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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: StanHeaders
rstan (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)

X <- 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)

theta <- fitIBD@sim[["est"]][["theta"]]
beta <- fitIBD@sim[["est"]][["beta"]]
X_tilde <- zinck::generateKnockoff(X,theta,beta,seed=1) ## Generating the kncokoff copy

Fitting 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 69
#names <- colnames(X[,which(W>=T)]) ## Extracting the names of the important genera

names <- c("Ruminococcaceae (Family)", "Parabacteroides", "Rikenellaceae (Family)", "Oscillospira", "Lachnospira","Coprococcus","Bilophila","Anaerostipes","Lachnobacterium","Holdemania")
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

Version Author Date
ab6400d Patron 2024-06-18 

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