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html d4297f5 ghoshstats 2024-12-12 Manually deploy website with existing HTML files
html 3a96692 Patron 2024-06-18 Updated CRC Study
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Rmd a6c38f8 Patron 2024-06-18 Add home, experiment, and simulation pages

CRC Data Analysis

We applied zinck to meta-analyze five metagenomic studies of CRC. The five studies correspond to five different countries for the CRC data, which are named “AT” (Australia), “US” (USA), “CN” (China), “DE” (Germany), and “FR” (France). The sample sizes are \(109\), \(127\), \(120\), \(114\), and \(104\), respectively, and the number of cases and controls is roughly balanced in each study. We focus on all the species whose relative abundances are more than \(0.2\).

##################### CRC data species level ##########################
#######################################################################
norm_count <- count/rowSums(count)
col_means <- colMeans(norm_count>0)
indices <- which(col_means > 0.2)
sorted_indices <- indices[order(col_means[indices],decreasing = TRUE)]
dcount <- count[,sorted_indices]

X <- dcount
Y <- ifelse(meta$Group=="CRC",1,0)

We train the zinck model on \(X\) using ADVI with an optimal number of clusters (20). We generate the knockoff matrix \(\tilde{X}\) by plugging in the learnt parameters \(\boldsymbol{\beta}\) and \(\boldsymbol{\theta}\) into the generative model.

fitCRC <- fit.zinck(X,num_clusters=20,method="ADVI",seed=1,boundary_correction = TRUE,
                    elbo_samples = 100,importance_resampling = FALSE,
                    prior_ZIGD = TRUE)
theta <- fitCRC$theta
beta <- fitCRC$beta
X_tilde <- zinck::generateKnockoff(X,theta,beta,seed=1) ## Generating the knockoff copy

We then move on to fit a tuned Random Forest model relating the augmented set of covariates with the outcome of interest \(Y\). The feature importance score used to construct the knockoff feature statistics \(W\) is the Mean Decrease in Gini Impurity.

filter_zinck <- zinck.filter(as.matrix(X),as.matrix(X_tilde),as.factor(Y),
                model="Random Forest",offset = 1,seed=1,mtry=28,
                rftuning=TRUE,metric = "Gini")

selected_species <- filter_zinck$selected


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

## Threshold ##
T <- filter_zinck$T

Finally, we can visualize the importance of these selected species using the Feature Statistics obtained by contrasting the Random Forest importance scores of the original and the knockoff features.

### Creating the data frame with Feature Importance Statistics
data.species <- data.frame(             
  impscores = sort(W[which(W>=T)], decreasing=FALSE) , 
  name = factor(names_zinck, levels = names_zinck),
  y = seq(length(names_zinck)) * 0.9
)

norm_count <- count/rowSums(count)
col_means <- colMeans(norm_count>0)
indices <- which(col_means > 0)
sorted_indices <- indices[order(col_means[indices],decreasing = TRUE)]
Xnorm <-norm_count[,sorted_indices]

# 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)])

# Determine colors based on the sum comparison
colors <- ifelse(case_sums > control_sums, "red", "blue")

The plot showing the identified species in order of decreasing importance is attached below.

# Create a data frame for plotting
data.species <- 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
)

plt.species <- ggplot(data.species) +
  geom_col(aes(impscores, name, fill = color), width = 0.6) +
  scale_fill_identity() +
  theme_bw() +
  ylab("Species") +
  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, face = data.species$fontface)
  )
print(plt.species)

Version Author Date
d4297f5 ghoshstats 2024-12-12

Note that the red colored bars indicate positive marginal association between microbial relative abundance and the odds of CRC, 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] rstan_2.21.8         StanHeaders_2.21.0-7 ggplot2_3.4.2       
[4] knockoff_0.3.6       reshape2_1.4.4       zinck_0.0.0.9000    
[7] randomForest_4.7-1.1

loaded via a namespace (and not attached):
 [1] sass_0.4.6         jsonlite_1.8.5     splines_4.1.3      foreach_1.5.2     
 [5] bslib_0.5.0        RcppParallel_5.1.7 highr_0.10         stats4_4.1.3      
 [9] yaml_2.3.7         pillar_1.9.0       lattice_0.21-8     glue_1.6.2        
[13] digest_0.6.31      promises_1.2.0.1   colorspace_2.1-0   htmltools_0.5.5   
[17] httpuv_1.6.11      Matrix_1.5-1       plyr_1.8.8         pkgconfig_2.0.3   
[21] scales_1.2.1       processx_3.8.1     whisker_0.4.1      later_1.3.1       
[25] git2r_0.32.0       tibble_3.2.1       generics_0.1.3     farver_2.1.1      
[29] cachem_1.0.8       withr_2.5.0        cli_3.6.1          survival_3.5-5    
[33] magrittr_2.0.3     crayon_1.5.2       evaluate_0.21      ps_1.7.5          
[37] fs_1.6.2           fansi_1.0.4        pkgbuild_1.4.2     tools_4.1.3       
[41] loo_2.6.0          prettyunits_1.1.1  lifecycle_1.0.3    matrixStats_0.63.0
[45] stringr_1.5.0      munsell_0.5.0      glmnet_4.1-7       callr_3.7.3       
[49] compiler_4.1.3     jquerylib_0.1.4    rlang_1.1.1        grid_4.1.3        
[53] iterators_1.0.14   rstudioapi_0.14    labeling_0.4.2     rmarkdown_2.22    
[57] gtable_0.3.3       codetools_0.2-19   inline_0.3.19      DBI_1.1.3         
[61] R6_2.5.1           gridExtra_2.3      knitr_1.43         dplyr_1.1.2       
[65] fastmap_1.1.1      utf8_1.2.3         workflowr_1.7.1    rprojroot_2.0.3   
[69] shape_1.4.6        stringi_1.7.12     parallel_4.1.3     Rcpp_1.0.10       
[73] vctrs_0.6.5        tidyselect_1.2.0   xfun_0.39