=== Log started: 2026-03-04 22:00:16.481997 === Session info: R version 4.5.2 (2025-10-31) Platform: x86_64-conda-linux-gnu Running under: Ubuntu 24.04.4 LTS Matrix products: default BLAS/LAPACK: /home/shared/8TB_HDD_02/shedurkin/.local/share/r-miniconda/envs/r_enet_rscript/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0 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 time zone: America/Los_Angeles tzcode source: system (glibc) attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets [8] methods base other attached packages: [1] pbapply_1.7-4 doParallel_1.0.17 [3] iterators_1.0.14 foreach_1.5.2 [5] scales_1.4.0 genefilter_1.80.3 [7] ggfortify_0.4.18 vegan_2.7-1 [9] permute_0.9-8 factoextra_1.0.7 [11] caret_7.0-1 lattice_0.22-7 [13] glmnet_4.1-10 Matrix_1.7-4 [15] pheatmap_1.0.13 rvest_1.0.5 [17] corrplot_0.95 ggcorrplot_0.1.4.1 [19] reshape2_1.4.4 edgeR_3.40.2 [21] limma_3.54.2 WGCNA_1.73 [23] fastcluster_1.3.0 dynamicTreeCut_1.63-1 [25] ggraph_2.2.1 tidygraph_1.3.1 [27] psych_2.5.6 igraph_2.1.4 [29] DESeq2_1.38.3 SummarizedExperiment_1.28.0 [31] Biobase_2.58.0 MatrixGenerics_1.10.0 [33] matrixStats_1.5.0 GenomicRanges_1.50.2 [35] GenomeInfoDb_1.34.9 IRanges_2.32.0 [37] S4Vectors_0.36.2 BiocGenerics_0.44.0 [39] lubridate_1.9.4 forcats_1.0.1 [41] stringr_1.6.0 dplyr_1.1.4 [43] purrr_1.2.0 readr_2.1.6 [45] tidyr_1.3.1 tibble_3.3.0 [47] ggplot2_3.5.2 tidyverse_2.0.0 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 shape_1.4.6.1 rstudioapi_0.17.1 [4] magrittr_2.0.4 farver_2.1.2 rmarkdown_2.29 [7] zlibbioc_1.44.0 vctrs_0.6.5 memoise_2.0.1 [10] RCurl_1.98-1.17 base64enc_0.1-3 htmltools_0.5.8.1 [13] pROC_1.18.5 Formula_1.2-5 parallelly_1.45.0 [16] htmlwidgets_1.6.4 plyr_1.8.9 impute_1.72.3 [19] cachem_1.1.0 lifecycle_1.0.4 pkgconfig_2.0.3 [22] R6_2.6.1 fastmap_1.2.0 future_1.58.0 [25] GenomeInfoDbData_1.2.9 digest_0.6.39 colorspace_2.1-2 [28] AnnotationDbi_1.60.2 geneplotter_1.76.0 Hmisc_5.2-3 [31] RSQLite_2.4.2 timechange_0.3.0 mgcv_1.9-4 [34] httr_1.4.7 polyclip_1.10-7 compiler_4.5.2 [37] bit64_4.6.0-1 withr_3.0.2 htmlTable_2.4.3 [40] backports_1.5.0 BiocParallel_1.32.6 viridis_0.6.5 [43] DBI_1.2.3 ggforce_0.5.0 lava_1.8.1 [46] MASS_7.3-65 DelayedArray_0.24.0 ModelMetrics_1.2.2.2 [49] tools_4.5.2 foreign_0.8-90 future.apply_1.20.0 [52] nnet_7.3-20 glue_1.8.0 nlme_3.1-168 [55] grid_4.5.2 checkmate_2.3.2 cluster_2.1.8.1 [58] recipes_1.3.1 generics_0.1.4 gtable_0.3.6 [61] tzdb_0.5.0 class_7.3-23 preprocessCore_1.60.2 [64] data.table_1.17.8 hms_1.1.4 xml2_1.5.0 [67] XVector_0.38.0 ggrepel_0.9.6 pillar_1.11.1 [70] splines_4.5.2 tweenr_2.0.3 survival_3.8-3 [73] bit_4.6.0 annotate_1.76.0 tidyselect_1.2.1 [76] GO.db_3.16.0 locfit_1.5-9.12 Biostrings_2.66.0 [79] knitr_1.50 gridExtra_2.3 xfun_0.54 [82] graphlayouts_1.2.2 hardhat_1.4.1 timeDate_4041.110 [85] stringi_1.8.7 evaluate_1.0.5 codetools_0.2-20 [88] cli_3.6.5 rpart_4.1.24 xtable_1.8-4 [91] dichromat_2.0-0.1 Rcpp_1.1.0 globals_0.18.0 [94] png_0.1-8 XML_3.99-0.18 gower_1.0.2 [97] blob_1.2.4 bitops_1.0-9 listenv_0.9.1 [100] viridisLite_0.4.2 ipred_0.9-15 prodlim_2025.04.28 [103] crayon_1.5.3 rlang_1.1.6 KEGGREST_1.38.0 [106] mnormt_2.1.1 Start time:2026-03-04 22:00:16.525648 Defining model functions Loading gene counts Loading miRNA counts Loading lncRNA counts Loading WGBS data Loading metadata table Species prefix and code:POC,Ptuh WGBS column name handling Filtering datasets filtering low count features Ensuring integer counts Filtered dimensions: Genes: 447 x 32 miRNA: 40 x 32 lncRNA: 11236 x 32 WGBS: 21025 x 32 Ensuring all dfs have identical column names and orders Applying variance stabilization Predictor set dimensions:32 Predictor set dimensions:32301 Gene set dimensions:32 Gene set dimensions:447 === Data Validation and Cleaning === Predictor matrix: 32 samples x 32301 features Columns with NaN: 0 Columns with Inf: 0 Columns with NA: 8685 Constant columns (sd = 0): 0 Total problematic columns: 8685 Problematic columns by type: Other: 8685 Removed 8685 problematic predictors. Remaining: 23616 Response matrix: 32 samples x 447 genes Genes with NaN/Inf/NA/constant values: 0 Samples available: 32 Cleaned data dimensions: Predictors: 32 samples x 23616 features Genes: 32 samples x 447 genes Pre-flight test: fitting one gene sequentially to verify data compatibility... Pre-flight passed: gene 'OG_02619', R2 = 0.0000, 0 non-zero coefficients === Data validation complete === === PART 1: Elastic Net with Bootstrapped Train/Test Splits === --- Round 1: 10 bootstrap replicates across all 447 genes --- Round1: Running 10 replicates across 10 cores... Round1: 0/10 (0.0%) Round1: 10/10 (100.0%) Round 1 results: 172/447 genes with mean R2 >= 0.50 --- Round 2: 10 bootstrap replicates on 172 well-predicted genes --- Round2: Running 10 replicates across 10 cores... Round2: 0/10 (0.0%) Round2: 10/10 (100.0%) Round 2 performance summary: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2078 0.5535 0.6695 0.6477 0.7520 0.8977 Bootstrapping completed in 9.15 minutes === PART 1.5: R-squared Bootstrap Plots === Plotting Round 1 R² (all genes)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_R2_round1-allgenes.png Plotting Round 2 R² (well-predicted genes)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_R2_round2-well-predictedgenes.png === PART 2: Stability Selection for Predictor Reliability === This step assesses how reliably each predictor is selected across random subsamples of the data, providing a robust measure of predictor importance. Unlike permutation testing, stability selection avoids the degenerate null distribution problem that arises with sparse penalized regression. Running stability selection for 172 well-predicted genes Calculating stability selection for 172 genes with 10 subsample iterations each Total predictors per gene: 23616 Using 10 core(s) for gene-level parallelization Complementary pairs: each iteration produces 2 fits (20 total fits per gene) StabSel: Running in parallel mode across 172 genes... StabSel: 0/172 (0.0%) StabSel: 172/172 (100.0%) === Stability Selection Summary === Total predictor-gene combinations: 4061952 Predictors ever selected (in at least 1 fit): 75658 (1.86%) Average predictors with any selection per gene: 439.9 Subsample iterations per gene: 10 (producing 20 complementary-pair fits) Stability selection completed in 1.70 minutes Applying PFER bound with selection threshold pi = 0.60 PFER bound applied with selection threshold pi = 0.60 Theoretical guarantee: E(false selections) <= PFER_bound per gene Per-gene PFER diagnostics: OG_00305: PFER <= 2.401 | Stable predictors: 53 | Avg selected/fit: 106.5 | p = 23616 OG_00314: PFER <= 1.420 | Stable predictors: 37 | Avg selected/fit: 81.9 | p = 23616 OG_00355: PFER <= 1.915 | Stable predictors: 31 | Avg selected/fit: 95.1 | p = 23616 OG_00430: PFER <= 1.773 | Stable predictors: 32 | Avg selected/fit: 91.5 | p = 23616 OG_00466: PFER <= 2.109 | Stable predictors: 36 | Avg selected/fit: 99.8 | p = 23616 OG_00510: PFER <= 2.470 | Stable predictors: 57 | Avg selected/fit: 108.0 | p = 23616 OG_00514: PFER <= 2.442 | Stable predictors: 39 | Avg selected/fit: 107.4 | p = 23616 OG_00572: PFER <= 1.034 | Stable predictors: 20 | Avg selected/fit: 69.9 | p = 23616 OG_00632: PFER <= 3.567 | Stable predictors: 75 | Avg selected/fit: 129.8 | p = 23616 OG_00807: PFER <= 1.980 | Stable predictors: 40 | Avg selected/fit: 96.7 | p = 23616 OG_01023: PFER <= 1.150 | Stable predictors: 40 | Avg selected/fit: 73.7 | p = 23616 OG_01121: PFER <= 2.134 | Stable predictors: 48 | Avg selected/fit: 100.4 | p = 23616 OG_01147: PFER <= 2.943 | Stable predictors: 55 | Avg selected/fit: 117.9 | p = 23616 OG_01155: PFER <= 2.151 | Stable predictors: 60 | Avg selected/fit: 100.8 | p = 23616 OG_01177: PFER <= 2.401 | Stable predictors: 33 | Avg selected/fit: 106.5 | p = 23616 OG_01223: PFER <= 2.392 | Stable predictors: 34 | Avg selected/fit: 106.3 | p = 23616 OG_01312: PFER <= 2.164 | Stable predictors: 28 | Avg selected/fit: 101.1 | p = 23616 OG_01317: PFER <= 2.321 | Stable predictors: 51 | Avg selected/fit: 104.7 | p = 23616 OG_01353: PFER <= 1.895 | Stable predictors: 27 | Avg selected/fit: 94.6 | p = 23616 OG_01354: PFER <= 2.096 | Stable predictors: 46 | Avg selected/fit: 99.5 | p = 23616 OG_01452: PFER <= 2.075 | Stable predictors: 23 | Avg selected/fit: 99.0 | p = 23616 OG_01486: PFER <= 1.685 | Stable predictors: 31 | Avg selected/fit: 89.2 | p = 23616 OG_01489: PFER <= 2.442 | Stable predictors: 58 | Avg selected/fit: 107.4 | p = 23616 OG_01608: PFER <= 1.923 | Stable predictors: 42 | Avg selected/fit: 95.3 | p = 23616 OG_01611: PFER <= 2.194 | Stable predictors: 57 | Avg selected/fit: 101.8 | p = 23616 OG_01630: PFER <= 2.092 | Stable predictors: 45 | Avg selected/fit: 99.4 | p = 23616 OG_01724: PFER <= 1.784 | Stable predictors: 32 | Avg selected/fit: 91.8 | p = 23616 OG_01781: PFER <= 1.815 | Stable predictors: 35 | Avg selected/fit: 92.6 | p = 23616 OG_01981: PFER <= 1.711 | Stable predictors: 34 | Avg selected/fit: 89.9 | p = 23616 OG_01999: PFER <= 0.873 | Stable predictors: 8 | Avg selected/fit: 64.2 | p = 23616 OG_02012: PFER <= 2.130 | Stable predictors: 26 | Avg selected/fit: 100.3 | p = 23616 OG_02090: PFER <= 1.927 | Stable predictors: 32 | Avg selected/fit: 95.4 | p = 23616 OG_02102: PFER <= 2.470 | Stable predictors: 46 | Avg selected/fit: 108.0 | p = 23616 OG_02146: PFER <= 1.992 | Stable predictors: 36 | Avg selected/fit: 97.0 | p = 23616 OG_02184: PFER <= 1.996 | Stable predictors: 56 | Avg selected/fit: 97.1 | p = 23616 OG_02190: PFER <= 1.939 | Stable predictors: 37 | Avg selected/fit: 95.7 | p = 23616 OG_02220: PFER <= 1.827 | Stable predictors: 38 | Avg selected/fit: 92.9 | p = 23616 OG_02316: PFER <= 2.849 | Stable predictors: 60 | Avg selected/fit: 116.0 | p = 23616 OG_02317: PFER <= 2.272 | Stable predictors: 44 | Avg selected/fit: 103.6 | p = 23616 OG_02358: PFER <= 2.220 | Stable predictors: 41 | Avg selected/fit: 102.4 | p = 23616 OG_02439: PFER <= 0.517 | Stable predictors: 6 | Avg selected/fit: 49.4 | p = 23616 OG_02537: PFER <= 2.908 | Stable predictors: 30 | Avg selected/fit: 117.2 | p = 23616 OG_02666: PFER <= 2.795 | Stable predictors: 49 | Avg selected/fit: 114.9 | p = 23616 OG_02935: PFER <= 1.570 | Stable predictors: 35 | Avg selected/fit: 86.1 | p = 23616 OG_03268: PFER <= 1.643 | Stable predictors: 37 | Avg selected/fit: 88.1 | p = 23616 OG_03289: PFER <= 2.689 | Stable predictors: 52 | Avg selected/fit: 112.7 | p = 23616 OG_03291: PFER <= 2.272 | Stable predictors: 38 | Avg selected/fit: 103.6 | p = 23616 OG_03298: PFER <= 1.907 | Stable predictors: 36 | Avg selected/fit: 94.9 | p = 23616 OG_03451: PFER <= 2.173 | Stable predictors: 44 | Avg selected/fit: 101.3 | p = 23616 OG_03671: PFER <= 1.636 | Stable predictors: 38 | Avg selected/fit: 87.9 | p = 23616 OG_03673: PFER <= 2.506 | Stable predictors: 45 | Avg selected/fit: 108.8 | p = 23616 OG_04115: PFER <= 2.186 | Stable predictors: 34 | Avg selected/fit: 101.6 | p = 23616 OG_04165: PFER <= 2.042 | Stable predictors: 53 | Avg selected/fit: 98.2 | p = 23616 OG_04209: PFER <= 2.021 | Stable predictors: 44 | Avg selected/fit: 97.7 | p = 23616 OG_04243: PFER <= 2.708 | Stable predictors: 50 | Avg selected/fit: 113.1 | p = 23616 OG_04372: PFER <= 1.265 | Stable predictors: 24 | Avg selected/fit: 77.3 | p = 23616 OG_04514: PFER <= 2.017 | Stable predictors: 45 | Avg selected/fit: 97.6 | p = 23616 OG_04547: PFER <= 2.312 | Stable predictors: 41 | Avg selected/fit: 104.5 | p = 23616 OG_04723: PFER <= 2.075 | Stable predictors: 44 | Avg selected/fit: 99.0 | p = 23616 OG_05514: PFER <= 2.820 | Stable predictors: 40 | Avg selected/fit: 115.4 | p = 23616 OG_05718: PFER <= 2.766 | Stable predictors: 45 | Avg selected/fit: 114.3 | p = 23616 OG_05749: PFER <= 2.580 | Stable predictors: 53 | Avg selected/fit: 110.4 | p = 23616 OG_05950: PFER <= 2.242 | Stable predictors: 38 | Avg selected/fit: 102.9 | p = 23616 OG_06101: PFER <= 2.419 | Stable predictors: 41 | Avg selected/fit: 106.9 | p = 23616 OG_06119: PFER <= 1.859 | Stable predictors: 33 | Avg selected/fit: 93.7 | p = 23616 OG_06158: PFER <= 1.239 | Stable predictors: 28 | Avg selected/fit: 76.5 | p = 23616 OG_06198: PFER <= 2.216 | Stable predictors: 59 | Avg selected/fit: 102.3 | p = 23616 OG_06234: PFER <= 1.992 | Stable predictors: 36 | Avg selected/fit: 97.0 | p = 23616 OG_06236: PFER <= 2.194 | Stable predictors: 25 | Avg selected/fit: 101.8 | p = 23616 OG_06279: PFER <= 1.555 | Stable predictors: 28 | Avg selected/fit: 85.7 | p = 23616 OG_06592: PFER <= 1.769 | Stable predictors: 37 | Avg selected/fit: 91.4 | p = 23616 OG_06670: PFER <= 2.727 | Stable predictors: 48 | Avg selected/fit: 113.5 | p = 23616 OG_06712: PFER <= 2.325 | Stable predictors: 43 | Avg selected/fit: 104.8 | p = 23616 OG_06717: PFER <= 1.662 | Stable predictors: 40 | Avg selected/fit: 88.6 | p = 23616 OG_06754: PFER <= 1.792 | Stable predictors: 34 | Avg selected/fit: 92.0 | p = 23616 OG_06908: PFER <= 0.700 | Stable predictors: 23 | Avg selected/fit: 57.5 | p = 23616 OG_06998: PFER <= 2.515 | Stable predictors: 66 | Avg selected/fit: 109.0 | p = 23616 OG_07000: PFER <= 2.392 | Stable predictors: 40 | Avg selected/fit: 106.3 | p = 23616 OG_07150: PFER <= 2.483 | Stable predictors: 34 | Avg selected/fit: 108.3 | p = 23616 OG_07183: PFER <= 2.339 | Stable predictors: 43 | Avg selected/fit: 105.1 | p = 23616 OG_07330: PFER <= 1.996 | Stable predictors: 39 | Avg selected/fit: 97.1 | p = 23616 OG_07404: PFER <= 2.004 | Stable predictors: 45 | Avg selected/fit: 97.3 | p = 23616 OG_07641: PFER <= 1.859 | Stable predictors: 33 | Avg selected/fit: 93.7 | p = 23616 OG_07822: PFER <= 2.492 | Stable predictors: 47 | Avg selected/fit: 108.5 | p = 23616 OG_07892: PFER <= 1.172 | Stable predictors: 33 | Avg selected/fit: 74.4 | p = 23616 OG_08083: PFER <= 2.361 | Stable predictors: 29 | Avg selected/fit: 105.6 | p = 23616 OG_08096: PFER <= 2.392 | Stable predictors: 46 | Avg selected/fit: 106.3 | p = 23616 OG_08143: PFER <= 1.122 | Stable predictors: 14 | Avg selected/fit: 72.8 | p = 23616 OG_08347: PFER <= 2.251 | Stable predictors: 30 | Avg selected/fit: 103.1 | p = 23616 OG_08497: PFER <= 2.143 | Stable predictors: 40 | Avg selected/fit: 100.6 | p = 23616 OG_08545: PFER <= 3.110 | Stable predictors: 67 | Avg selected/fit: 121.2 | p = 23616 OG_08639: PFER <= 2.029 | Stable predictors: 45 | Avg selected/fit: 97.9 | p = 23616 OG_08658: PFER <= 2.795 | Stable predictors: 38 | Avg selected/fit: 114.9 | p = 23616 OG_08842: PFER <= 2.670 | Stable predictors: 52 | Avg selected/fit: 112.3 | p = 23616 OG_08849: PFER <= 2.130 | Stable predictors: 57 | Avg selected/fit: 100.3 | p = 23616 OG_08856: PFER <= 1.792 | Stable predictors: 51 | Avg selected/fit: 92.0 | p = 23616 OG_08878: PFER <= 2.548 | Stable predictors: 25 | Avg selected/fit: 109.7 | p = 23616 OG_09174: PFER <= 2.670 | Stable predictors: 46 | Avg selected/fit: 112.3 | p = 23616 OG_09310: PFER <= 1.831 | Stable predictors: 34 | Avg selected/fit: 93.0 | p = 23616 OG_09423: PFER <= 1.911 | Stable predictors: 41 | Avg selected/fit: 95.0 | p = 23616 OG_09424: PFER <= 1.980 | Stable predictors: 32 | Avg selected/fit: 96.7 | p = 23616 OG_09455: PFER <= 1.393 | Stable predictors: 27 | Avg selected/fit: 81.1 | p = 23616 OG_09486: PFER <= 1.606 | Stable predictors: 31 | Avg selected/fit: 87.1 | p = 23616 OG_09511: PFER <= 1.298 | Stable predictors: 23 | Avg selected/fit: 78.3 | p = 23616 OG_09596: PFER <= 1.658 | Stable predictors: 38 | Avg selected/fit: 88.5 | p = 23616 OG_09657: PFER <= 2.246 | Stable predictors: 34 | Avg selected/fit: 103.0 | p = 23616 OG_09806: PFER <= 1.951 | Stable predictors: 50 | Avg selected/fit: 96.0 | p = 23616 OG_09942: PFER <= 2.675 | Stable predictors: 46 | Avg selected/fit: 112.4 | p = 23616 OG_10004: PFER <= 2.520 | Stable predictors: 40 | Avg selected/fit: 109.1 | p = 23616 OG_10039: PFER <= 2.312 | Stable predictors: 30 | Avg selected/fit: 104.5 | p = 23616 OG_10109: PFER <= 1.580 | Stable predictors: 44 | Avg selected/fit: 86.4 | p = 23616 OG_10121: PFER <= 1.943 | Stable predictors: 42 | Avg selected/fit: 95.8 | p = 23616 OG_10205: PFER <= 1.169 | Stable predictors: 33 | Avg selected/fit: 74.3 | p = 23616 OG_10265: PFER <= 2.609 | Stable predictors: 66 | Avg selected/fit: 111.0 | p = 23616 OG_10267: PFER <= 2.237 | Stable predictors: 51 | Avg selected/fit: 102.8 | p = 23616 OG_10274: PFER <= 2.627 | Stable predictors: 34 | Avg selected/fit: 111.4 | p = 23616 OG_13910: PFER <= 2.543 | Stable predictors: 47 | Avg selected/fit: 109.6 | p = 23616 OG_14012: PFER <= 1.899 | Stable predictors: 39 | Avg selected/fit: 94.7 | p = 23616 OG_14038: PFER <= 2.723 | Stable predictors: 52 | Avg selected/fit: 113.4 | p = 23616 OG_14066: PFER <= 2.121 | Stable predictors: 27 | Avg selected/fit: 100.1 | p = 23616 OG_14094: PFER <= 2.033 | Stable predictors: 39 | Avg selected/fit: 98.0 | p = 23616 OG_14189: PFER <= 2.290 | Stable predictors: 30 | Avg selected/fit: 104.0 | p = 23616 OG_14257: PFER <= 2.251 | Stable predictors: 39 | Avg selected/fit: 103.1 | p = 23616 OG_14279: PFER <= 2.050 | Stable predictors: 39 | Avg selected/fit: 98.4 | p = 23616 OG_14295: PFER <= 3.064 | Stable predictors: 78 | Avg selected/fit: 120.3 | p = 23616 OG_14514: PFER <= 2.168 | Stable predictors: 43 | Avg selected/fit: 101.2 | p = 23616 OG_14540: PFER <= 2.479 | Stable predictors: 46 | Avg selected/fit: 108.2 | p = 23616 OG_14685: PFER <= 2.993 | Stable predictors: 49 | Avg selected/fit: 118.9 | p = 23616 OG_14793: PFER <= 2.785 | Stable predictors: 55 | Avg selected/fit: 114.7 | p = 23616 OG_14915: PFER <= 2.781 | Stable predictors: 51 | Avg selected/fit: 114.6 | p = 23616 OG_15303: PFER <= 2.553 | Stable predictors: 59 | Avg selected/fit: 109.8 | p = 23616 OG_15459: PFER <= 1.843 | Stable predictors: 25 | Avg selected/fit: 93.3 | p = 23616 OG_15550: PFER <= 1.992 | Stable predictors: 38 | Avg selected/fit: 97.0 | p = 23616 OG_15594: PFER <= 2.000 | Stable predictors: 42 | Avg selected/fit: 97.2 | p = 23616 OG_15627: PFER <= 1.431 | Stable predictors: 31 | Avg selected/fit: 82.2 | p = 23616 OG_15682: PFER <= 1.455 | Stable predictors: 34 | Avg selected/fit: 82.9 | p = 23616 OG_15759: PFER <= 1.603 | Stable predictors: 32 | Avg selected/fit: 87.0 | p = 23616 OG_15815: PFER <= 1.229 | Stable predictors: 27 | Avg selected/fit: 76.2 | p = 23616 OG_15907: PFER <= 1.147 | Stable predictors: 29 | Avg selected/fit: 73.6 | p = 23616 OG_15920: PFER <= 2.548 | Stable predictors: 37 | Avg selected/fit: 109.7 | p = 23616 OG_16011: PFER <= 2.348 | Stable predictors: 42 | Avg selected/fit: 105.3 | p = 23616 OG_16159: PFER <= 1.931 | Stable predictors: 45 | Avg selected/fit: 95.5 | p = 23616 OG_16192: PFER <= 2.557 | Stable predictors: 47 | Avg selected/fit: 109.9 | p = 23616 OG_16383: PFER <= 1.704 | Stable predictors: 36 | Avg selected/fit: 89.7 | p = 23616 OG_16489: PFER <= 1.606 | Stable predictors: 39 | Avg selected/fit: 87.1 | p = 23616 OG_16539: PFER <= 2.155 | Stable predictors: 40 | Avg selected/fit: 100.9 | p = 23616 OG_16548: PFER <= 2.727 | Stable predictors: 41 | Avg selected/fit: 113.5 | p = 23616 OG_16592: PFER <= 1.963 | Stable predictors: 49 | Avg selected/fit: 96.3 | p = 23616 OG_16665: PFER <= 2.334 | Stable predictors: 43 | Avg selected/fit: 105.0 | p = 23616 OG_16747: PFER <= 2.361 | Stable predictors: 44 | Avg selected/fit: 105.6 | p = 23616 OG_16789: PFER <= 2.151 | Stable predictors: 42 | Avg selected/fit: 100.8 | p = 23616 OG_16980: PFER <= 1.943 | Stable predictors: 38 | Avg selected/fit: 95.8 | p = 23616 OG_17006: PFER <= 1.963 | Stable predictors: 39 | Avg selected/fit: 96.3 | p = 23616 OG_17014: PFER <= 2.121 | Stable predictors: 45 | Avg selected/fit: 100.1 | p = 23616 OG_17015: PFER <= 2.164 | Stable predictors: 37 | Avg selected/fit: 101.1 | p = 23616 OG_17045: PFER <= 1.321 | Stable predictors: 21 | Avg selected/fit: 79.0 | p = 23616 OG_17106: PFER <= 1.726 | Stable predictors: 45 | Avg selected/fit: 90.3 | p = 23616 OG_17124: PFER <= 2.637 | Stable predictors: 41 | Avg selected/fit: 111.6 | p = 23616 OG_17157: PFER <= 0.416 | Stable predictors: 9 | Avg selected/fit: 44.3 | p = 23616 OG_17176: PFER <= 2.251 | Stable predictors: 46 | Avg selected/fit: 103.1 | p = 23616 OG_17285: PFER <= 1.867 | Stable predictors: 34 | Avg selected/fit: 93.9 | p = 23616 OG_17483: PFER <= 2.566 | Stable predictors: 42 | Avg selected/fit: 110.1 | p = 23616 OG_17646: PFER <= 2.829 | Stable predictors: 67 | Avg selected/fit: 115.6 | p = 23616 OG_17666: PFER <= 3.069 | Stable predictors: 62 | Avg selected/fit: 120.4 | p = 23616 OG_17751: PFER <= 2.312 | Stable predictors: 34 | Avg selected/fit: 104.5 | p = 23616 OG_17800: PFER <= 2.121 | Stable predictors: 41 | Avg selected/fit: 100.1 | p = 23616 OG_17806: PFER <= 1.963 | Stable predictors: 22 | Avg selected/fit: 96.3 | p = 23616 OG_17849: PFER <= 2.058 | Stable predictors: 23 | Avg selected/fit: 98.6 | p = 23616 OG_18058: PFER <= 1.707 | Stable predictors: 44 | Avg selected/fit: 89.8 | p = 23616 OG_18159: PFER <= 2.233 | Stable predictors: 42 | Avg selected/fit: 102.7 | p = 23616 OG_18223: PFER <= 3.188 | Stable predictors: 60 | Avg selected/fit: 122.7 | p = 23616 OG_18258: PFER <= 2.242 | Stable predictors: 33 | Avg selected/fit: 102.9 | p = 23616 === Stable Predictor Summary (Selection Prob >= 60%) === Number of stable predictor-gene associations: 6916 Number of genes with at least one stable predictor: 172 Breakdown of EVER-SELECTED predictors by type: # A tibble: 3 × 4 Predictor_Type N_ever_selected N_stable Pct_stable 1 Other 23570 841 3.57 2 lncRNA 51953 6069 11.7 3 miRNA 135 6 4.44 Breakdown of STABLE predictors by type: # A tibble: 3 × 5 Predictor_Type N_stable N_unique_predictors N_genes_affected 1 Other 841 766 161 2 lncRNA 6069 3557 172 3 miRNA 6 5 4 # ℹ 1 more variable: Mean_Selection_Prob === PART 3: Generating Visualizations and Output Files === Creating summary plots... Creating per-gene stability selection plots... Saving stability selection results... Enhancing top predictors table with stability information... === PART 3.5: Stacked Bar Charts and Heatmaps === Building composition data (with significance filtering)... Building composition data (without significance filtering)... Generating stacked bar charts (stable predictors only)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_alphabetical_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_predominant_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_r2_sig.png Generating stacked bar charts (all non-zero predictors)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_alphabetical_nosig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_predominant_nosig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_stacked_bar_r2_nosig.png Generating predictor-type heatmaps... Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_predictor_heatmap_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/Ptuh_predictor_heatmap_nosig.png === ANALYSIS COMPLETE === End time:2026-03-04 22:13:57.173769 Timing summary: - Bootstrapping: 9.15 minutes - Stability selection: 1.70 minutes - Total runtime: 10.85 minutes Output files generated: 1. Ptuh_stabsel_results_full.csv - All predictor-gene selection probabilities 2. Ptuh_stable_predictors.csv - Stable associations only (>= 60%) 3. Ptuh_gene_summary.csv - Summary per gene 4. Ptuh_predictor_type_summary.csv - Summary by predictor type 5. Ptuh_top_predictors_with_stability.csv - Top predictors with stability info 6. Ptuh_R2_round1*.png - R² per gene with error bars (all genes) 7. Ptuh_R2_round2*.png - R² per gene with error bars (well-predicted) 8. Ptuh_stacked_bar_*.png - Predictor type composition (3 orderings x 2 versions) 9. Ptuh_predictor_heatmap_*.png - Predictor type heatmap (2 versions) 10. Various per-gene plots in ../output/26.6-ElasticNet-stability-selection-2/Ptuh/ Key statistics: - Total genes analyzed: 447 - Well-predicted genes (R2 > 0.50): 172 - Stable predictor-gene associations: 6916 - Bootstrap replicates: 10 (round 1), 10 (round 2) - Subsample iterations: 10 (producing 20 complementary-pair fits) - Selection threshold: 60% - Cores used: 10 Statistical notes: - Predictor reliability assessed via stability selection (Meinshausen & Bühlmann, 2010; Shah & Samworth, 2013) - Error control: PFER bound (expected false selections per gene) - Selection threshold: pi = 0.60 - No separate multiple testing correction needed (PFER bound is simultaneous) Log file saved to: ../output/26.6-ElasticNet-stability-selection-2/Ptuh/ElasticNet_stabsel_log_20260304_220016.txt === END OF SCRIPT === === Log closed: 2026-03-04 22:13:57.184003 ===