Inflammatory Bowel Disease (IBD) Study
Soham Ghosh
2024-06-18
Last updated: 2024-06-18
Checks: 6 1
Knit directory: zinck-website/
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IBD Data Analysis
Out of the ten uniformly processed 16S rRNA gene sequencing studies of the IBD mucosal/stool microbiome (https://github.com/biobakery/ibd_paper/tree/paper_publication), we focus on five studies – RISK (430 cases, 201 controls), CS PRISM (359 cases, 38 controls), HMP2 (59 cases, 22 controls), Pouchitis (308 cases, 45 controls), and Mucosal IBD (36 cases, 47 controls). Here cases indicate patients with Ulcerative Colitis (UC) or Crohn’s Disease (CD). We included all \(249\) IBD genera in our analyses.
library(zinck)
library(reshape2)
library(knockoff)
library(ggplot2)
library(rstan)Loading required package: StanHeadersrstan (Version 2.21.8, GitRev: 2e1f913d3ca3)For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)library(phyloseq)
################################################################################
###################### IBD data genus level ####################################
load("/Users/Patron/Documents/zinLDA research/genera.RData") ## Loading the meta IBD studies
combined_studies <- as.data.frame(t(physeq_genera@otu_table))
study_names <- physeq_genera@sam_data[["dataset_name"]]
#### Study : RISK ####
risk_indices <- which(study_names == "RISK")
risk_otu <- combined_studies[risk_indices, ]
IBD_resp <- physeq_genera@sam_data[["disease"]][risk_indices]
risk_Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0) ## Labelling "CD" or "UC" as 1, rest as 0
#### Study : CS-PRISM ####
prism_indices <- which(study_names == "CS-PRISM")
prism_otu <- combined_studies[prism_indices, ]
IBD_resp <- physeq_genera@sam_data[["disease"]][prism_indices]
prism_Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0)
#### Study : HMP2 ######
hmp_indices <- which(study_names == "HMP2")
hmp_otu <- combined_studies[hmp_indices, ]
IBD_resp <- physeq_genera@sam_data[["disease"]][hmp_indices]
hmp_Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0)
##### Study : MucosalIBD #####
mi_indices <- which(study_names == "MucosalIBD")
mi_otu <- combined_studies[mi_indices, ]
IBD_resp <- physeq_genera@sam_data[["disease"]][mi_indices]
mi_Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0)
##### Study : Pouchitis #####
pouchitis_indices <- which(study_names == "Pouchitis")
pouchitis_otu <- combined_studies[pouchitis_indices, ]
IBD_resp <- physeq_genera@sam_data[["disease"]][pouchitis_indices]
pouchitis_Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0)
######### Combining all the 5 studies together #############
X <- rbind(risk_otu,prism_otu,hmp_otu,mi_otu,pouchitis_otu)
Y <- c(risk_Y,prism_Y,hmp_Y,mi_Y,pouchitis_Y)We train the zinck model on \(X\) with the optimal number of clusters (27), and use the posterior estimates of the latent parameters to generate the knockoff matrix.
zinck_code <- "data {
int<lower=1> K; // num topics
int<lower=1> V; // num words
int<lower=0> D; // num docs
int<lower=0> n[D, V]; // word counts for each doc
// hyperparameters
vector<lower=0, upper=1>[V] delta;
}
parameters {
simplex[K] theta[D]; // topic mixtures
vector<lower=0, upper=1>[V] zeta[K]; // zero-inflated betas
vector<lower=0>[V] gamma1[K];
vector<lower=0>[V] gamma2[K];
vector<lower=0>[K] alpha;
}
transformed parameters {
vector[V] beta[K];
// Efficiently compute beta using vectorized operations
for (k in 1:K) {
vector[V] cum_log1m;
cum_log1m[1:(V - 1)] = cumulative_sum(log1m(zeta[k, 1:(V - 1)]));
cum_log1m[V] = 0;
beta[k] = zeta[k] .* exp(cum_log1m);
beta[k] = beta[k] / sum(beta[k]);
}
}
model {
for (k in 1:K) {
alpha[k] ~ gamma(100,100); // Change these hyperparameters as needed
}
for (d in 1:D) {
theta[d] ~ dirichlet(alpha);
}
for (k in 1:K) {
for (m in 1:V) {
gamma1[k,m] ~ gamma(1,1);
gamma2[k,m] ~ gamma(1,1);
}
}
// Zero-inflated beta likelihood and data likelihood
for (k in 1:K) {
for (m in 1:V) {
real lp_non_zero = bernoulli_lpmf(0 | delta[m]) + beta_lpdf(zeta[k, m] | gamma1[k, m], gamma2[k, m]);
real lp_zero = bernoulli_lpmf(1 | delta[m]);
target += log_sum_exp(lp_non_zero, lp_zero);
}
}
// Compute the eta values and data likelihood more efficiently
for (d in 1:D) {
vector[V] eta = theta[d, 1] * beta[1];
for (k in 2:K) {
eta += theta[d, k] * beta[k];
}
eta = eta / sum(eta);
n[d] ~ multinomial(eta);
}
}
"
stan.model = stan_model(model_code = zinck_code)Trying to compile a simple C fileRunning /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
In file included from <built-in>:1:
In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
namespace Eigen {
^
/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
namespace Eigen {
^
;
In file included from <built-in>:1:
In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#include <complex>
^~~~~~~~~
3 errors generated.
make: *** [foo.o] Error 1X <- X[,order(decreasing=T,colSums(X,na.rm=T),apply(X,2L,paste,collapse=''))] ## ordering the columns w/ decreasing abundance
dlt <- rep(0,ncol(X)) ## Initializing the deltas with the sparsity of each column
for(t in (1:ncol(X)))
{
dlt[t] <- 1-mean(X[,t]>0)
if(dlt[t]==0)
{
dlt[t] = dlt[t]+0.01
}
if (dlt[t]==1)
{
dlt[t] = dlt[t]-0.01
}
}
zinLDA_stan_data <- list(
K = 27,
V = ncol(X),
D = nrow(X),
n = X,
delta = dlt
)
## Fitting the zinck model ##
set.seed(1)
fitIBD <- vb(stan.model, data=zinLDA_stan_data, algorithm="meanfield", importance_resampling=TRUE, iter=10000,tol_rel_obj=0.01,elbo_samples=500)Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1: This procedure has not been thoroughly tested and may be unstable
Chain 1: or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1:
Chain 1:
Chain 1:
Chain 1: Gradient evaluation took 0.7248 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7248 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Begin eta adaptation.
Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation)
Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation)
Chain 1: Iteration: 250 / 250 [100%] (Adaptation)
Chain 1: Success! Found best value [eta = 0.1].
Chain 1:
Chain 1: Begin stochastic gradient ascent.
Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes
Chain 1: 100 -126700447.982 1.000 1.000
Chain 1: 200 -77685031.927 0.815 1.000
Chain 1: 300 -59307492.121 0.647 0.631
Chain 1: 400 -50860990.107 0.527 0.631
Chain 1: 500 -45537078.584 0.445 0.310
Chain 1: 600 -41685839.741 0.386 0.310
Chain 1: 700 -38580134.819 0.342 0.166
Chain 1: 800 -36053383.596 0.308 0.166
Chain 1: 900 -33979738.726 0.281 0.117
Chain 1: 1000 -32254605.026 0.258 0.117
Chain 1: 1100 -30792620.765 0.163 0.092
Chain 1: 1200 -29559706.949 0.104 0.081
Chain 1: 1300 -28474935.327 0.077 0.070
Chain 1: 1400 -27530699.160 0.064 0.061
Chain 1: 1500 -26698219.449 0.055 0.053
Chain 1: 1600 -25944401.570 0.049 0.047
Chain 1: 1700 -25287308.192 0.043 0.042
Chain 1: 1800 -24690624.955 0.039 0.038
Chain 1: 1900 -24160947.818 0.035 0.034
Chain 1: 2000 -23683162.157 0.031 0.031
Chain 1: 2100 -23249587.266 0.029 0.029
Chain 1: 2200 -22863841.826 0.026 0.026
Chain 1: 2300 -22511759.353 0.024 0.024
Chain 1: 2400 -22195116.004 0.022 0.022
Chain 1: 2500 -21911818.977 0.020 0.020
Chain 1: 2600 -21658464.199 0.018 0.019
Chain 1: 2700 -21427457.111 0.017 0.017
Chain 1: 2800 -21217341.358 0.015 0.016
Chain 1: 2900 -21028919.131 0.014 0.014
Chain 1: 3000 -20858398.183 0.013 0.013
Chain 1: 3100 -20704895.615 0.012 0.012
Chain 1: 3200 -20558554.504 0.011 0.011
Chain 1: 3300 -20420888.342 0.010 0.010 MEAN ELBO CONVERGED MEDIAN ELBO CONVERGED
Chain 1:
Chain 1: Drawing a sample of size 1000 from the approximate posterior...
Chain 1: COMPLETED.Warning: Pareto k diagnostic value is Inf. Resampling is disabled. Decreasing
tol_rel_obj may help if variational algorithm has terminated prematurely.
Otherwise consider using sampling instead.theta <- fitIBD@sim[["est"]][["theta"]]
beta <- fitIBD@sim[["est"]][["beta"]]
X_tilde <- zinck::generateKnockoff(X,theta,beta,seed=1) ## Generating the kncokoff copyFitting the Random Forest model associating the augmented set of covariates with the outcome of interest, we extract the Feature Importance scores.
set.seed(5)
W <- stat.random_forest(X,X_tilde,Y)
T <- knockoff.threshold(W,fdr=0.1,offset = 0) ## This is the knockoff filter threshold
print(which(W>=T)) [1] 8 16 22 24 28 32 37 61 65 69names <- colnames(X[,which(W>=T)]) ## Extracting the names of the important genera
data.genus <- data.frame(
impscores = sort(W[which(W>=T)], decreasing=FALSE) ,
name = factor(names, levels = names),
y = seq(length(names)) * 0.9
)
plt.genus <- ggplot(data.genus) +
geom_col(aes(impscores, name), fill = "black", width = 0.6)+theme_bw()+ylab("Genera")+xlab("Feature Statistic")
plt.genus
sessionInfo()R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] phyloseq_1.38.0 rstan_2.21.8 StanHeaders_2.21.0-7
[4] ggplot2_3.4.2 knockoff_0.3.6 reshape2_1.4.4
[7] zinck_0.0.0.9000 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] nlme_3.1-162 bitops_1.0-7 matrixStats_0.63.0
[4] fs_1.6.2 httr_1.4.6 rprojroot_2.0.3
[7] GenomeInfoDb_1.30.1 tools_4.1.3 bslib_0.5.0
[10] vegan_2.6-4 utf8_1.2.3 R6_2.5.1
[13] mgcv_1.8-42 DBI_1.1.3 BiocGenerics_0.40.0
[16] colorspace_2.1-0 permute_0.9-7 rhdf5filters_1.6.0
[19] ade4_1.7-22 withr_2.5.0 tidyselect_1.2.0
[22] gridExtra_2.3 prettyunits_1.1.1 processx_3.8.1
[25] compiler_4.1.3 git2r_0.32.0 glmnet_4.1-7
[28] cli_3.6.1 Biobase_2.54.0 labeling_0.4.2
[31] sass_0.4.6 scales_1.2.1 randomForest_4.7-1.1
[34] callr_3.7.3 stringr_1.5.0 digest_0.6.31
[37] rmarkdown_2.22 XVector_0.34.0 pkgconfig_2.0.3
[40] htmltools_0.5.5 highr_0.10 fastmap_1.1.1
[43] rlang_1.1.1 rstudioapi_0.14 farver_2.1.1
[46] shape_1.4.6 jquerylib_0.1.4 generics_0.1.3
[49] jsonlite_1.8.5 dplyr_1.1.2 inline_0.3.19
[52] RCurl_1.98-1.12 magrittr_2.0.3 GenomeInfoDbData_1.2.7
[55] loo_2.6.0 biomformat_1.22.0 Matrix_1.5-1
[58] Rhdf5lib_1.16.0 Rcpp_1.0.10 munsell_0.5.0
[61] S4Vectors_0.32.4 fansi_1.0.4 ape_5.7-1
[64] lifecycle_1.0.3 stringi_1.7.12 whisker_0.4.1
[67] yaml_2.3.7 MASS_7.3-60 zlibbioc_1.40.0
[70] rhdf5_2.38.1 pkgbuild_1.4.2 plyr_1.8.8
[73] grid_4.1.3 parallel_4.1.3 promises_1.2.0.1
[76] crayon_1.5.2 lattice_0.21-8 Biostrings_2.62.0
[79] splines_4.1.3 multtest_2.50.0 knitr_1.43
[82] ps_1.7.5 pillar_1.9.0 ranger_0.15.1
[85] igraph_1.4.2 codetools_0.2-19 stats4_4.1.3
[88] glue_1.6.2 evaluate_0.21 getPass_0.2-2
[91] data.table_1.14.8 RcppParallel_5.1.7 vctrs_0.6.5
[94] httpuv_1.6.11 foreach_1.5.2 gtable_0.3.3
[97] cachem_1.0.8 xfun_0.39 later_1.3.1
[100] survival_3.5-5 tibble_3.2.1 iterators_1.0.14
[103] IRanges_2.28.0 cluster_2.1.4