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, sort, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyreh = 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 13.10915sampleTable## 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!
sessionInfo()## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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_1.0.0 ggalluvial_0.12.5
## [3] ggplot2_3.5.0 SummarizedExperiment_1.32.0
## [5] Biobase_2.62.0 GenomicRanges_1.54.1
## [7] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [9] S4Vectors_0.40.2 MatrixGenerics_1.14.0
## [11] matrixStats_1.2.0 multiWGCNAdata_1.0.0
## [13] ExperimentHub_2.10.0 AnnotationHub_3.10.1
## [15] BiocFileCache_2.10.2 dbplyr_2.5.0
## [17] BiocGenerics_0.48.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.16.0 jsonlite_1.8.8
## [3] magrittr_2.0.3 rmarkdown_2.26
## [5] zlibbioc_1.48.2 vctrs_0.6.5
## [7] memoise_2.0.1 RCurl_1.98-1.14
## [9] base64enc_0.1-3 htmltools_0.5.8.1
## [11] S4Arrays_1.2.1 dynamicTreeCut_1.63-1
## [13] curl_5.2.1 SparseArray_1.2.4
## [15] Formula_1.2-5 sass_0.4.9
## [17] bslib_0.7.0 htmlwidgets_1.6.4
## [19] impute_1.76.0 cachem_1.0.8
## [21] igraph_2.0.3 mime_0.12
## [23] lifecycle_1.0.4 iterators_1.0.14
## [25] pkgconfig_2.0.3 Matrix_1.6-5
## [27] R6_2.5.1 fastmap_1.1.1
## [29] GenomeInfoDbData_1.2.11 shiny_1.8.1.1
## [31] digest_0.6.35 colorspace_2.1-0
## [33] patchwork_1.2.0 AnnotationDbi_1.64.1
## [35] Hmisc_5.1-2 RSQLite_2.3.6
## [37] filelock_1.0.3 fansi_1.0.6
## [39] httr_1.4.7 abind_1.4-5
## [41] compiler_4.3.3 rngtools_1.5.2
## [43] bit64_4.0.5 withr_3.0.0
## [45] doParallel_1.0.17 htmlTable_2.4.2
## [47] backports_1.4.1 DBI_1.2.2
## [49] rappdirs_0.3.3 DelayedArray_0.28.0
## [51] flashClust_1.01-2 tools_4.3.3
## [53] foreign_0.8-86 interactiveDisplayBase_1.40.0
## [55] httpuv_1.6.15 nnet_7.3-19
## [57] glue_1.7.0 promises_1.3.0
## [59] grid_4.3.3 checkmate_2.3.1
## [61] cluster_2.1.6 generics_0.1.3
## [63] gtable_0.3.4 tzdb_0.4.0
## [65] preprocessCore_1.64.0 hms_1.1.3
## [67] data.table_1.15.4 WGCNA_1.72-5
## [69] utf8_1.2.4 XVector_0.42.0
## [71] ggrepel_0.9.5 BiocVersion_3.18.1
## [73] foreach_1.5.2 pillar_1.9.0
## [75] stringr_1.5.1 later_1.3.2
## [77] splines_4.3.3 dplyr_1.1.4
## [79] lattice_0.22-6 survival_3.5-8
## [81] bit_4.0.5 tidyselect_1.2.1
## [83] GO.db_3.18.0 Biostrings_2.70.3
## [85] knitr_1.46 gridExtra_2.3
## [87] bookdown_0.38 xfun_0.43
## [89] stringi_1.8.3 yaml_2.3.8
## [91] evaluate_0.23 codetools_0.2-20
## [93] tibble_3.2.1 BiocManager_1.30.22
## [95] cli_3.6.2 rpart_4.1.23
## [97] xtable_1.8-4 munsell_0.5.1
## [99] jquerylib_0.1.4 Rcpp_1.0.12
## [101] png_0.1-8 fastcluster_1.2.6
## [103] parallel_4.3.3 readr_2.1.5
## [105] blob_1.2.4 dcanr_1.18.0
## [107] doRNG_1.8.6 bitops_1.0-7
## [109] scales_1.3.0 purrr_1.0.2
## [111] crayon_1.5.2 rlang_1.1.3
## [113] cowplot_1.1.3 KEGGREST_1.42.0