Heatmaps

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Last updated: 2024-06-18

Checks: 6 1

Knit directory: zinck-website/

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Environment: empty

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Seed: set.seed(20240617)

The command set.seed(20240617) 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.

Session information: recorded

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File paths: relative

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Repository version: 6e66a4e

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The results in this page were generated with repository version 6e66a4e. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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Untracked files:
    Untracked:  analysis/CRC.Rmd
    Untracked:  analysis/Heatmaps.Rmd
    Untracked:  analysis/IBD.Rmd
    Untracked:  analysis/simulation.Rmd

Unstaged changes:
    Modified:   analysis/index.Rmd

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Comparing the Heatmaps for Original and Knockoff Sample Taxa Matrices

We demonstrate the ability of zinck to capture the compositional and highly sparse nature of microbiome count data by comparing the heatmaps of the original sample taxa matrix \(\mathbf{X}\) with its high quality knockoff copy, \(\tilde{\mathbf{X}}\).

We consider a toy setting with \(20\) samples and \(30\) taxa with the library size between \(400\) and \(500\) for each sample, for a high zero-inflation level of \(0.8\). We use the simulateZINLDA() function from the zinLDA package to generate the original sample taxa matrix.

library(zinck)
library(zinLDA)
library(ggplot2)
library(reshape2)
library(gridExtra)
library(cowplot)

set.seed(1) 
N.d=zinLDA::rdu(n=20,min=400,max=500) # Drawing random library sizes between 400, 500
sim_data = zinLDA::simulateZINLDA(D=20,V=30,N=N.d,K=5,Alpha=0.1,Pi=0.8,a=0.5,b=10)
X_original <- sim_data$sampleTaxaMatrix ## The original sample taxa count matrix


model_zinck <- fit.zinck(X_original, num_clusters=5, method="Gibbs", seed=1)

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Theta <- model_zinck$theta
Beta <- model_zinck$beta

X_zinck <- generateKnockoff(X_original,Theta,Beta,seed=1)
rownames(X_zinck) <- rownames(X_original)

heat1 <- draw_heatmap(X_original, "Original")
heat2 <- draw_heatmap(X_zinck, "Knockoff")
plot_grid(heat1, heat2, ncol = 2, align="v")

It is evident from the above heatmaps that the knockoff copy is almost indistinguishable from the original matrix! This underscores the fact that the knockoff copy preserves the underlying structure of the observed sample taxa count matrix.

Session information

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] cowplot_1.1.1     gridExtra_2.3     reshape2_1.4.4    ggplot2_3.4.2    
[5] zinLDA_0.0.0.9000 zinck_0.0.0.9000  workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] mcmc_0.9-7           matrixStats_0.63.0   fs_1.6.2            
 [4] httr_1.4.6           rprojroot_2.0.3      rstan_2.21.8        
 [7] tools_4.1.3          bslib_0.5.0          utf8_1.2.3          
[10] R6_2.5.1             DBI_1.1.3            colorspace_2.1-0    
[13] withr_2.5.0          tidyselect_1.2.0     prettyunits_1.1.1   
[16] processx_3.8.1       compiler_4.1.3       git2r_0.32.0        
[19] glmnet_4.1-7         cli_3.6.1            quantreg_5.95       
[22] SparseM_1.81         xml2_1.3.4           NLP_0.2-1           
[25] slam_0.1-50          sass_0.4.6           scales_1.2.1        
[28] tm_0.7-8             randomForest_4.7-1.1 callr_3.7.3         
[31] pbapply_1.7-2        stringr_1.5.0        digest_0.6.31       
[34] StanHeaders_2.21.0-7 rmarkdown_2.22       MCMCpack_1.6-3      
[37] pkgconfig_2.0.3      htmltools_0.5.5      highr_0.10          
[40] fastmap_1.1.1        rlang_1.1.1          rstudioapi_0.14     
[43] shape_1.4.6          jquerylib_0.1.4      generics_0.1.3      
[46] farver_2.1.1         jsonlite_1.8.5       dplyr_1.1.2         
[49] inline_0.3.19        magrittr_2.0.3       modeltools_0.2-23   
[52] loo_2.6.0            Matrix_1.5-1         Rcpp_1.0.10         
[55] munsell_0.5.0        fansi_1.0.4          lifecycle_1.0.3     
[58] stringi_1.7.12       whisker_0.4.1        yaml_2.3.7          
[61] MASS_7.3-60          pkgbuild_1.4.2       plyr_1.8.8          
[64] grid_4.1.3           parallel_4.1.3       promises_1.2.0.1    
[67] crayon_1.5.2         lattice_0.21-8       splines_4.1.3       
[70] knockoff_0.3.6       knitr_1.43           ps_1.7.5            
[73] pillar_1.9.0         topicmodels_0.2-14   codetools_0.2-19    
[76] stats4_4.1.3         glue_1.6.2           evaluate_0.21       
[79] getPass_0.2-2        RcppParallel_5.1.7   vctrs_0.6.5         
[82] httpuv_1.6.11        foreach_1.5.2        MatrixModels_0.5-1  
[85] gtable_0.3.3         cachem_1.0.8         xfun_0.39           
[88] coda_0.19-4          later_1.3.1          survival_3.5-5      
[91] tibble_3.2.1         iterators_1.0.14