0.521526003
6 Anaerostipes 0.01964884 7.896663e-17 0.015027129
49 Rothia -0.22890121 3.398823e-13 -0.290549017
36 Gemella -0.21803551 1.996532e-10 -0.285211863
1 Acinetobacter -0.16825193 6.271810e-09 -0.225018020
53 Sutterella 0.05533172 7.625112e-09 0.036557487
46 Pseudomonas -0.08796856 6.155709e-04 -0.138314704
42 Lactococcus -0.02968543 1.510302e-02 -0.053630230
55 Clostridium_XlVb 0.21028491 2.240963e-02 0.029779068
27 Eubacterium 0.03094116 3.460606e-02 0.002239575
5 Anaerococcus -0.06852514 8.104484e-02 -0.145508398
upper fdr
2 -0.382164622 0.000000e+00
3 1.536586897 0.000000e+00
9 -0.048351467 0.000000e+00
10 -0.078249785 0.000000e+00
11 0.140387905 0.000000e+00
16 0.120466430 0.000000e+00
17 -0.152788061 0.000000e+00
18 0.392551166 0.000000e+00
24 -0.748999588 0.000000e+00
25 -0.492917868 0.000000e+00
26 -0.246081584 0.000000e+00
28 0.503973241 0.000000e+00
29 -0.089948150 0.000000e+00
30 1.035007208 0.000000e+00
31 0.292079697 0.000000e+00
34 -0.649268869 0.000000e+00
40 -0.389567387 0.000000e+00
41 0.233950580 0.000000e+00
45 -0.589214181 0.000000e+00
48 -0.535575804 0.000000e+00
52 0.356542551 0.000000e+00
39 -0.391506161 2.208891e-305
19 -0.430239566 4.143713e-271
14 -0.308706208 9.900374e-260
22 0.357603272 3.107386e-243
38 0.308085924 7.481683e-230
4 0.303786990 8.917467e-229
33 0.098423690 5.229290e-157
20 -0.921001619 3.693323e-143
50 0.113507425 2.992609e-119
44 0.564189955 1.634590e-100
7 -0.480031954 4.712221e-97
54 0.044244487 7.129607e-93
51 -0.097347109 6.718508e-67
23 0.148483908 2.301212e-52
21 -0.375133747 4.565363e-44
56 0.293043419 3.620439e-35
57 -0.244360185 4.391564e-34
35 0.326145854 8.165093e-34
47 -0.158593045 3.555277e-30
15 -0.074614131 3.128542e-29
43 1.705532002 4.263613e-26
12 -1.062893873 5.738843e-26
8 -0.257358022 2.351620e-25
58 -0.347802638 1.340156e-22
6 0.024270560 9.956662e-17
49 -0.167253396 4.194293e-13
36 -0.150859154 2.412476e-10
1 -0.111485830 7.423775e-09
53 0.074105959 8.845130e-09
46 -0.037622420 7.000610e-04
42 -0.005740635 1.684567e-02
55 0.390790760 2.452374e-02
27 0.059642747 3.716948e-02
5 0.008458120 8.546547e-02
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~cd4t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver, data=longicd4[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~cd4t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longicd4[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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 = "BY")
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.1),]
rrbcellfdr
microbe coef pvalue lower upper
2 Actinomyces -0.53669827 0.000000e+00 -0.54832404 -0.52507250
3 Akkermansia 0.62660495 0.000000e+00 0.60389677 0.64931313
9 Bacteroides 0.19146050 0.000000e+00 0.19055067 0.19237033
10 Bifidobacterium -0.12121164 0.000000e+00 -0.12196241 -0.12046088
11 Blautia -0.03674316 0.000000e+00 -0.03783307 -0.03565324
15 Clostridium_XVIII 0.06144901 0.000000e+00 0.06004493 0.06285309
16 Clostridium_XlVa -0.06608792 0.000000e+00 -0.06819711 -0.06397873
17 Clostridium_sensu_stricto -0.07394477 0.000000e+00 -0.07559486 -0.07229468
23 Enterobacter -0.36890195 0.000000e+00 -0.37511474 -0.36268915
24 Enterococcus -0.17980241 0.000000e+00 -0.18102288 -0.17858193
25 Escherichia/Shigella 0.08136018 0.000000e+00 0.07992963 0.08279073
27 F__Enterobacteriaceae -0.39302887 0.000000e+00 -0.39797774 -0.38808000
28 F__Erysipelotrichaceae -0.30824133 0.000000e+00 -0.31366426 -0.30281840
29 F__Lachnospiraceae 0.09367065 0.000000e+00 0.09199954 0.09534176
30 F__Ruminococcaceae 0.30368820 0.000000e+00 0.29276823 0.31460817
31 Faecalibacterium 0.28374494 0.000000e+00 0.28020814 0.28728173
33 Flavonifractor -0.24100200 0.000000e+00 -0.24672048 -0.23528351
34 Fusicatenibacter -0.44706982 0.000000e+00 -0.45345134 -0.44068829
38 Haemophilus -0.36411643 0.000000e+00 -0.37431746 -0.35391540
39 Intestinibacter -0.20255507 0.000000e+00 -0.21002812 -0.19508202
40 Klebsiella -0.12936684 0.000000e+00 -0.13178796 -0.12694573
44 Prevotella -0.26171536 0.000000e+00 -0.26711194 -0.25631878
47 Roseburia -0.13755040 0.000000e+00 -0.14404848 -0.13105231
50 Staphylococcus -0.27964282 0.000000e+00 -0.28623500 -0.27305063
51 Streptococcus -0.09377999 0.000000e+00 -0.09517119 -0.09238880
53 Terrisporobacter -0.33533690 0.000000e+00 -0.34215417 -0.32851963
54 Veillonella -0.20868232 0.000000e+00 -0.21091647 -0.20644817
6 Anaerostipes 0.06155075 1.335940e-298 0.05828436 0.06481714
21 Eggerthella 0.18593756 4.480538e-226 0.17458418 0.19729093
13 Clostridium_IV -0.12389657 2.557501e-205 -0.13183867 -0.11595446
19 Corynebacterium -0.52667201 4.937706e-137 -0.56810159 -0.48524244
43 Peptoniphilus 0.29426870 1.517172e-98 0.26689414 0.32164326
4 Alistipes 0.08912914 2.500072e-91 0.08050968 0.09774860
37 Granulicatella -0.22378381 1.476356e-90 -0.24551932 -0.20204829
8 Atopobium -0.41575266 1.483879e-89 -0.45636382 -0.37514150
22 Eisenbergiella 0.13544000 4.159306e-70 0.12044251 0.15043749
48 Rothia -0.25724924 2.692172e-63 -0.28727206 -0.22722642
52 Sutterella 0.13759739 4.236486e-63 0.12151301 0.15368177
18 Coprobacillus 0.14475410 5.585819e-58 0.12707855 0.16242966
55 Dorea 0.13159473 7.602219e-54 0.11489998 0.14828949
41 Lactococcus -0.09224320 4.777220e-46 -0.10493464 -0.07955176
42 Parasutterella 0.26697357 3.619040e-39 0.22700753 0.30693960
20 Dialister -0.15714845 8.668078e-39 -0.18079372 -0.13350319
14 Clostridium_XI 0.05085363 7.741716e-32 0.04236520 0.05934207
35 Fusobacterium 0.18471356 2.242715e-30 0.15310580 0.21632132
36 Gemella -0.20474105 3.386839e-30 -0.23988564 -0.16959646
45 Pseudomonas -0.15272423 3.026013e-28 -0.17988500 -0.12556346
56 F__Peptostreptococcaceae -0.12158914 6.686999e-22 -0.14636613 -0.09681216
7 Anaerotruncus -0.11771718 6.216697e-19 -0.14367614 -0.09175822
1 Acinetobacter -0.10689318 5.122589e-11 -0.13879514 -0.07499123
46 Romboutsia -0.06151209 9.363301e-10 -0.08121235 -0.04181182
5 Anaerococcus 0.09886900 5.776200e-06 0.05613294 0.14160506
26 Eubacterium -0.03320789 4.001883e-04 -0.05159439 -0.01482139
fdr
2 0.000000e+00
3 0.000000e+00
9 0.000000e+00
10 0.000000e+00
11 0.000000e+00
15 0.000000e+00
16 0.000000e+00
17 0.000000e+00
23 0.000000e+00
24 0.000000e+00
25 0.000000e+00
27 0.000000e+00
28 0.000000e+00
29 0.000000e+00
30 0.000000e+00
31 0.000000e+00
33 0.000000e+00
34 0.000000e+00
38 0.000000e+00
39 0.000000e+00
40 0.000000e+00
44 0.000000e+00
47 0.000000e+00
50 0.000000e+00
51 0.000000e+00
53 0.000000e+00
54 0.000000e+00
6 1.232130e-297
21 3.989877e-225
13 2.201517e-204
19 4.113304e-136
43 1.224369e-97
4 1.956437e-90
37 1.121345e-89
8 1.094858e-88
22 2.983635e-69
48 1.879007e-62
52 2.879052e-62
18 3.698704e-57
55 4.908036e-53
41 3.008976e-45
42 2.225212e-38
20 5.205731e-38
14 4.543724e-31
35 1.287031e-29
36 1.901359e-29
45 1.662648e-27
56 3.597637e-21
7 3.276355e-18
1 2.645738e-10
46 4.741177e-09
5 2.868575e-05
26 1.949916e-03
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~cd8t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longicd8[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~cd8t + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longicd8[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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 = "BY")
rrbcell=data.frame(microbe,rrbcell,fdr)
rrbcell=rrbcell[order(rrbcell[,3]),]
rrbcellfdr=rrbcell[which(rrbcell[,6]<0.1),]
rrbcellfdr
microbe coef pvalue lower
3 Akkermansia 0.434404177 0.000000e+00 0.41674089
6 Anaerostipes 0.136827178 0.000000e+00 0.13200739
9 Bacteroides -0.124926929 0.000000e+00 -0.12632632
10 Bifidobacterium -0.193670475 0.000000e+00 -0.19514562
11 Blautia -0.265738939 0.000000e+00 -0.26803151
15 Clostridium_XI 0.365357399 0.000000e+00 0.35092988
16 Clostridium_XVIII 0.347048731 0.000000e+00 0.34405157
17 Clostridium_XlVa 0.096104830 0.000000e+00 0.09253855
18 Clostridium_sensu_stricto 0.095930278 0.000000e+00 0.09322181
26 Escherichia/Shigella 0.163711677 0.000000e+00 0.16115239
28 F__Enterobacteriaceae 1.302204909 0.000000e+00 1.28845797
30 F__Lachnospiraceae -0.067517524 0.000000e+00 -0.07037841
31 F__Ruminococcaceae -0.914502520 0.000000e+00 -0.94721050
32 Faecalibacterium -0.095835282 0.000000e+00 -0.10078733
39 Haemophilus 0.849503693 0.000000e+00 0.82745858
46 Prevotella 0.284986078 0.000000e+00 0.27924804
51 Ruminococcus2 -0.420869307 0.000000e+00 -0.43159678
52 Staphylococcus 0.254406863 0.000000e+00 0.24468952
53 Streptococcus 0.317552636 0.000000e+00 0.31490263
56 Veillonella 0.130407713 0.000000e+00 0.12653722
41 Intestinibacter 0.215727794 1.406841e-252 0.20327186
13 Butyricicoccus -0.467423923 1.402676e-135 -0.50439236
25 Enterococcus 0.025307559 1.485667e-135 0.02330580
19 Coprobacillus -0.216283445 6.306860e-131 -0.23369518
24 Enterobacter -0.129437406 1.481028e-124 -0.14012505
20 Corynebacterium -0.890484918 1.503918e-120 -0.96524421
35 Fusicatenibacter 0.066566897 3.223537e-111 0.06074460
54 Sutterella -0.255528956 2.135271e-107 -0.27828024
49 Roseburia 0.089426430 2.068974e-60 0.07873576
40 Hungatella 0.267696625 2.715563e-54 0.23388026
50 Rothia 0.347940255 5.290368e-52 0.30299618
55 Varibaculum 0.314506898 3.351490e-46 0.27130993
34 Flavonifractor 0.064647758 3.036906e-44 0.05556627
14 Clostridium_IV -0.081282917 1.137743e-32 -0.09466699
37 Gemella 0.302754193 1.781540e-26 0.24702558
36 Fusobacterium 0.192754301 1.299188e-24 0.15586344
58 Dorea -0.204264871 2.038076e-23 -0.24441653
29 F__Erysipelotrichaceae -0.048081314 2.968298e-21 -0.05803915
44 Parasutterella -0.351100352 9.371500e-21 -0.42475579
48 Romboutsia 0.084645729 6.558846e-18 0.06540440
42 Lactococcus -0.116389606 1.954959e-17 -0.14323893
4 Alistipes -0.045734542 2.234651e-17 -0.05630414
7 Anaerotruncus 0.149220450 3.088407e-13 0.10910349
21 Dialister -0.133507772 3.231944e-11 -0.17294285
8 Atopobium -0.186018868 4.648308e-11 -0.24141371
59 F__Peptostreptococcaceae 0.106809131 1.314417e-10 0.07422862
5 Anaerococcus -0.205273827 4.593098e-10 -0.26982937
2 Actinomyces -0.033144994 1.011526e-09 -0.04378164
45 Peptoniphilus 0.069806524 2.261752e-04 0.03270590
47 Pseudomonas -0.083807059 2.652697e-04 -0.12884672
12 Buttiauxella -0.175845232 4.054269e-04 -0.27330151
38 Granulicatella 0.047937217 4.619431e-03 0.01476548
43 Parabacteroides -0.006593554 7.047075e-03 -0.01138956
upper fdr
3 0.452067463 0.000000e+00
6 0.141646968 0.000000e+00
9 -0.123527535 0.000000e+00
10 -0.192195328 0.000000e+00
11 -0.263446370 0.000000e+00
15 0.379784915 0.000000e+00
16 0.350045890 0.000000e+00
17 0.099671107 0.000000e+00
18 0.098638745 0.000000e+00
26 0.166270964 0.000000e+00
28 1.315951852 0.000000e+00
30 -0.064656642 0.000000e+00
31 -0.881794544 0.000000e+00
32 -0.090883231 0.000000e+00
39 0.871548810 0.000000e+00
46 0.290724112 0.000000e+00
51 -0.410141836 0.000000e+00
52 0.264124210 0.000000e+00
53 0.320202639 0.000000e+00
56 0.134278210 0.000000e+00
41 0.228183723 1.843156e-251
13 -0.430455481 1.754168e-134
25 0.027309315 1.777175e-134
19 -0.198871712 7.230001e-130
24 -0.118749765 1.629895e-123
20 -0.815725623 1.591429e-119
35 0.072389189 3.284773e-110
54 -0.232777677 2.098125e-106
49 0.100117096 1.962878e-59
40 0.301512991 2.490434e-53
50 0.392884331 4.695271e-51
55 0.357703864 2.881538e-45
34 0.073729245 2.531943e-43
14 -0.067898840 9.206648e-32
37 0.358482808 1.400438e-25
36 0.229645162 9.929007e-24
58 -0.164113211 1.515497e-22
29 -0.038123480 2.149118e-20
44 -0.277444917 6.611209e-20
48 0.103887055 4.511322e-17
42 -0.089540283 1.311868e-16
4 -0.035164940 1.463851e-16
7 0.189337408 1.976071e-12
21 -0.094072695 2.020913e-10
8 -0.130624027 2.841965e-10
59 0.139389643 7.861613e-10
5 -0.140718282 2.688712e-09
2 -0.022508346 5.797920e-09
45 0.106907151 1.269946e-03
47 -0.038767399 1.459668e-03
12 -0.078388951 2.187151e-03
38 0.081108957 2.444114e-02
43 -0.001797551 3.658216e-02
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~mono + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longimono[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~mono + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longimono[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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]<0.1),]
rrbcellfdr
microbe coef pvalue lower
2 Actinomyces -0.43979602 0.000000e+00 -0.4513223483
3 Akkermansia 0.54220075 0.000000e+00 0.5219880589
9 Bacteroides 0.03602320 0.000000e+00 0.0348187237
10 Bifidobacterium -0.18026823 0.000000e+00 -0.1815271508
11 Blautia -0.09629720 0.000000e+00 -0.0983302017
13 Clostridium_IV -0.41872245 0.000000e+00 -0.4388140802
15 Clostridium_XVIII 0.13790398 0.000000e+00 0.1361153531
16 Clostridium_XlVa -0.12909909 0.000000e+00 -0.1323831129
18 Coprobacillus -0.76289771 0.000000e+00 -0.7977756657
24 Enterococcus -0.20805085 0.000000e+00 -0.2101514986
25 Escherichia/Shigella 0.16372658 0.000000e+00 0.1616618634
27 F__Enterobacteriaceae 1.07327959 0.000000e+00 1.0622192199
29 F__Lachnospiraceae 0.17832290 0.000000e+00 0.1754435944
33 Flavonifractor -0.35073356 0.000000e+00 -0.3614842486
37 Haemophilus 0.44752514 0.000000e+00 0.4265179974
40 Parabacteroides -0.32764523 0.000000e+00 -0.3327539100
47 Ruminococcus2 -0.16805259 0.000000e+00 -0.1751910056
49 Streptococcus 0.17367307 0.000000e+00 0.1711789534
51 Terrisporobacter -0.44978370 0.000000e+00 -0.4630391001
53 Veillonella 0.09076814 0.000000e+00 0.0867551046
28 F__Erysipelotrichaceae -0.23171421 4.978069e-282 -0.2443696948
14 Clostridium_XI 0.27297335 6.270172e-247 0.2570312897
23 Enterobacter -0.15802454 2.988105e-227 -0.1676483140
43 Prevotella 0.10406623 2.794285e-125 0.0954987577
30 F__Ruminococcaceae 0.22570773 1.626771e-119 0.2066756131
19 Corynebacterium -1.30115440 8.756296e-110 -1.4157144905
17 Clostridium_sensu_stricto -0.02513382 1.141110e-71 -0.0278856228
7 Anaerotruncus -0.46187162 3.581101e-69 -0.5133693678
39 Lactococcus -0.23280874 1.437306e-67 -0.2590812242
20 Dialister -0.48779802 5.850303e-53 -0.5502161535
42 Peptoniphilus 0.27114136 6.421749e-48 0.2346001630
35 Gemella -0.47973681 8.828421e-42 -0.5491711893
36 Granulicatella -0.26143905 1.292595e-37 -0.3014142879
8 Atopobium -0.36764021 1.462680e-34 -0.4264087840
50 Sutterella -0.12617641 1.350039e-30 -0.1476849940
48 Staphylococcus 0.05947593 3.434276e-29 0.0490817401
34 Fusobacterium 0.91569516 5.260155e-29 0.7551246722
5 Anaerococcus -0.42334878 4.555305e-25 -0.5035821571
46 Rothia -0.24782316 2.099379e-19 -0.3017458801
45 Romboutsia 0.10481712 1.332718e-18 0.0814792960
21 Eggerthella 0.07268707 4.866772e-18 0.0562292098
26 Eubacterium -0.16689520 8.540361e-14 -0.2107342643
4 Alistipes 0.03230738 4.290865e-11 0.0227038546
12 Buttiauxella -0.29800968 9.674539e-11 -0.3882605077
52 Varibaculum 0.10704392 9.154692e-10 0.0727814276
6 Anaerostipes -0.01336692 2.944050e-09 -0.0177815007
56 F__Peptostreptococcaceae 0.11184051 1.916970e-08 0.0728310024
41 Parasutterella 0.11212164 8.287768e-08 0.0711276599
54 Clostridium_XlVb -0.46152901 4.193909e-06 -0.6581149459
38 Intestinibacter 0.02719835 4.127543e-05 0.0141969130
55 Dorea 0.04638435 1.155887e-03 0.0184071241
32 Finegoldia -0.09079764 1.302846e-03 -0.1461456661
44 Pseudomonas -0.05245429 1.968326e-02 -0.0965348248
31 Faecalibacterium 0.00418020 5.571906e-02 -0.0001021951
22 Eisenbergiella 0.01696497 7.228281e-02 -0.0015354107
upper fdr
2 -0.428269701 0.000000e+00
3 0.562413434 0.000000e+00
9 0.037227671 0.000000e+00
10 -0.179009311 0.000000e+00
11 -0.094264190 0.000000e+00
13 -0.398630813 0.000000e+00
15 0.139692609 0.000000e+00
16 -0.125815071 0.000000e+00
18 -0.728019749 0.000000e+00
24 -0.205950192 0.000000e+00
25 0.165791306 0.000000e+00
27 1.084339957 0.000000e+00
29 0.181202204 0.000000e+00
33 -0.339982871 0.000000e+00
37 0.468532276 0.000000e+00
40 -0.322536547 0.000000e+00
47 -0.160914179 0.000000e+00
49 0.176167188 0.000000e+00
51 -0.436528294 0.000000e+00
53 0.094781182 0.000000e+00
28 -0.219058725 1.327485e-281
14 0.288915406 1.596044e-246
23 -0.148400776 7.275387e-227
43 0.112633693 6.519997e-125
30 0.244739853 3.643968e-119
19 -1.186594316 1.885972e-109
17 -0.022382019 2.366746e-71
7 -0.410373867 7.162201e-69
39 -0.206536247 2.775488e-67
20 -0.425379886 1.092057e-52
42 0.307682548 1.160058e-47
35 -0.410302435 1.544974e-41
36 -0.221463816 2.193495e-37
8 -0.308871639 2.409120e-34
50 -0.104667824 2.160062e-30
48 0.069870110 5.342207e-29
34 1.076265647 7.961315e-29
5 -0.343115410 6.713082e-25
46 -0.193900430 3.014493e-19
45 0.128154936 1.865806e-18
21 0.089144926 6.647298e-18
26 -0.123056136 1.138715e-13
4 0.041910902 5.588103e-11
12 -0.207758847 1.231305e-10
52 0.141306419 1.139251e-09
6 -0.008952338 3.584061e-09
56 0.150850028 2.284049e-08
41 0.153115610 9.669062e-08
54 -0.264943070 4.793039e-06
38 0.040199778 4.622848e-05
55 0.074361574 1.269210e-03
32 -0.035449623 1.403064e-03
44 -0.008373759 2.079741e-02
31 0.008462596 5.778272e-02
22 0.035465342 7.359704e-02
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~nk + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longink[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~nk + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver, data=longink[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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]<0.1),]
rrbcellfdr
rrbcellfdr
microbe coef pvalue lower upper
2 Actinomyces 0.21714501 0.000000e+00 0.21065255 0.223637467
3 Akkermansia 0.32174897 0.000000e+00 0.30924170 0.334256237
6 Anaerostipes 0.09339175 0.000000e+00 0.09028773 0.096495769
9 Bacteroides -0.05029753 0.000000e+00 -0.05110555 -0.049489522
10 Bifidobacterium -0.12863773 0.000000e+00 -0.12961710 -0.127658354
11 Blautia -0.09253996 0.000000e+00 -0.09391577 -0.091164146
15 Clostridium_XI 0.23933613 0.000000e+00 0.23022246 0.248449801
18 Clostridium_sensu_stricto 0.08603289 0.000000e+00 0.08449337 0.087572409
19 Coprobacillus -0.33381136 0.000000e+00 -0.34582277 -0.321799950
24 Enterobacter -0.27179446 0.000000e+00 -0.27884042 -0.264748493
25 Enterococcus 0.06045612 0.000000e+00 0.05910238 0.061809862
28 F__Enterobacteriaceae 0.71001312 0.000000e+00 0.70250296 0.717523283
29 F__Erysipelotrichaceae 0.26745672 0.000000e+00 0.25998760 0.274925831
30 F__Lachnospiraceae -0.05566937 0.000000e+00 -0.05756143 -0.053777308
34 Flavonifractor 0.23603225 0.000000e+00 0.23000469 0.242059804
35 Fusicatenibacter -0.20587189 0.000000e+00 -0.21196095 -0.199782824
39 Haemophilus 0.17998306 0.000000e+00 0.17090599 0.189060132
42 Klebsiella 0.07150698 0.000000e+00 0.06818248 0.074831468
47 Prevotella 0.07905975 0.000000e+00 0.07570228 0.082417227
50 Roseburia -0.35685441 0.000000e+00 -0.36811637 -0.345592439
52 Ruminococcus2 -0.16876823 0.000000e+00 -0.17487350 -0.162662956
54 Streptococcus 0.03925520 0.000000e+00 0.03780918 0.040701230
56 Terrisporobacter 0.36812803 0.000000e+00 0.36207177 0.374184303
16 Clostridium_XVIII 0.03247688 3.568533e-302 0.03076368 0.034190074
26 Escherichia/Shigella -0.03344387 1.443865e-207 -0.03557595 -0.031311786
20 Corynebacterium -0.92451699 6.803489e-201 -0.98443694 -0.864597034
13 Butyricicoccus -0.29991542 1.888359e-199 -0.31942454 -0.280406304
55 Sutterella 0.24803107 2.007291e-199 0.23189587 0.264166256
14 Clostridium_IV -0.15890737 7.761735e-183 -0.16970868 -0.148106049
53 Staphylococcus 0.07273543 5.687206e-101 0.06605256 0.079418307
17 Clostridium_XlVa -0.01942074 5.427047e-82 -0.02140533 -0.017436152
36 Fusobacterium 0.33738151 1.320312e-69 0.29988465 0.374878367
41 Intestinibacter 0.07267691 2.165038e-66 0.06440081 0.080953010
23 Eisenbergiella 0.16702905 2.718529e-52 0.14551547 0.188542644
31 F__Ruminococcaceae -0.09969405 4.399283e-52 -0.11256145 -0.086826646
58 Veillonella -0.01642436 3.812711e-36 -0.01898877 -0.013859957
21 Dialister -0.15166092 3.472012e-27 -0.17918660 -0.124135242
22 Eggerthella -0.05188764 5.726545e-22 -0.06244362 -0.041331657
8 Atopobium -0.17148724 3.086109e-21 -0.20701821 -0.135956261
44 Parabacteroides 0.01359972 1.326974e-16 0.01037701 0.016822430
43 Lactococcus -0.06256715 4.121895e-16 -0.07764186 -0.047492445
59 Clostridium_XlVb 0.43536663 1.055652e-15 0.32897058 0.541762679
12 Buttiauxella -0.33963380 4.767045e-15 -0.42461911 -0.254648501
32 Faecalibacterium -0.01642859 7.355225e-14 -0.02073262 -0.012124558
5 Anaerococcus -0.14393323 1.010320e-13 -0.18185343 -0.106013019
48 Pseudomonas -0.12463168 2.575981e-13 -0.15802633 -0.091237030
27 Eubacterium -0.09401617 2.115072e-06 -0.13287479 -0.055157557
33 Finegoldia -0.07903298 1.058240e-05 -0.11419927 -0.043866684
46 Peptoniphilus -0.05643194 1.399846e-05 -0.08189453 -0.030969341
7 Anaerotruncus 0.05658493 5.391193e-05 0.02911915 0.084050709
38 Granulicatella 0.03892541 9.942891e-05 0.01932259 0.058528227
51 Rothia 0.06261323 1.679444e-04 0.02999970 0.095226760
60 F__Peptostreptococcaceae -0.04796961 4.059045e-04 -0.07455749 -0.021381736
40 Hungatella 0.03411931 1.013341e-03 0.01377310 0.054465514
1 Acinetobacter -0.03696525 7.436600e-02 -0.07756922 0.003638717
fdr
2 0.000000e+00
3 0.000000e+00
6 0.000000e+00
9 0.000000e+00
10 0.000000e+00
11 0.000000e+00
15 0.000000e+00
18 0.000000e+00
19 0.000000e+00
24 0.000000e+00
25 0.000000e+00
28 0.000000e+00
29 0.000000e+00
30 0.000000e+00
34 0.000000e+00
35 0.000000e+00
39 0.000000e+00
42 0.000000e+00
47 0.000000e+00
50 0.000000e+00
52 0.000000e+00
54 0.000000e+00
56 0.000000e+00
16 8.921333e-302
26 3.465276e-207
20 1.570036e-200
13 4.196354e-199
55 4.301338e-199
14 1.605876e-182
53 1.137441e-100
17 1.050396e-81
36 2.475586e-69
41 3.936433e-66
23 4.797403e-52
31 7.541628e-52
58 6.354518e-36
21 5.630290e-27
22 9.041914e-22
8 4.747861e-21
44 1.990461e-16
43 6.032042e-16
59 1.508074e-15
12 6.651691e-15
32 1.002985e-13
5 1.347094e-13
48 3.359975e-13
27 2.700091e-06
33 1.322800e-05
46 1.714097e-05
7 6.469432e-05
38 1.169752e-04
51 1.937820e-04
60 4.595146e-04
40 1.125934e-03
1 8.112655e-02
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~nrbc + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longinrbc[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~nrbc + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longinrbc[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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]<0.1),]
rrbcellfdr
rrbcellfdr
microbe coef pvalue lower
2 Actinomyces 0.231907580 0.000000e+00 0.227480101
6 Anaerostipes 0.047020818 0.000000e+00 0.044746084
9 Bacteroides -0.026964459 0.000000e+00 -0.027498346
11 Blautia -0.042375003 0.000000e+00 -0.043082722
17 Clostridium_XlVa 0.032128195 0.000000e+00 0.030484167
18 Clostridium_sensu_stricto -0.030738075 0.000000e+00 -0.031773718
19 Coprobacillus -0.303852850 0.000000e+00 -0.317011138
24 Enterobacter 0.072044647 0.000000e+00 0.068292196
25 Enterococcus 0.101870987 0.000000e+00 0.101037890
26 Escherichia/Shigella -0.037866592 0.000000e+00 -0.038687924
28 F__Erysipelotrichaceae 0.222001130 0.000000e+00 0.214493508
29 F__Lachnospiraceae 0.024885816 0.000000e+00 0.023831942
30 F__Ruminococcaceae -0.146180548 0.000000e+00 -0.152207661
31 Faecalibacterium -0.063695341 0.000000e+00 -0.065475993
37 Haemophilus -0.348354253 0.000000e+00 -0.358200224
42 Parabacteroides 0.341561231 0.000000e+00 0.336781115
47 Roseburia -0.109115572 0.000000e+00 -0.113811937
50 Staphylococcus -0.129623673 0.000000e+00 -0.134194901
51 Streptococcus -0.107663119 0.000000e+00 -0.108701688
49 Ruminococcus2 -0.079814772 1.424245e-288 -0.084123913
56 Dorea -0.347868796 1.284978e-238 -0.368538794
22 Eggerthella -0.108544407 2.888999e-216 -0.115322236
39 Intestinibacter 0.105680099 5.643644e-211 0.098998070
38 Hungatella 0.131221309 1.054331e-202 0.122755019
33 Flavonifractor 0.043471338 2.442534e-193 0.040598756
4 Alistipes -0.096537402 1.906243e-159 -0.103569980
3 Akkermansia -0.166258773 4.828302e-142 -0.179101105
10 Bifidobacterium -0.005911343 3.783829e-126 -0.006396302
16 Clostridium_XVIII 0.008759725 1.152147e-95 0.007932420
13 Butyricicoccus -0.188284410 6.518477e-92 -0.206433771
15 Clostridium_XI 0.051187005 1.886995e-70 0.045533185
44 Peptoniphilus -0.153847067 6.707376e-65 -0.171572363
54 Veillonella -0.010342958 8.267904e-61 -0.011575246
43 Parasutterella -0.267822174 1.270068e-48 -0.303643885
46 Romboutsia -0.087546467 2.533693e-45 -0.099691471
53 Varibaculum 0.287667766 8.939628e-38 0.243779833
58 Turicibacter 0.243982951 1.007678e-33 0.204474328
20 Corynebacterium 0.168330278 2.184426e-32 0.140485354
57 F__Peptostreptococcaceae 0.114335018 1.688266e-31 0.095142287
7 Anaerotruncus 0.102433784 2.555141e-30 0.084888229
40 Klebsiella 0.011088441 2.754351e-24 0.008951020
14 Clostridium_IV -0.033453810 5.701329e-21 -0.040432718
5 Anaerococcus -0.099739388 7.188575e-13 -0.126982233
48 Rothia -0.058423963 2.161752e-11 -0.075528788
52 Sutterella -0.047262304 5.282337e-11 -0.061377434
32 Finegoldia -0.056572981 6.417221e-10 -0.074515725
21 Dialister -0.067500744 2.887350e-09 -0.089781677
34 Fusobacterium -0.050374476 2.129897e-06 -0.071201389
55 Clostridium_XlVb 0.097007961 4.886109e-06 0.055399330
27 Eubacterium -0.039878125 1.093663e-05 -0.057651005
8 Atopobium 0.043999460 1.220049e-05 0.024283078
45 Pseudomonas -0.037768905 4.311241e-05 -0.055867837
36 Granulicatella 0.024053534 1.308066e-04 0.011727854
23 Eisenbergiella -0.016481662 3.020261e-04 -0.025421349
41 Lactococcus 0.009536642 1.432168e-02 0.001904500
35 Gemella 0.020074353 6.818193e-02 -0.001499108
upper fdr
2 0.236335060 0.000
6 0.049295552 0.000
9 -0.026430572 0.000
11 -0.041667283 0.000
17 0.033772224 0.000
18 -0.029702432 0.000
19 -0.290694563 0.000
24 0.075797097 0.000
25 0.102704083 0.000
26 -0.037045260 0.000
28 0.229508753 0.000
29 0.025939690 0.000
30 -0.140153435 0.000
31 -0.061914688 0.000
37 -0.338508282 0.000
42 0.346341347 0.000
47 -0.104419207 0.000
50 -0.125052445 0.000
51 -0.106624550 0.000
49 -0.075505630 0.000
56 -0.327198797 0.000
22 -0.101766577 0.000
39 0.112362127 0.000
38 0.139687599 0.000
33 0.046343921 0.000
4 -0.089504824 0.000
3 -0.153416440 0.000
10 -0.005426384 0.000
16 0.009587031 0.000
13 -0.170135050 0.000
15 0.056840825 0.000
44 -0.136121772 0.000
54 -0.009110671 0.000
43 -0.232000464 0.000
46 -0.075401464 0.000
53 0.331555698 0.000
58 0.283491575 0.000
20 0.196175201 0.000
57 0.133527748 0.000
7 0.119979339 0.000
40 0.013225861 0.000
14 -0.026474903 0.000
5 -0.072496542 0.000
48 -0.041319139 0.000
52 -0.033147173 0.000
32 -0.038630236 0.000
21 -0.045219811 0.000
34 -0.029547564 0.000
55 0.138616593 0.000
27 -0.022105245 0.000
8 0.063715842 0.000
45 -0.019669974 0.000
36 0.036379215 0.000
23 -0.007541976 0.000
41 0.017168783 0.015
35 0.041647813 0.071
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[74:117,k+2]
y2=apply(longidata[74:117,-c(1,2,k+2)],1,sum)
if (length(which(y1>0))<=5) {next}
fm=try({glm(cbind(y1,y2)~gran + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longigran[74:117,],family="binomial")},silent = T)
if (inherits(fm,"try-error")){
next()
}else{
fm=glm(cbind(y1,y2)~gran + bbymale + enrollment_age + imrbthwghtg_all +gestage_all+evercigpreg+alpha+deliver , data=longigran[74:117,],family="binomial")
a=summary(fm)
r1=a$coefficients[2,1]
r2=a$coefficients[2,4]
r3= c(r1-1.96*a$coefficients[2,2],r1+ 1.96*a$coefficients[2,2])
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]<0.1),]
rrbcellfdr
microbe coef pvalue lower upper fdr
1 Acinetobacter 0.030 0.000 0.015 0.044 0.000
2 Actinomyces -0.057 0.000 -0.060 -0.054 0.000
3 Akkermansia -0.122 0.000 -0.126 -0.118 0.000
4 Alistipes -0.005 0.000 -0.008 -0.003 0.000
5 Anaerococcus 0.067 0.000 0.053 0.081 0.000
6 Anaerostipes -0.044 0.000 -0.045 -0.043 0.000
7 Anaerotruncus 0.071 0.000 0.054 0.087 0.000
8 Atopobium 0.094 0.000 0.081 0.106 0.000
9 Bacteroides -0.007 0.000 -0.008 -0.007 0.000
10 Bifidobacterium 0.068 0.000 0.068 0.069 0.000
11 Blautia 0.056 0.000 0.055 0.057 0.000
12 Buttiauxella 0.317 0.000 0.231 0.403 0.000
13 Butyricicoccus 0.125 0.000 0.117 0.134 0.000
14 Clostridium_IV 0.092 0.000 0.087 0.097 0.000
15 Clostridium_XI -0.084 0.000 -0.088 -0.080 0.000
16 Clostridium_XVIII -0.064 0.000 -0.065 -0.064 0.000
17 Clostridium_XlVa 0.012 0.000 0.011 0.012 0.000
18 Clostridium_sensu_stricto -0.023 0.000 -0.023 -0.022 0.000
19 Coprobacillus 0.137 0.000 0.132 0.142 0.000
20 Corynebacterium 0.259 0.000 0.242 0.276 0.000
21 Dialister 0.095 0.000 0.081 0.109 0.000
23 Eisenbergiella -0.094 0.000 -0.103 -0.085 0.000
24 Enterobacter 0.124 0.000 0.121 0.126 0.000
25 Enterococcus 0.022 0.000 0.021 0.022 0.000
26 Escherichia/Shigella 0.002 0.000 0.001 0.003 0.000
27 Eubacterium 0.036 0.000 0.025 0.048 0.000
28 F__Erysipelotrichaceae -0.118 0.000 -0.124 -0.113 0.000
29 F__Lachnospiraceae -0.011 0.000 -0.011 -0.010 0.000
30 F__Ruminococcaceae -0.045 0.000 -0.050 -0.039 0.000
31 Faecalibacterium -0.059 0.000 -0.061 -0.057 0.000
32 Finegoldia 0.028 0.000 0.017 0.040 0.000
33 Flavonifractor -0.066 0.000 -0.069 -0.064 0.000
34 Fusicatenibacter 0.058 0.000 0.056 0.060 0.000
35 Fusobacterium -0.105 0.000 -0.118 -0.092 0.000
36 Gemella 0.058 0.000 0.042 0.074 0.000
37 Granulicatella 0.022 0.000 0.014 0.031 0.000
38 Haemophilus 0.039 0.000 0.035 0.043 0.000
39 Intestinibacter 0.013 0.000 0.010 0.017 0.000
41 Lactococcus 0.039 0.000 0.034 0.044 0.000
42 Parabacteroides -0.014 0.000 -0.015 -0.013 0.000
43 Parasutterella -0.019 0.000 -0.030 -0.009 0.000
44 Peptoniphilus -0.035 0.000 -0.045 -0.026 0.000
45 Prevotella 0.045 0.000 0.043 0.048 0.000
46 Pseudomonas 0.058 0.000 0.046 0.070 0.000
47 Romboutsia 0.015 0.000 0.011 0.020 0.000
48 Roseburia 0.136 0.000 0.131 0.140 0.000
49 Rothia 0.086 0.000 0.072 0.100 0.000
50 Ruminococcus2 0.052 0.000 0.050 0.054 0.000
51 Staphylococcus 0.081 0.000 0.078 0.084 0.000
52 Streptococcus -0.007 0.000 -0.008 -0.007 0.000
54 Terrisporobacter -0.114 0.000 -0.118 -0.111 0.000
55 Varibaculum 0.349 0.000 0.318 0.380 0.000
56 Veillonella 0.026 0.000 0.025 0.027 0.000
22 Eggerthella -0.007 0.002 -0.011 -0.002 0.002
57 Clostridium_XlVb -0.072 0.003 -0.120 -0.024 0.003
40 Klebsiella -0.002 0.005 -0.003 -0.001 0.005
53 Sutterella -0.006 0.063 -0.013 0.000 0.064
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
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