Hi, guys. I wanted to make ceRNA network using gdcCEAnalysis() from GDCRNATools, however I encountered a
Step 1/3: Hypergenometric test done !
Error in cor.test.default(lncDa, mirDa, alternative = "less") :
not enough finite observations
error. I have checked if I have only mature miRNA, done voom transformation, provided ENSEMBL gene names like specified in function. Have anyone encountered this issue?
library(GDCRNATools)
samples <- c('TCGA-2F-A9KO-01', 'TCGA-2F-A9KP-01',
'TCGA-2F-A9KQ-01', 'TCGA-2F-A9KR-01',
'TCGA-2F-A9KT-01', 'TCGA-2F-A9KW-01')
num_rows <- length(demiRNAs)
num_columns <- length(samples)
mir.expr <- as.data.frame(matrix(runif(num_rows * num_columns, min = -1, max = 1), nrow = num_rows, ncol = num_columns))
rownames(mir.expr) <- demiRNA_names
colnames(mir.expr) <- samples
num_rows <- length(de_proteing_coding_names + length(delncRNA_names)
num_columns <- length(samples)
# Create a random dataframe with specified dimensions
rna.expr <- as.data.frame(matrix(runif(num_rows * num_columns, min = -1, max = 1), nrow = num_rows, ncol = num_columns))
rownames(expression) <- c(de_proteing_coding_names, delncRNA_names)
colnames(expression) <- samples
ceOutput <- gdcCEAnalysis(lnc = delncRNA_names,
pc = de_proteing_coding_names,
deMIR = rownames(demiRNA_names),
lnc.targets = 'starBase',
pc.targets = 'starBase',
rna.expr = rna.expr,
mir.expr = mir.expr)
sessionInfo( ) R version 4.3.1 (2023-06-16) Platform: x86_64-apple-darwin20 (64-bit) Running under: macOS Sonoma 14.2.1
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-x86_64/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: Europe/Warsaw tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GDCRNATools_1.22.0 DESeq2_1.42.0
[3] SummarizedExperiment_1.32.0 Biobase_2.62.0
[5] MatrixGenerics_1.14.0 matrixStats_1.2.0
[7] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[9] IRanges_2.36.0 S4Vectors_0.40.2
[11] BiocGenerics_0.48.1 magrittr_2.0.3
[13] miRBaseConverter_1.26.0 biomaRt_2.58.0
loaded via a namespace (and not attached):
[1] splines_4.3.1 later_1.3.2
[3] pbdZMQ_0.3-11 bitops_1.0-7
[5] ggplotify_0.1.2 filelock_1.0.3
[7] tibble_3.2.1 polyclip_1.10-6
[9] graph_1.80.0 XML_3.99-0.16
[11] lifecycle_1.0.4 rstatix_0.7.2
[13] edgeR_4.0.12 doParallel_1.0.17
[15] lattice_0.22-5 MASS_7.3-60.0.1
[17] backports_1.4.1 limma_3.58.1
[19] yaml_2.3.8 httpuv_1.6.13
[21] cowplot_1.1.2 DBI_1.2.1
[23] RColorBrewer_1.1-3 abind_1.4-5
[25] zlibbioc_1.48.0 purrr_1.0.2
[27] ggraph_2.1.0 RCurl_1.98-1.14
[29] yulab.utils_0.1.3 tweenr_2.0.2
[31] rappdirs_0.3.3 circlize_0.4.15
[33] GenomeInfoDbData_1.2.11 KMsurv_0.1-5
[35] enrichplot_1.22.0 ggrepel_0.9.5
[37] tidytree_0.4.6 codetools_0.2-19
[39] DelayedArray_0.28.0 DOSE_3.28.2
[41] DT_0.31 xml2_1.3.6
[43] ggforce_0.4.1 tidyselect_1.2.0
[45] shape_1.4.6 aplot_0.2.2
[47] farver_2.1.1 viridis_0.6.4
[49] pathview_1.42.0 BiocFileCache_2.10.1
[51] jsonlite_1.8.8 GetoptLong_1.0.5
[53] ellipsis_0.3.2 tidygraph_1.3.0
[55] survival_3.5-7 iterators_1.0.14
[57] foreach_1.5.2 tools_4.3.1
[59] progress_1.2.3 treeio_1.26.0
[61] Rcpp_1.0.12 glue_1.7.0
[63] GenomicDataCommons_1.26.0 gridExtra_2.3
[65] SparseArray_1.2.3 xfun_0.41
[67] qvalue_2.34.0 dplyr_1.1.4
[69] withr_3.0.0 BiocManager_1.30.22
[71] fastmap_1.1.1 fansi_1.0.6
[73] caTools_1.18.2 digest_0.6.34
[75] R6_2.5.1 mime_0.12
[77] gridGraphics_0.5-1 colorspace_2.1-0
[79] GO.db_3.18.0 gtools_3.9.5
[81] RSQLite_2.3.5 utf8_1.2.4
[83] tidyr_1.3.0 generics_0.1.3
[85] data.table_1.14.10 prettyunits_1.2.0
[87] graphlayouts_1.1.0 httr_1.4.7
[89] htmlwidgets_1.6.4 S4Arrays_1.2.0
[91] scatterpie_0.2.1 pkgconfig_2.0.3
[93] gtable_0.3.4 blob_1.2.4
[95] ComplexHeatmap_2.18.0 XVector_0.42.0
[97] survMisc_0.5.6 clusterProfiler_4.10.0
[99] shadowtext_0.1.3 htmltools_0.5.7
[101] carData_3.0-5 fgsea_1.28.0
[103] clue_0.3-65 scales_1.3.0
[105] png_0.1-8 ggfun_0.1.4
[107] knitr_1.45 km.ci_0.5-6
[109] rstudioapi_0.15.0 tzdb_0.4.0
[111] reshape2_1.4.4 rjson_0.2.21
[113] nlme_3.1-164 curl_5.2.0
[115] org.Hs.eg.db_3.18.0 zoo_1.8-12
[117] cachem_1.0.8 GlobalOptions_0.1.2
[119] stringr_1.5.1 KernSmooth_2.23-22
[121] BiocVersion_3.18.1 parallel_4.3.1
[123] HDO.db_0.99.1 AnnotationDbi_1.64.1
[125] pillar_1.9.0 grid_4.3.1
[127] vctrs_0.6.5 gplots_3.1.3
[129] ggpubr_0.6.0 promises_1.2.1
[131] car_3.1-2 dbplyr_2.4.0
[133] xtable_1.8-4 cluster_2.1.6
[135] Rgraphviz_2.46.0 KEGGgraph_1.62.0
[137] readr_2.1.5 cli_3.6.2
[139] locfit_1.5-9.8 compiler_4.3.1
[141] rlang_1.1.3 crayon_1.5.2
[143] ggsignif_0.6.4 survminer_0.4.9
[145] plyr_1.8.9 fs_1.6.3
[147] stringi_1.8.3 viridisLite_0.4.2
[149] BiocParallel_1.36.0 munsell_0.5.0
[151] Biostrings_2.70.1 lazyeval_0.2.2
[153] GOSemSim_2.28.1 Matrix_1.6-5
[155] hms_1.1.3 patchwork_1.2.0
[157] bit64_4.0.5 ggplot2_3.4.4
[159] KEGGREST_1.42.0 statmod_1.5.0
[161] shiny_1.8.0 interactiveDisplayBase_1.40.0
[163] AnnotationHub_3.10.0 broom_1.0.5
[165] igraph_1.6.0 memoise_2.0.1
[167] ggtree_3.10.0 fastmatch_1.1-4
[169] bit_4.0.5 ape_5.7-1
[171] gson_0.1.0
```
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