I am running WGCNA and trying to visualize the gene network as TOM plot. Rather than using the native function in the package, I am looking for plotting it out by ggplot or other packages. The main reason is I would like to also annotate the TOM plot by placing rectangles on each module found by the WGCNA.
However, I found a very hard time reproducing the plot of the native function, one of the main issues is when I trying to look for the order of genes by using the net$dendrograms[[1]]$order and also the module labels by net$color, I find that the color (or module) are not clustered together. Rather they are very scattered. Therefore, I am not sure if I can place a rectangle to bracket the modules. However, when I look at the dendrograms with module labels plotted by the native WGCNA function, the module (at least at the color level) seems clustered together. I did not use the pam options and the tree deep is set to 2, which I believe is quite conservative in defining modules.
I am confused and not sure whether I have missed something. I have also tried to reach out to the Biostars community before but the problem is not fully solved. I am here to reproduce the problem using the tutorial dataset.
library(WGCNA)
library(tidyverse)
lnames = load(file = "FemaleLiver-01-dataInput.RData"); # followed everything mentioned in the first tutorial
net = blockwiseModules(datExpr,
power = 6,
TOMType = "signed",
minModuleSize = 30,
reassignThreshold = 0,
mergeCutHeight = 0.25,
deepSplit = 2,
pamStage = FALSE,
numericLabels = TRUE,
pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = "femaleMouseTOM",
verbose = 3)
moduleLabels = net$colors # this line is not necessary for reproducing the error, but I ran it in my Rstduio
moduleColors = labels2colors(net$colors) # this line is not necessary for reproducing the error, but I ran it in my Rstduio
MEs = net$MEs; # this line is not necessary for reproducing the error, but I ran it in my Rstduio
geneTree = net$dendrograms[[1]]; # this line is not necessary for reproducing the error, but I ran it in my Rstduio
### look at the clustering of each gene/ probe
annotation_dataset <- as_tibble(net$color, rownames = "generow") %>%
dplyr::rename(cluster = value) %>%
mutate(geneorder = !!(net$dendrograms[[1]]$order)) %>%
arrange(geneorder)
sessionInfo( )
The output is like this
generow cluster geneorder
MMT00005210 14 1
MMT00051382 1 2
MMT00049262 1 3
MMT00004428 0 4
MMT00011541 1 5
MMT00055921 9 6
MMT00029144 10 7
MMT00069114 14 8
MMT00021297 5 9
MMT00039882 0 10
Here is the session information:
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[4] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[7] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.0
[10] WGCNA_1.70-3 fastcluster_1.2.3 dynamicTreeCut_1.63-1
[13] xml2_1.3.3
loaded via a namespace (and not attached):
[1] fs_1.5.2 matrixStats_0.61.0 lubridate_1.7.10
[4] bit64_4.0.5 doParallel_1.0.16 RColorBrewer_1.1-2
[7] httr_1.4.2 tools_4.1.2 backports_1.4.1
[10] utf8_1.2.2 R6_2.5.1 rpart_4.1-15
[13] Hmisc_4.6-0 DBI_1.1.2 BiocGenerics_0.34.0
[16] colorspace_2.0-2 nnet_7.3-16 withr_2.4.3
[19] tidyselect_1.1.1 gridExtra_2.3 bit_4.0.4
[22] compiler_4.1.2 preprocessCore_1.50.0 cli_3.0.1
[25] rvest_1.0.2 Biobase_2.48.0 htmlTable_2.3.0
[28] scales_1.1.1 checkmate_2.0.0 digest_0.6.29
[31] foreign_0.8-81 base64enc_0.1-3 jpeg_0.1-9
[34] pkgconfig_2.0.3 htmltools_0.5.2 dbplyr_2.1.1
[37] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_0.4.12
[40] readxl_1.3.1 rstudioapi_0.13 RSQLite_2.2.7
[43] impute_1.62.0 generics_0.1.1 jsonlite_1.7.2
[46] magrittr_2.0.1 GO.db_3.11.4 Formula_1.2-4
[49] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
[52] S4Vectors_0.26.1 fansi_0.5.0 lifecycle_1.0.1
[55] stringi_1.7.6 grid_4.1.2 blob_1.2.2
[58] parallel_4.1.2 crayon_1.4.1 lattice_0.20-45
[61] haven_2.4.3 splines_4.1.2 hms_1.1.1
[64] knitr_1.37 pillar_1.6.4 codetools_0.2-18
[67] stats4_4.1.2 reprex_2.0.1 glue_1.6.0
[70] latticeExtra_0.6-29 data.table_1.14.2 modelr_0.1.8
[73] png_0.1-7 vctrs_0.3.8 tzdb_0.2.0
[76] foreach_1.5.1 cellranger_1.1.0 gtable_0.3.0
[79] assertthat_0.2.1 cachem_1.0.6 xfun_0.29
[82] broom_0.7.10 survival_3.2-13 iterators_1.0.13
[85] AnnotationDbi_1.50.3 memoise_2.0.1 IRanges_2.22.2
[88] cluster_2.1.2 ellipsis_0.3.2
