Hello,

I'm having a trouble clustering TCGA GBM RNA data

One cluster is having massive genes and others have smaller.

I tried limma::removebatcheffect() but I think it's not working or I didn't do it correctly.

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Asia/Seoul
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gridExtra_2.3               DESeq2_1.40.1               lubridate_1.9.2             forcats_1.0.0               stringr_1.5.0              
 [6] dplyr_1.1.2                 purrr_1.0.1                 readr_2.1.4                 tidyr_1.3.0                 tibble_3.2.1               
[11] ggplot2_3.4.2               tidyverse_2.0.0             WGCNA_1.72-1                fastcluster_1.2.3           dynamicTreeCut_1.63-1      
[16] SummarizedExperiment_1.30.2 Biobase_2.60.0              GenomicRanges_1.52.0        GenomeInfoDb_1.36.1         IRanges_2.34.1             
[21] S4Vectors_0.38.1            BiocGenerics_0.46.0         MatrixGenerics_1.12.2       matrixStats_1.0.0           TCGAbiolinks_2.28.3        

loaded via a namespace (and not attached):
  [1] DBI_1.1.3                   bitops_1.0-7                biomaRt_2.56.1              rlang_1.1.1                 magrittr_2.0.3             
  [6] compiler_4.3.0              RSQLite_2.3.1               png_0.1-8                   vctrs_0.6.3                 rvest_1.0.3                
 [11] pkgconfig_2.0.3             crayon_1.5.2                fastmap_1.1.1               backports_1.4.1             dbplyr_2.3.2               
 [16] XVector_0.40.0              labeling_0.4.2              utf8_1.2.3                  rmarkdown_2.22              tzdb_0.4.0                 
 [21] preprocessCore_1.62.1       bit_4.0.5                   xfun_0.39                   zlibbioc_1.46.0             cachem_1.0.8               
 [26] jsonlite_1.8.5              progress_1.2.2              blob_1.2.4                  DelayedArray_0.26.3         BiocParallel_1.34.2        
 [31] cluster_2.1.4               parallel_4.3.0              prettyunits_1.1.1           R6_2.5.1                    stringi_1.7.12             
 [36] limma_3.56.2                rpart_4.1.19                Rcpp_1.0.10                 iterators_1.0.14            knitr_1.43                 
 [41] base64enc_0.1-3             downloader_0.4              R.utils_2.12.2              timechange_0.2.0            nnet_7.3-19                
 [46] Matrix_1.5-4.1              splines_4.3.0               tidyselect_1.2.0            rstudioapi_0.14             yaml_2.3.7                 
 [51] doParallel_1.0.17           codetools_0.2-19            curl_5.0.1                  lattice_0.21-8              plyr_1.8.8                 
 [56] withr_2.5.0                 KEGGREST_1.40.0             evaluate_0.21               foreign_0.8-84              survival_3.5-5             
 [61] BiocFileCache_2.8.0         xml2_1.3.4                  Biostrings_2.68.1           pillar_1.9.0                BiocManager_1.30.21        
 [66] filelock_1.0.2              checkmate_2.2.0             foreach_1.5.2               generics_0.1.3              RCurl_1.98-1.12            
 [71] hms_1.1.3                   munsell_0.5.0               scales_1.2.1                glue_1.6.2                  Hmisc_5.1-0                
 [76] tools_4.3.0                 data.table_1.14.8           locfit_1.5-9.8              XML_3.99-0.14               grid_4.3.0                 
 [81] impute_1.74.1               AnnotationDbi_1.62.1        colorspace_2.1-0            GenomeInfoDbData_1.2.10     htmlTable_2.4.1            
 [86] Formula_1.2-5               cli_3.6.1                   rappdirs_0.3.3              fansi_1.0.4                 S4Arrays_1.0.4             
 [91] gtable_0.3.3                R.methodsS3_1.8.2           TCGAbiolinksGUI.data_1.20.0 digest_0.6.31               farver_2.1.1               
 [96] htmlwidgets_1.6.2           memoise_2.0.1               htmltools_0.5.5             R.oo_1.25.0                 lifecycle_1.0.3            
[101] httr_1.4.6                  GO.db_3.17.0                bit64_4.0.5

WGCNA is not a Bioconductor package, you won't find answers here. Consider using biostars.org, but if you do add details and not just "not working". Try to ask a precise question.