I'm trying to construct a consensus network from three RNA seq datasets from three different brain regions. I am trying to use the function below to do so, given that this appears to be the most straightforward way to construct a signed network. The function seems to be returning everything it is supposed to about the consensus network, but it doesn't seem to be saving the individual TOM data in the directory. I would like to use those to compare the individual networks later. The .RData files are saving temporarily and then being deleted when the function is finished running. Is this supposed to happen? I thought that the TOM would be saved with saveIndividualTOMs = TRUE. Any insights on how to maintain the individual matrices? Thanks.
net =
blockwiseConsensusModules(
multiExpr,
TOMType = "signed",
networkType = "signed",
power = softPower,
minModuleSize = 30,
deepSplit = 2,
pamRespectsDendro = FALSE,
mergeCutHeight = 0.25,
numericLabels = TRUE,
verbose = 5,
maxBlockSize = 25000,
saveIndividualTOMs = TRUE,
individualTOMFileNames = "individualTOM-Set%sBlock%b.RData")
Session Info
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
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[4] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[7] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[10] tidyverse_2.0.0 WGCNA_1.72-1 fastcluster_1.2.3
[13] dynamicTreeCut_1.63-1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 blob_1.2.4
[3] Biostrings_2.68.1 bitops_1.0-7
[5] fastmap_1.1.1 RCurl_1.98-1.12
[7] digest_0.6.31 rpart_4.1.19
[9] timechange_0.2.0 lifecycle_1.0.3
[11] cluster_2.1.4 survival_3.5-5
[13] KEGGREST_1.40.0 RSQLite_2.3.1
[15] magrittr_2.0.3 compiler_4.3.0
[17] rlang_1.1.1 Hmisc_5.1-0
[19] tools_4.3.0 utf8_1.2.3
[21] yaml_2.3.7 data.table_1.14.8
[23] knitr_1.42 htmlwidgets_1.6.2
[25] bit_4.0.5 withr_2.5.0
[27] foreign_0.8-84 BiocGenerics_0.46.0
[29] nnet_7.3-19 grid_4.3.0
[31] stats4_4.3.0 preprocessCore_1.62.1
[33] fansi_1.0.4 colorspace_2.1-0
[35] GO.db_3.17.0 scales_1.2.1
[37] iterators_1.0.14 cli_3.6.1
[39] rmarkdown_2.21 crayon_1.5.2
[41] generics_0.1.3 rstudioapi_0.14
[43] tzdb_0.4.0 httr_1.4.6
[45] DBI_1.1.3 cachem_1.0.8
[47] zlibbioc_1.46.0 splines_4.3.0
[49] parallel_4.3.0 impute_1.74.1
[51] AnnotationDbi_1.62.1 BiocManager_1.30.20
[53] XVector_0.40.0 matrixStats_0.63.0
[55] base64enc_0.1-3 vctrs_0.6.2
[57] Matrix_1.5-4.1 hms_1.1.3
[59] IRanges_2.34.0 S4Vectors_0.38.1
[61] bit64_4.0.5 Formula_1.2-5
[63] htmlTable_2.4.1 foreach_1.5.2
[65] glue_1.6.2 codetools_0.2-19
[67] stringi_1.7.12 gtable_0.3.3
[69] GenomeInfoDb_1.36.0 munsell_0.5.0
[71] pillar_1.9.0 htmltools_0.5.5
[73] GenomeInfoDbData_1.2.10 R6_2.5.1
[75] doParallel_1.0.17 evaluate_0.21
[77] lattice_0.21-8 Biobase_2.60.0
[79] backports_1.4.1 png_0.1-8
[81] memoise_2.0.1 Rcpp_1.0.10
[83] gridExtra_2.3 checkmate_2.2.0
[85] xfun_0.39 pkgconfig_2.0.3
