Example data for multiWGCNA is stored in ExperimentHub. Access it like this:

# Load expression matrix and metadata
library(ExperimentHub)
## Loading required package: BiocGenerics
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
##     union, unique, unsplit, which.max, which.min
## Loading required package: AnnotationHub
## Loading required package: BiocFileCache
## Loading required package: dbplyr
eh = ExperimentHub()
eh_query = query(eh, c("multiWGCNAdata"))

## download the autism data and metadata
autism_se = eh_query[["EH8219"]]
## see ?multiWGCNAdata and browseVignettes('multiWGCNAdata') for documentation
## loading from cache
## require("SummarizedExperiment")

Now, proceed with the multiWGCNA analysis:

# Load multiWGCNA R package
library(multiWGCNA)
## Loading required package: ggalluvial
## Loading required package: ggplot2
## 
# Obtain metadata
sampleTable = colData(autism_se)

# Randomly sample 2000 genes from the expression matrix
set.seed(1)
autism_se = autism_se[sample(rownames(autism_se), 2000),]

# Check the data
assays(autism_se)[[1]][1:5, 1:5]
##              GSM706412 GSM706413 GSM706414 GSM706415 GSM706416
## ILMN_1672121 11.034264 10.446682 11.473705 11.732849  11.43105
## ILMN_2151368 10.379812  9.969130  9.990030  9.542288  10.26247
## ILMN_1757569  9.426955  9.050024  9.347505  9.235251   9.38837
## ILMN_2400219 12.604047 12.886037 12.890658 12.446960  12.98925
## ILMN_2222101 12.385019 12.748229 12.418027 11.690253  13.10915
sampleTable
## DataFrame with 58 rows and 3 columns
##                Sample      Status      Tissue
##           <character> <character> <character>
## GSM706412   GSM706412      autism          FC
## GSM706413   GSM706413      autism          FC
## GSM706414   GSM706414      autism          FC
## GSM706415   GSM706415      autism          FC
## GSM706416   GSM706416      autism          FC
## ...               ...         ...         ...
## GSM706465   GSM706465    controls          TC
## GSM706466   GSM706466    controls          TC
## GSM706467   GSM706467    controls          TC
## GSM706468   GSM706468    controls          TC
## GSM706469   GSM706469    controls          TC
# Set the alpha level for statistical analyses and the soft power for network construction
alphaLevel = 0.05
softPower = 10

# If your sample traits include numbers that you'd like to be considered numerical 
# variables rather than categorical variables, set detectNumbers = TRUE
detectNumbers = FALSE

We now perform network construction, module eigengene calculation, module-trait correlation.

# Define our conditions for trait 1 (disease) and 2 (brain region)
conditions1 = unique(sampleTable[,2])
conditions2 = unique(sampleTable[,3])
# Construct the combined networks and all the sub-networks (autism only, controls only, FC only, and TC only)
# Same parameters as Tommasini and Fogel. BMC Bioinformatics
myNetworks = constructNetworks(autism_se, sampleTable, conditions1, conditions2, 
                                  networkType = "signed", TOMType = "unsigned", 
                                  power = softPower, minModuleSize = 100, maxBlockSize = 25000,
                                  reassignThreshold = 0, minKMEtoStay = 0, mergeCutHeight = 0,
                                  numericLabels = TRUE, pamRespectsDendro = FALSE, 
                                  deepSplit = 4, verbose = 3)

Carry on with the multiWGCNA analysis according to the generalWorkflow.Rmd vignette!

sessionInfo()
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] multiWGCNA_1.3.0            ggalluvial_0.12.5          
##  [3] ggplot2_3.5.1               SummarizedExperiment_1.35.0
##  [5] Biobase_2.65.0              GenomicRanges_1.57.0       
##  [7] GenomeInfoDb_1.41.0         IRanges_2.39.0             
##  [9] S4Vectors_0.43.0            MatrixGenerics_1.17.0      
## [11] matrixStats_1.3.0           multiWGCNAdata_1.3.0       
## [13] ExperimentHub_2.13.0        AnnotationHub_3.13.0       
## [15] BiocFileCache_2.13.0        dbplyr_2.5.0               
## [17] BiocGenerics_0.51.0         BiocStyle_2.33.0           
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.2.2               gridExtra_2.3           rlang_1.1.3            
##   [4] magrittr_2.0.3          compiler_4.4.0          RSQLite_2.3.6          
##   [7] png_0.1-8               vctrs_0.6.5             stringr_1.5.1          
##  [10] pkgconfig_2.0.3         crayon_1.5.2            fastmap_1.1.1          
##  [13] backports_1.4.1         XVector_0.45.0          utf8_1.2.4             
##  [16] rmarkdown_2.26          tzdb_0.4.0              preprocessCore_1.67.0  
##  [19] UCSC.utils_1.1.0        purrr_1.0.2             bit_4.0.5              
##  [22] xfun_0.43               zlibbioc_1.51.0         cachem_1.0.8           
##  [25] dcanr_1.21.0            flashClust_1.01-2       jsonlite_1.8.8         
##  [28] blob_1.2.4              DelayedArray_0.31.0     parallel_4.4.0         
##  [31] cluster_2.1.6           R6_2.5.1                stringi_1.8.3          
##  [34] bslib_0.7.0             rpart_4.1.23            jquerylib_0.1.4        
##  [37] Rcpp_1.0.12             bookdown_0.39           iterators_1.0.14       
##  [40] knitr_1.46              WGCNA_1.72-5            base64enc_0.1-3        
##  [43] readr_2.1.5             igraph_2.0.3            nnet_7.3-19            
##  [46] Matrix_1.7-0            splines_4.4.0           tidyselect_1.2.1       
##  [49] rstudioapi_0.16.0       abind_1.4-5             yaml_2.3.8             
##  [52] doParallel_1.0.17       codetools_0.2-20        curl_5.2.1             
##  [55] doRNG_1.8.6             lattice_0.22-6          tibble_3.2.1           
##  [58] withr_3.0.0             KEGGREST_1.45.0         evaluate_0.23          
##  [61] foreign_0.8-86          survival_3.6-4          Biostrings_2.73.0      
##  [64] pillar_1.9.0            BiocManager_1.30.22     filelock_1.0.3         
##  [67] rngtools_1.5.2          checkmate_2.3.1         foreach_1.5.2          
##  [70] generics_0.1.3          hms_1.1.3               BiocVersion_3.20.0     
##  [73] munsell_0.5.1           scales_1.3.0            glue_1.7.0             
##  [76] Hmisc_5.1-2             tools_4.4.0             data.table_1.15.4      
##  [79] fastcluster_1.2.6       cowplot_1.1.3           grid_4.4.0             
##  [82] impute_1.79.0           AnnotationDbi_1.67.0    colorspace_2.1-0       
##  [85] patchwork_1.2.0         GenomeInfoDbData_1.2.12 htmlTable_2.4.2        
##  [88] Formula_1.2-5           cli_3.6.2               rappdirs_0.3.3         
##  [91] fansi_1.0.6             S4Arrays_1.5.0          dplyr_1.1.4            
##  [94] gtable_0.3.5            dynamicTreeCut_1.63-1   sass_0.4.9             
##  [97] digest_0.6.35           ggrepel_0.9.5           SparseArray_1.5.0      
## [100] htmlwidgets_1.6.4       memoise_2.0.1           htmltools_0.5.8.1      
## [103] lifecycle_1.0.4         httr_1.4.7              GO.db_3.19.1           
## [106] mime_0.12               bit64_4.0.5