Hello,
I'm running a Gene regulatory network inference. Host 13 is the file that contain the observations, Host13tfs is the file that contain 116 transcription factors. Two columns: one is the protein ID and the second column lists the family name that the transcription factor belongs to. I followed the steps in https://bioconductor.org/packages/release/bioc/vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html
When I get to the step to calculate the SFT fit and select the best fit for each network, this is the code I wrote based on the tutorial:
> grn <- exp2grn(exp = final_exp, regulators = Host13tfs$Protein.ID, nTrees = 10)
I get the following error:
Error in .checkArguments(exprMatrix = exprMatrix, regulators = regulators, :
Provide at least 2 potential regulators.
Please let me know how can I fix this problem. Thanks, Adriana
```r
Host13tfs<-read.csv("Host13tfs.csv") Host13tfs Protein.ID Family 1 199670 SNF2 2 246194 NmrA 3 235769 CP2 4 111668 GATA
grn <- exp2grn(exp = final_exp, regulators = Host13tfs$Protein.ID, nTrees = 10) Error in .checkArguments(exprMatrix = exprMatrix, regulators = regulators, : Provide at least 2 potential regulators.
sessionInfo( )
R version 4.3.0 (2023-04-21) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Ventura 13.4.1
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York tzcode source: internal
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] BioNERO_1.8.5
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 ggdendro_0.1.23 rstudioapi_0.15.0
[4] shape_1.4.6 NetRep_1.2.6 magrittr_2.0.3
[7] rmarkdown_2.23 GlobalOptions_0.1.2 zlibbioc_1.46.0
[10] vctrs_0.6.3 memoise_2.0.1 RCurl_1.98-1.12
[13] base64enc_0.1-3 htmltools_0.5.5 S4Arrays_1.0.5
[16] dynamicTreeCut_1.63-1 Formula_1.2-5 htmlwidgets_1.6.2
[19] plyr_1.8.8 impute_1.74.1 cachem_1.0.8
[22] igraph_1.5.0.1 lifecycle_1.0.3 ggnetwork_0.5.12
[25] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.6-0
[28] R6_2.5.1 fastmap_1.1.1 GenomeInfoDbData_1.2.10
[31] MatrixGenerics_1.12.3 clue_0.3-64 digest_0.6.33
[34] colorspace_2.1-0 patchwork_1.1.2 AnnotationDbi_1.62.2
[37] S4Vectors_0.38.1 GENIE3_1.22.0 Hmisc_5.1-0
[40] GenomicRanges_1.52.0 RSQLite_2.3.1 fansi_1.0.4
[43] httr_1.4.6 abind_1.4-5 mgcv_1.9-0
[46] compiler_4.3.0 bit64_4.0.5 doParallel_1.0.17
[49] htmlTable_2.4.1 backports_1.4.1 BiocParallel_1.34.2
[52] DBI_1.1.3 intergraph_2.0-2 highr_0.10
[55] MASS_7.3-60 DelayedArray_0.26.7 rjson_0.2.21
[58] tools_4.3.0 foreign_0.8-84 nnet_7.3-19
[61] glue_1.6.2 nlme_3.1-163 grid_4.3.0
[64] checkmate_2.2.0 cluster_2.1.4 reshape2_1.4.4
[67] generics_0.1.3 sva_3.48.0 gtable_0.3.3
[70] preprocessCore_1.62.1 data.table_1.14.8 WGCNA_1.72-1
[73] utf8_1.2.3 XVector_0.40.0 BiocGenerics_0.46.0
[76] ggrepel_0.9.3 foreach_1.5.2 pillar_1.9.0
[79] stringr_1.5.0 limma_3.56.2 genefilter_1.82.1
[82] circlize_0.4.15 splines_4.3.0 dplyr_1.1.2
[85] lattice_0.21-8 survival_3.5-5 bit_4.0.5
[88] annotate_1.78.0 tidyselect_1.2.0 locfit_1.5-9.8
[91] GO.db_3.17.0 ComplexHeatmap_2.16.0 Biostrings_2.68.1
[94] knitr_1.43 gridExtra_2.3 IRanges_2.34.1
[97] edgeR_3.42.4 SummarizedExperiment_1.30.2 RhpcBLASctl_0.23-42
[100] stats4_4.3.0 xfun_0.39 Biobase_2.60.0
[103] statmod_1.5.0 matrixStats_1.0.0 stringi_1.7.12
[106] statnet.common_4.9.0 minet_3.58.0 evaluate_0.21
[109] codetools_0.2-19 tibble_3.2.1 cli_3.6.1
[112] rpart_4.1.19 xtable_1.8-4 munsell_0.5.0
[115] network_1.18.1 Rcpp_1.0.11 GenomeInfoDb_1.36.1
[118] coda_0.19-4 png_0.1-8 fastcluster_1.2.3
[121] XML_3.99-0.14 parallel_4.3.0 ggplot2_3.4.2
[124] blob_1.2.4 bitops_1.0-7 scales_1.2.1
[127] crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.1
[130] KEGGREST_1.40.0
```r
