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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.

############################################################################################
################################## IBD data genus level ####################################

load("/Users/Patron/Documents/zinck 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"]]

## RISK ##
risk_indices <- which(study_names == "RISK")
risk_otu <- combined_studies[risk_indices, ]

## CS-PRISM ##
prism_indices <- which(study_names == "CS-PRISM")
prism_otu <- combined_studies[prism_indices, ]

## HMP2 ##
HMP_indices <- which(study_names == "HMP2")
hmp_otu <- combined_studies[HMP_indices, ]

## Pouchitis ##
pouchitis_indices <- which(study_names == "Pouchitis")
pouchitis_otu <- combined_studies[pouchitis_indices, ]

## MucosalIBD ##
mi_indices <- which(study_names == "MucosalIBD")
mi_otu <- combined_studies[mi_indices, ]

metadata <- physeq_genera@sam_data

# Get the indices of the last occurrence of each unique subject_accession
latest_indices <- sapply(unique(metadata$subject_accession), function(x) {
  max(which(metadata$subject_accession == x))
})

# Subset the metadata to keep only the latest samples
latest_metadata <- metadata[latest_indices, ]

study_names <- c("CS-PRISM","HMP2","MucosalIBD","Pouchitis","RISK")
meta_IBD <- latest_metadata[latest_metadata$dataset_name %in% study_names,]

# Extract row names for each study
risk_row_names <- row.names(latest_metadata[latest_metadata$dataset_name == "RISK", ])
prism_row_names <- row.names(latest_metadata[latest_metadata$dataset_name == "CS-PRISM", ])
hmp_row_names <- row.names(latest_metadata[latest_metadata$dataset_name == "HMP2", ])
mi_row_names <- row.names(latest_metadata[latest_metadata$dataset_name == "MucosalIBD", ])
pouchitis_row_names <- row.names(latest_metadata[latest_metadata$dataset_name == "Pouchitis", ])

# Subset the OTU matrices and Y vectors based on these row names
risk_otu_latest <- risk_otu[row.names(risk_otu) %in% risk_row_names, ]
prism_otu_latest <- prism_otu[row.names(prism_otu) %in% prism_row_names, ]
hmp_otu_latest <- hmp_otu[row.names(hmp_otu) %in% hmp_row_names, ]
mi_otu_latest <- mi_otu[row.names(mi_otu) %in% mi_row_names, ]
pouchitis_otu_latest <- pouchitis_otu[row.names(pouchitis_otu) %in% pouchitis_row_names, ]

combined_otu <- rbind(prism_otu_latest,hmp_otu_latest,mi_otu_latest,
                      pouchitis_otu_latest,risk_otu_latest)
combined_otu <- combined_otu[ match(rownames(meta_IBD), rownames(combined_otu)), ] 
## To make sure samples in OTU correspond to the samples in meta

IBD_resp <- meta_IBD$disease
Y <- ifelse(IBD_resp %in% c("CD", "UC"), 1, 0)

X <- combined_otu

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.

fitIBD <- fit.zinck(as.matrix(X),num_clusters=27,method="ADVI",seed=1,
                    importance_resampling = TRUE,elbo_samples = 500,
                    boundary_correction = TRUE,prior_ZIGD = TRUE)
theta <- fitIBD$theta
beta <- fitIBD$beta
X_tilde <- zinck::generateKnockoff(X,theta,beta,seed=1) ## Generating the knockoff copy

Fitting the Random Forest model associating the augmented set of covariates with the outcome of interest, we extract the Feature Importance scores.

filter_zinck <- zinck.filter(as.matrix(X),as.matrix(X_tilde),as.factor(Y),
                             model="Random Forest",offset=0,seed=312)

selected_genera <- filter_zinck$selected


## Importance scores ##
W <- filter_zinck$W

## Threshold ##
T <- filter_zinck$T

Xnorm <- X / rowSums(X)

# Calculate the column sums for cases and controls
case_sums <- colMeans(Xnorm[Y == 1, which(W>=T)])
control_sums <- colMeans(Xnorm[Y == 0, which(W>=T)])

colors <- ifelse(case_sums > control_sums, "red", "blue")


# Create a data frame for plotting
data.genus <- data.frame(
  impscores = sort(W[which(W >= T)], decreasing = FALSE),
  name = factor(names_zinck, levels = names_zinck),
  y = seq(length(names_zinck)) * 0.9,
  color = colors
)


# Create the plot
plt.genus <- ggplot(data.genus) +
  geom_col(aes(impscores, name, fill = color), width = 0.6) +
  scale_fill_identity() +
  theme_bw() +
  ylab("Genera") +
  xlab("Feature Statistic") +
  theme(
    axis.title.x = element_text(size = 22),
    axis.title.y = element_text(size = 22),
    axis.text.x = element_text(size = 18),
    axis.text.y = element_text(size = 18) 
  )
print(plt.genus)

Version Author Date
d404602 Patron 2024-06-18
ab6400d Patron 2024-06-18

Note that the red coloured bars indicate positive marginal association between microbial relative abundance and the odds of IBD, while blue indicate negative marginal association.


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] kosel_0.0.1          phyloseq_1.38.0      rstan_2.21.8        
[4] StanHeaders_2.21.0-7 ggplot2_3.4.2        knockoff_0.3.6      
[7] reshape2_1.4.4       zinck_0.0.0.9000    

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               rprojroot_2.0.3        GenomeInfoDb_1.30.1   
  [7] tools_4.1.3            bslib_0.5.0            vegan_2.6-4           
 [10] utf8_1.2.3             R6_2.5.1               mgcv_1.8-42           
 [13] DBI_1.1.3              BiocGenerics_0.40.0    colorspace_2.1-0      
 [16] permute_0.9-7          rhdf5filters_1.6.0     ade4_1.7-22           
 [19] withr_2.5.0            tidyselect_1.2.0       gridExtra_2.3         
 [22] prettyunits_1.1.1      processx_3.8.1         compiler_4.1.3        
 [25] git2r_0.32.0           glmnet_4.1-7           cli_3.6.1             
 [28] Biobase_2.54.0         labeling_0.4.2         sass_0.4.6            
 [31] scales_1.2.1           randomForest_4.7-1.1   callr_3.7.3           
 [34] stringr_1.5.0          digest_0.6.31          rmarkdown_2.22        
 [37] ordinalNet_2.12        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] Rcpp_1.0.10            munsell_0.5.0          S4Vectors_0.32.4      
 [61] Rhdf5lib_1.16.0        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          data.table_1.14.8     
 [91] RcppParallel_5.1.7     vctrs_0.6.5            httpuv_1.6.11         
 [94] foreach_1.5.2          gtable_0.3.3           cachem_1.0.8          
 [97] xfun_0.39              later_1.3.1            survival_3.5-5        
[100] tibble_3.2.1           iterators_1.0.14       IRanges_2.28.0        
[103] cluster_2.1.4          workflowr_1.7.1