The gene clusters from the WGCNA dendrogram output are not in order
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Last seen 8 weeks ago
United States

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.


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)) %>% 

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/
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=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
Clustering WGCNA • 79 views

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