Heatmaps
Soham Ghosh
2024-06-17
Last updated: 2024-06-19
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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$sampleTaxaMatrix ## The original sample taxa count matrix
model_zinck <- fit.zinck(X_original, num_clusters=5, method="Gibbs", seed=1)
Theta <- model_zinck$theta
Beta <- model_zinck$beta
X_zinck <- generateKnockoff(X_original,Theta,Beta,seed=1)
rownames(X_zinck) <- rownames(X_original)We will now visualize the heatmaps of the original matrix and its corresponding knockoff copy. The function applies an arcsinh transformation to the data for normalization and better visualization of abundance patterns and zero inflation within the sample taxa matrix.
draw.heatmap <- function(X, title="") {
reshape2::melt(asinh(X)) %>%
dplyr::rename(sample = Var1, taxa = Var2, asinh.abun = value) %>%
ggplot2::ggplot(., aes (x = taxa, y = sample, fill = asinh.abun)) +
ggplot2::geom_tile() + ggplot2::theme_bw() + ggplot2::ggtitle(title) +
ggplot2::labs(fill = "arcsinh\nabundance") +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
axis.title.x=element_blank(), axis.title.y=element_blank(),
axis.text.x = element_text(size=3, angle=90), axis.text.y = element_text(size=4)) +
viridis::scale_fill_viridis(discrete = FALSE, direction = -1, na.value = "grey") +
theme(axis.title.x = element_blank(),axis.text.x = element_blank(),axis.ticks.x=element_blank(),
axis.title.y = element_blank(),axis.text.y=element_blank(),axis.ticks.y=element_blank())+
theme(panel.grid.major=element_blank(),panel.grid.minor = element_blank(),
panel.background = element_blank(),panel.border = element_blank())+
theme(legend.position="none") +
ggplot2::coord_fixed(ratio = 1) # Fixing the aspect ratio
}
heat1 <- draw.heatmap(X_original, "Original")
heat2 <- draw.heatmap(X_zinck, "Knockoff")
plot_grid(heat1, heat2, ncol = 2, align="v")
| Version | Author | Date |
|---|---|---|
| 51a584c | Patron | 2024-06-18 |
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.
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] farver_2.1.1 shape_1.4.6 jquerylib_0.1.4
[46] generics_0.1.3 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 viridis_0.6.5
[58] lifecycle_1.0.3 stringi_1.7.12 whisker_0.4.1
[61] yaml_2.3.7 MASS_7.3-60 pkgbuild_1.4.2
[64] plyr_1.8.8 grid_4.1.3 parallel_4.1.3
[67] promises_1.2.0.1 crayon_1.5.2 lattice_0.21-8
[70] splines_4.1.3 knockoff_0.3.6 knitr_1.43
[73] ps_1.7.5 pillar_1.9.0 topicmodels_0.2-14
[76] codetools_0.2-19 stats4_4.1.3 glue_1.6.2
[79] evaluate_0.21 getPass_0.2-2 RcppParallel_5.1.7
[82] vctrs_0.6.5 httpuv_1.6.11 foreach_1.5.2
[85] MatrixModels_0.5-1 gtable_0.3.3 cachem_1.0.8
[88] xfun_0.39 coda_0.19-4 later_1.3.1
[91] viridisLite_0.4.2 survival_3.5-5 tibble_3.2.1
[94] iterators_1.0.14