Example data for multiWGCNA is stored in ExperimentHub. Access it like this:
## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
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## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
## unsplit, which.max, which.min## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyreh_query = query(eh, c("multiWGCNAdata"))
## download the autism data and metadata
autism_se = eh_query[["EH8219"]]## see ?multiWGCNAdata and browseVignettes('multiWGCNAdata') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## require("SummarizedExperiment")Now, proceed with the multiWGCNA analysis:
## 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## 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 = FALSEWe 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!
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 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/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
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## 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
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## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] multiWGCNA_1.10.0 ggalluvial_0.12.6
## [3] ggplot2_4.0.3 SummarizedExperiment_1.42.0
## [5] Biobase_2.72.0 GenomicRanges_1.64.0
## [7] Seqinfo_1.2.0 IRanges_2.46.0
## [9] S4Vectors_0.50.3 MatrixGenerics_1.24.0
## [11] matrixStats_1.5.0 multiWGCNAdata_1.10.0
## [13] ExperimentHub_3.2.2 AnnotationHub_4.2.2
## [15] BiocFileCache_3.2.0 dbplyr_2.6.0
## [17] BiocGenerics_0.58.1 generics_0.1.4
## [19] BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3 rstudioapi_0.19.0
## [4] jsonlite_2.0.0 magrittr_2.0.5 farver_2.1.2
## [7] rmarkdown_2.32 vctrs_0.7.3 memoise_2.0.1
## [10] base64enc_0.1-6 htmltools_0.5.9 S4Arrays_1.12.1
## [13] dynamicTreeCut_1.63-1 curl_8.0.0 SparseArray_1.12.3
## [16] Formula_1.2-6 sass_0.4.10 bslib_0.12.0
## [19] htmlwidgets_1.6.4 httr2_1.3.0 impute_1.86.0
## [22] cachem_1.1.0 buildtools_1.0.0 igraph_2.3.4
## [25] lifecycle_1.0.5 iterators_1.0.14 pkgconfig_2.0.3
## [28] Matrix_1.7-6 R6_2.6.1 fastmap_1.2.0
## [31] digest_0.6.39 colorspace_2.1-4 patchwork_1.3.2
## [34] AnnotationDbi_1.74.0 Hmisc_5.3-0 RSQLite_3.53.3
## [37] filelock_1.0.3 httr_1.4.9 polyclip_1.10-7
## [40] abind_1.4-8 compiler_4.6.1 rngtools_1.5.2
## [43] bit64_4.8.6 withr_3.0.3 doParallel_1.0.17
## [46] htmlTable_2.5.0 S7_0.2.2 backports_1.5.1
## [49] viridis_0.6.5 DBI_1.3.0 ggforce_0.5.0
## [52] MASS_7.3-66 rappdirs_0.3.4 DelayedArray_0.38.2
## [55] flashClust_1.1-4 tools_4.6.1 foreign_0.8-91
## [58] nnet_7.3-21 glue_1.8.1 grid_4.6.1
## [61] checkmate_2.3.4 cluster_2.1.8.3 gtable_0.3.6
## [64] tzdb_0.5.0 preprocessCore_1.74.0 tidyr_1.3.2
## [67] hms_1.1.4 data.table_1.18.6.1 WGCNA_1.74
## [70] tidygraph_1.3.1 XVector_0.52.0 ggrepel_0.9.8
## [73] BiocVersion_3.23.1 foreach_1.5.2 pillar_1.11.1
## [76] stringr_1.6.0 splines_4.6.1 dplyr_1.2.1
## [79] tweenr_2.0.3 lattice_0.23-1 survival_3.8-12
## [82] bit_4.6.0 tidyselect_1.2.1 maketools_1.3.2
## [85] Biostrings_2.80.2 knitr_1.52 gridExtra_2.3.1
## [88] xfun_0.61 graphlayouts_1.2.5 stringi_1.8.9
## [91] yaml_2.3.12 evaluate_1.0.5 codetools_0.2-20
## [94] ggraph_2.2.2 tibble_3.3.1 BiocManager_1.30.27
## [97] cli_3.6.6 rpart_4.1.27 jquerylib_0.1.4
## [100] Rcpp_1.1.2 png_0.1-9 fastcluster_1.3.0
## [103] parallel_4.6.1 readr_2.2.0 blob_1.3.0
## [106] dcanr_1.28.0 doRNG_1.8.6.3 viridisLite_0.4.3
## [109] scales_1.4.0 purrr_1.2.2 crayon_1.5.3
## [112] rlang_1.3.0 cowplot_1.2.0 KEGGREST_1.52.2