=== Log started: 2026-03-04 23:44:42.932754 === 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 23:44:42.968018 Defining model functions Loading gene counts Loading miRNA counts Loading lncRNA counts Loading WGBS data Loading metadata table Species prefix and code:ACR,Apul WGBS column name handling Filtering datasets filtering low count features Ensuring integer counts Filtered dimensions: Genes: 469 x 39 miRNA: 48 x 39 lncRNA: 15553 x 39 WGBS: 14228 x 39 Ensuring all dfs have identical column names and orders Applying variance stabilization Predictor set dimensions:39 Predictor set dimensions:29829 Gene set dimensions:39 Gene set dimensions:469 === Data Validation and Cleaning === Predictor matrix: 39 samples x 29829 features Columns with NaN: 0 Columns with Inf: 0 Columns with NA: 0 Constant columns (sd = 0): 0 Total problematic columns: 0 Response matrix: 39 samples x 469 genes Genes with NaN/Inf/NA/constant values: 0 Samples available: 39 Cleaned data dimensions: Predictors: 39 samples x 29829 features Genes: 39 samples x 469 genes Pre-flight test: fitting one gene sequentially to verify data compatibility... Pre-flight passed: gene 'OG_13910', R2 = 0.8423, 46 non-zero coefficients === Data validation complete === === PART 1: Elastic Net with Bootstrapped Train/Test Splits === --- Round 1: 10 bootstrap replicates across all 469 genes --- Round1: Running 10 replicates across 10 cores... Round1: 0/10 (0.0%) Round1: 10/10 (100.0%) Round 1 results: 218/469 genes with mean R2 >= 0.50 --- Round 2: 10 bootstrap replicates on 218 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. 0.2868 0.5581 0.6875 0.6685 0.7719 0.9557 Bootstrapping completed in 8.97 minutes === PART 1.5: R-squared Bootstrap Plots === Plotting Round 1 R² (all genes)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_R2_round1-allgenes.png Plotting Round 2 R² (well-predicted genes)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_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 218 well-predicted genes Calculating stability selection for 218 genes with 10 subsample iterations each Total predictors per gene: 29829 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 218 genes... StabSel: 0/218 (0.0%) StabSel: 218/218 (100.0%) === Stability Selection Summary === Total predictor-gene combinations: 6502722 Predictors ever selected (in at least 1 fit): 94257 (1.45%) Average predictors with any selection per gene: 432.4 Subsample iterations per gene: 10 (producing 20 complementary-pair fits) Stability selection completed in 2.37 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_00171: PFER <= 1.845 | Stable predictors: 64 | Avg selected/fit: 104.9 | p = 29829 OG_00297: PFER <= 1.494 | Stable predictors: 42 | Avg selected/fit: 94.4 | p = 29829 OG_00430: PFER <= 1.616 | Stable predictors: 38 | Avg selected/fit: 98.2 | p = 29829 OG_00460: PFER <= 1.355 | Stable predictors: 40 | Avg selected/fit: 89.9 | p = 29829 OG_00643: PFER <= 1.988 | Stable predictors: 66 | Avg selected/fit: 108.9 | p = 29829 OG_00807: PFER <= 1.422 | Stable predictors: 37 | Avg selected/fit: 92.1 | p = 29829 OG_00843: PFER <= 0.737 | Stable predictors: 34 | Avg selected/fit: 66.3 | p = 29829 OG_01023: PFER <= 2.552 | Stable predictors: 52 | Avg selected/fit: 123.4 | p = 29829 OG_01117: PFER <= 2.065 | Stable predictors: 49 | Avg selected/fit: 111.0 | p = 29829 OG_01147: PFER <= 1.316 | Stable predictors: 50 | Avg selected/fit: 88.6 | p = 29829 OG_01155: PFER <= 1.852 | Stable predictors: 55 | Avg selected/fit: 105.1 | p = 29829 OG_01277: PFER <= 1.620 | Stable predictors: 36 | Avg selected/fit: 98.3 | p = 29829 OG_01317: PFER <= 2.487 | Stable predictors: 62 | Avg selected/fit: 121.8 | p = 29829 OG_01354: PFER <= 0.908 | Stable predictors: 20 | Avg selected/fit: 73.6 | p = 29829 OG_01452: PFER <= 2.228 | Stable predictors: 71 | Avg selected/fit: 115.3 | p = 29829 OG_01475: PFER <= 1.670 | Stable predictors: 38 | Avg selected/fit: 99.8 | p = 29829 OG_01486: PFER <= 1.355 | Stable predictors: 40 | Avg selected/fit: 89.9 | p = 29829 OG_01489: PFER <= 1.453 | Stable predictors: 43 | Avg selected/fit: 93.1 | p = 29829 OG_01611: PFER <= 1.200 | Stable predictors: 30 | Avg selected/fit: 84.6 | p = 29829 OG_01630: PFER <= 1.717 | Stable predictors: 42 | Avg selected/fit: 101.2 | p = 29829 OG_01724: PFER <= 1.280 | Stable predictors: 42 | Avg selected/fit: 87.4 | p = 29829 OG_01727: PFER <= 1.792 | Stable predictors: 50 | Avg selected/fit: 103.4 | p = 29829 OG_01747: PFER <= 1.670 | Stable predictors: 43 | Avg selected/fit: 99.8 | p = 29829 OG_01758: PFER <= 2.054 | Stable predictors: 51 | Avg selected/fit: 110.7 | p = 29829 OG_01981: PFER <= 0.746 | Stable predictors: 26 | Avg selected/fit: 66.7 | p = 29829 OG_02012: PFER <= 1.217 | Stable predictors: 36 | Avg selected/fit: 85.2 | p = 29829 OG_02070: PFER <= 2.221 | Stable predictors: 41 | Avg selected/fit: 115.1 | p = 29829 OG_02088: PFER <= 1.796 | Stable predictors: 44 | Avg selected/fit: 103.5 | p = 29829 OG_02090: PFER <= 1.841 | Stable predictors: 38 | Avg selected/fit: 104.8 | p = 29829 OG_02102: PFER <= 2.507 | Stable predictors: 44 | Avg selected/fit: 122.3 | p = 29829 OG_02124: PFER <= 2.217 | Stable predictors: 50 | Avg selected/fit: 115.0 | p = 29829 OG_02146: PFER <= 1.966 | Stable predictors: 56 | Avg selected/fit: 108.3 | p = 29829 OG_02163: PFER <= 2.036 | Stable predictors: 55 | Avg selected/fit: 110.2 | p = 29829 OG_02190: PFER <= 1.620 | Stable predictors: 43 | Avg selected/fit: 98.3 | p = 29829 OG_02220: PFER <= 1.275 | Stable predictors: 30 | Avg selected/fit: 87.2 | p = 29829 OG_02317: PFER <= 1.545 | Stable predictors: 56 | Avg selected/fit: 96.0 | p = 29829 OG_02358: PFER <= 1.497 | Stable predictors: 36 | Avg selected/fit: 94.5 | p = 29829 OG_02408: PFER <= 1.816 | Stable predictors: 56 | Avg selected/fit: 104.1 | p = 29829 OG_02666: PFER <= 2.099 | Stable predictors: 53 | Avg selected/fit: 111.9 | p = 29829 OG_02690: PFER <= 1.723 | Stable predictors: 39 | Avg selected/fit: 101.4 | p = 29829 OG_03267: PFER <= 2.099 | Stable predictors: 60 | Avg selected/fit: 111.9 | p = 29829 OG_03268: PFER <= 1.127 | Stable predictors: 37 | Avg selected/fit: 82.0 | p = 29829 OG_03269: PFER <= 1.905 | Stable predictors: 55 | Avg selected/fit: 106.6 | p = 29829 OG_03270: PFER <= 1.397 | Stable predictors: 25 | Avg selected/fit: 91.3 | p = 29829 OG_03288: PFER <= 1.529 | Stable predictors: 39 | Avg selected/fit: 95.5 | p = 29829 OG_03298: PFER <= 1.941 | Stable predictors: 54 | Avg selected/fit: 107.6 | p = 29829 OG_03330: PFER <= 1.400 | Stable predictors: 38 | Avg selected/fit: 91.4 | p = 29829 OG_03429: PFER <= 1.532 | Stable predictors: 29 | Avg selected/fit: 95.6 | p = 29829 OG_03451: PFER <= 2.125 | Stable predictors: 56 | Avg selected/fit: 112.6 | p = 29829 OG_03484: PFER <= 2.099 | Stable predictors: 41 | Avg selected/fit: 111.9 | p = 29829 OG_03628: PFER <= 1.737 | Stable predictors: 34 | Avg selected/fit: 101.8 | p = 29829 OG_03671: PFER <= 0.918 | Stable predictors: 24 | Avg selected/fit: 74.0 | p = 29829 OG_03673: PFER <= 1.108 | Stable predictors: 25 | Avg selected/fit: 81.3 | p = 29829 OG_03780: PFER <= 3.114 | Stable predictors: 73 | Avg selected/fit: 136.3 | p = 29829 OG_04115: PFER <= 1.966 | Stable predictors: 51 | Avg selected/fit: 108.3 | p = 29829 OG_04238: PFER <= 2.194 | Stable predictors: 51 | Avg selected/fit: 114.4 | p = 29829 OG_04247: PFER <= 1.237 | Stable predictors: 36 | Avg selected/fit: 85.9 | p = 29829 OG_04331: PFER <= 1.434 | Stable predictors: 30 | Avg selected/fit: 92.5 | p = 29829 OG_04514: PFER <= 1.400 | Stable predictors: 32 | Avg selected/fit: 91.4 | p = 29829 OG_04538: PFER <= 1.248 | Stable predictors: 44 | Avg selected/fit: 86.3 | p = 29829 OG_04547: PFER <= 0.945 | Stable predictors: 27 | Avg selected/fit: 75.1 | p = 29829 OG_04560: PFER <= 0.773 | Stable predictors: 17 | Avg selected/fit: 67.9 | p = 29829 OG_04651: PFER <= 1.337 | Stable predictors: 40 | Avg selected/fit: 89.3 | p = 29829 OG_04694: PFER <= 1.848 | Stable predictors: 48 | Avg selected/fit: 105.0 | p = 29829 OG_04723: PFER <= 1.522 | Stable predictors: 26 | Avg selected/fit: 95.3 | p = 29829 OG_05010: PFER <= 2.118 | Stable predictors: 47 | Avg selected/fit: 112.4 | p = 29829 OG_05121: PFER <= 1.410 | Stable predictors: 40 | Avg selected/fit: 91.7 | p = 29829 OG_05210: PFER <= 2.002 | Stable predictors: 58 | Avg selected/fit: 109.3 | p = 29829 OG_05250: PFER <= 1.768 | Stable predictors: 52 | Avg selected/fit: 102.7 | p = 29829 OG_05303: PFER <= 1.894 | Stable predictors: 40 | Avg selected/fit: 106.3 | p = 29829 OG_05366: PFER <= 1.730 | Stable predictors: 47 | Avg selected/fit: 101.6 | p = 29829 OG_05498: PFER <= 1.995 | Stable predictors: 43 | Avg selected/fit: 109.1 | p = 29829 OG_05514: PFER <= 1.782 | Stable predictors: 39 | Avg selected/fit: 103.1 | p = 29829 OG_05637: PFER <= 1.355 | Stable predictors: 33 | Avg selected/fit: 89.9 | p = 29829 OG_05952: PFER <= 1.183 | Stable predictors: 31 | Avg selected/fit: 84.0 | p = 29829 OG_06004: PFER <= 1.551 | Stable predictors: 30 | Avg selected/fit: 96.2 | p = 29829 OG_06119: PFER <= 1.796 | Stable predictors: 36 | Avg selected/fit: 103.5 | p = 29829 OG_06181: PFER <= 1.535 | Stable predictors: 36 | Avg selected/fit: 95.7 | p = 29829 OG_06198: PFER <= 2.095 | Stable predictors: 55 | Avg selected/fit: 111.8 | p = 29829 OG_06279: PFER <= 1.999 | Stable predictors: 55 | Avg selected/fit: 109.2 | p = 29829 OG_06592: PFER <= 1.587 | Stable predictors: 47 | Avg selected/fit: 97.3 | p = 29829 OG_06599: PFER <= 1.916 | Stable predictors: 48 | Avg selected/fit: 106.9 | p = 29829 OG_06655: PFER <= 1.316 | Stable predictors: 33 | Avg selected/fit: 88.6 | p = 29829 OG_06670: PFER <= 1.519 | Stable predictors: 41 | Avg selected/fit: 95.2 | p = 29829 OG_06875: PFER <= 2.366 | Stable predictors: 71 | Avg selected/fit: 118.8 | p = 29829 OG_06931: PFER <= 1.859 | Stable predictors: 57 | Avg selected/fit: 105.3 | p = 29829 OG_06955: PFER <= 1.456 | Stable predictors: 35 | Avg selected/fit: 93.2 | p = 29829 OG_06998: PFER <= 1.751 | Stable predictors: 48 | Avg selected/fit: 102.2 | p = 29829 OG_07000: PFER <= 1.301 | Stable predictors: 36 | Avg selected/fit: 88.1 | p = 29829 OG_07042: PFER <= 1.977 | Stable predictors: 55 | Avg selected/fit: 108.6 | p = 29829 OG_07150: PFER <= 1.937 | Stable predictors: 40 | Avg selected/fit: 107.5 | p = 29829 OG_07330: PFER <= 2.099 | Stable predictors: 33 | Avg selected/fit: 111.9 | p = 29829 OG_07331: PFER <= 1.959 | Stable predictors: 31 | Avg selected/fit: 108.1 | p = 29829 OG_07394: PFER <= 2.036 | Stable predictors: 50 | Avg selected/fit: 110.2 | p = 29829 OG_07404: PFER <= 2.021 | Stable predictors: 51 | Avg selected/fit: 109.8 | p = 29829 OG_07508: PFER <= 1.999 | Stable predictors: 55 | Avg selected/fit: 109.2 | p = 29829 OG_07641: PFER <= 1.222 | Stable predictors: 31 | Avg selected/fit: 85.4 | p = 29829 OG_07772: PFER <= 2.148 | Stable predictors: 41 | Avg selected/fit: 113.2 | p = 29829 OG_07892: PFER <= 2.171 | Stable predictors: 57 | Avg selected/fit: 113.8 | p = 29829 OG_07958: PFER <= 2.228 | Stable predictors: 37 | Avg selected/fit: 115.3 | p = 29829 OG_08143: PFER <= 0.923 | Stable predictors: 29 | Avg selected/fit: 74.2 | p = 29829 OG_08271: PFER <= 2.607 | Stable predictors: 69 | Avg selected/fit: 124.7 | p = 29829 OG_08347: PFER <= 2.186 | Stable predictors: 64 | Avg selected/fit: 114.2 | p = 29829 OG_08432: PFER <= 1.995 | Stable predictors: 40 | Avg selected/fit: 109.1 | p = 29829 OG_08497: PFER <= 2.244 | Stable predictors: 49 | Avg selected/fit: 115.7 | p = 29829 OG_08528: PFER <= 0.529 | Stable predictors: 5 | Avg selected/fit: 56.2 | p = 29829 OG_08545: PFER <= 1.100 | Stable predictors: 32 | Avg selected/fit: 81.0 | p = 29829 OG_08622: PFER <= 2.358 | Stable predictors: 49 | Avg selected/fit: 118.6 | p = 29829 OG_08639: PFER <= 1.765 | Stable predictors: 41 | Avg selected/fit: 102.6 | p = 29829 OG_08663: PFER <= 1.459 | Stable predictors: 37 | Avg selected/fit: 93.3 | p = 29829 OG_08856: PFER <= 1.683 | Stable predictors: 36 | Avg selected/fit: 100.2 | p = 29829 OG_08878: PFER <= 1.532 | Stable predictors: 29 | Avg selected/fit: 95.6 | p = 29829 OG_08914: PFER <= 1.012 | Stable predictors: 31 | Avg selected/fit: 77.7 | p = 29829 OG_09024: PFER <= 1.723 | Stable predictors: 62 | Avg selected/fit: 101.4 | p = 29829 OG_09106: PFER <= 1.304 | Stable predictors: 30 | Avg selected/fit: 88.2 | p = 29829 OG_09174: PFER <= 1.180 | Stable predictors: 28 | Avg selected/fit: 83.9 | p = 29829 OG_09423: PFER <= 1.848 | Stable predictors: 38 | Avg selected/fit: 105.0 | p = 29829 OG_09424: PFER <= 2.129 | Stable predictors: 46 | Avg selected/fit: 112.7 | p = 29829 OG_09455: PFER <= 2.921 | Stable predictors: 59 | Avg selected/fit: 132.0 | p = 29829 OG_09596: PFER <= 2.137 | Stable predictors: 55 | Avg selected/fit: 112.9 | p = 29829 OG_09597: PFER <= 1.325 | Stable predictors: 45 | Avg selected/fit: 88.9 | p = 29829 OG_09598: PFER <= 1.866 | Stable predictors: 35 | Avg selected/fit: 105.5 | p = 29829 OG_09657: PFER <= 1.036 | Stable predictors: 28 | Avg selected/fit: 78.6 | p = 29829 OG_09796: PFER <= 1.761 | Stable predictors: 40 | Avg selected/fit: 102.5 | p = 29829 OG_09806: PFER <= 1.030 | Stable predictors: 19 | Avg selected/fit: 78.4 | p = 29829 OG_09896: PFER <= 2.091 | Stable predictors: 58 | Avg selected/fit: 111.7 | p = 29829 OG_09942: PFER <= 1.898 | Stable predictors: 60 | Avg selected/fit: 106.4 | p = 29829 OG_09969: PFER <= 1.650 | Stable predictors: 24 | Avg selected/fit: 99.2 | p = 29829 OG_10004: PFER <= 2.691 | Stable predictors: 80 | Avg selected/fit: 126.7 | p = 29829 OG_10007: PFER <= 2.080 | Stable predictors: 53 | Avg selected/fit: 111.4 | p = 29829 OG_10008: PFER <= 1.406 | Stable predictors: 33 | Avg selected/fit: 91.6 | p = 29829 OG_10205: PFER <= 1.926 | Stable predictors: 41 | Avg selected/fit: 107.2 | p = 29829 OG_10355: PFER <= 2.820 | Stable predictors: 57 | Avg selected/fit: 129.7 | p = 29829 OG_10450: PFER <= 1.364 | Stable predictors: 34 | Avg selected/fit: 90.2 | p = 29829 OG_10475: PFER <= 2.275 | Stable predictors: 56 | Avg selected/fit: 116.5 | p = 29829 OG_10480: PFER <= 1.403 | Stable predictors: 52 | Avg selected/fit: 91.5 | p = 29829 OG_10509: PFER <= 1.516 | Stable predictors: 47 | Avg selected/fit: 95.1 | p = 29829 OG_10516: PFER <= 1.768 | Stable predictors: 45 | Avg selected/fit: 102.7 | p = 29829 OG_10523: PFER <= 2.140 | Stable predictors: 48 | Avg selected/fit: 113.0 | p = 29829 OG_10812: PFER <= 1.866 | Stable predictors: 33 | Avg selected/fit: 105.5 | p = 29829 OG_10818: PFER <= 1.382 | Stable predictors: 35 | Avg selected/fit: 90.8 | p = 29829 OG_11038: PFER <= 1.269 | Stable predictors: 33 | Avg selected/fit: 87.0 | p = 29829 OG_11074: PFER <= 1.444 | Stable predictors: 32 | Avg selected/fit: 92.8 | p = 29829 OG_11156: PFER <= 2.103 | Stable predictors: 39 | Avg selected/fit: 112.0 | p = 29829 OG_11234: PFER <= 2.017 | Stable predictors: 46 | Avg selected/fit: 109.7 | p = 29829 OG_11307: PFER <= 1.587 | Stable predictors: 32 | Avg selected/fit: 97.3 | p = 29829 OG_11360: PFER <= 1.469 | Stable predictors: 29 | Avg selected/fit: 93.6 | p = 29829 OG_11365: PFER <= 0.881 | Stable predictors: 35 | Avg selected/fit: 72.5 | p = 29829 OG_11379: PFER <= 0.674 | Stable predictors: 22 | Avg selected/fit: 63.4 | p = 29829 OG_11381: PFER <= 1.522 | Stable predictors: 28 | Avg selected/fit: 95.3 | p = 29829 OG_11419: PFER <= 2.263 | Stable predictors: 46 | Avg selected/fit: 116.2 | p = 29829 OG_11429: PFER <= 2.065 | Stable predictors: 41 | Avg selected/fit: 111.0 | p = 29829 OG_11794: PFER <= 1.782 | Stable predictors: 51 | Avg selected/fit: 103.1 | p = 29829 OG_11812: PFER <= 1.422 | Stable predictors: 27 | Avg selected/fit: 92.1 | p = 29829 OG_11813: PFER <= 1.188 | Stable predictors: 34 | Avg selected/fit: 84.2 | p = 29829 OG_11831: PFER <= 2.175 | Stable predictors: 68 | Avg selected/fit: 113.9 | p = 29829 OG_11839: PFER <= 1.535 | Stable predictors: 27 | Avg selected/fit: 95.7 | p = 29829 OG_11852: PFER <= 1.730 | Stable predictors: 30 | Avg selected/fit: 101.6 | p = 29829 OG_11870: PFER <= 2.225 | Stable predictors: 50 | Avg selected/fit: 115.2 | p = 29829 OG_11957: PFER <= 1.141 | Stable predictors: 33 | Avg selected/fit: 82.5 | p = 29829 OG_11964: PFER <= 2.152 | Stable predictors: 50 | Avg selected/fit: 113.3 | p = 29829 OG_11984: PFER <= 1.806 | Stable predictors: 37 | Avg selected/fit: 103.8 | p = 29829 OG_12046: PFER <= 1.373 | Stable predictors: 41 | Avg selected/fit: 90.5 | p = 29829 OG_12050: PFER <= 1.887 | Stable predictors: 45 | Avg selected/fit: 106.1 | p = 29829 OG_12107: PFER <= 1.564 | Stable predictors: 45 | Avg selected/fit: 96.6 | p = 29829 OG_12125: PFER <= 1.785 | Stable predictors: 57 | Avg selected/fit: 103.2 | p = 29829 OG_12165: PFER <= 1.908 | Stable predictors: 40 | Avg selected/fit: 106.7 | p = 29829 OG_12320: PFER <= 1.319 | Stable predictors: 27 | Avg selected/fit: 88.7 | p = 29829 OG_12331: PFER <= 1.413 | Stable predictors: 31 | Avg selected/fit: 91.8 | p = 29829 OG_12369: PFER <= 0.968 | Stable predictors: 30 | Avg selected/fit: 76.0 | p = 29829 OG_12371: PFER <= 1.105 | Stable predictors: 32 | Avg selected/fit: 81.2 | p = 29829 OG_12496: PFER <= 1.158 | Stable predictors: 40 | Avg selected/fit: 83.1 | p = 29829 OG_12518: PFER <= 1.376 | Stable predictors: 24 | Avg selected/fit: 90.6 | p = 29829 OG_12568: PFER <= 1.813 | Stable predictors: 47 | Avg selected/fit: 104.0 | p = 29829 OG_12611: PFER <= 1.891 | Stable predictors: 48 | Avg selected/fit: 106.2 | p = 29829 OG_12612: PFER <= 1.916 | Stable predictors: 20 | Avg selected/fit: 106.9 | p = 29829 OG_12616: PFER <= 2.039 | Stable predictors: 45 | Avg selected/fit: 110.3 | p = 29829 OG_12785: PFER <= 2.594 | Stable predictors: 54 | Avg selected/fit: 124.4 | p = 29829 OG_12878: PFER <= 1.538 | Stable predictors: 43 | Avg selected/fit: 95.8 | p = 29829 OG_12880: PFER <= 1.191 | Stable predictors: 37 | Avg selected/fit: 84.3 | p = 29829 OG_12888: PFER <= 1.955 | Stable predictors: 42 | Avg selected/fit: 108.0 | p = 29829 OG_12990: PFER <= 0.460 | Stable predictors: 13 | Avg selected/fit: 52.4 | p = 29829 OG_13050: PFER <= 2.118 | Stable predictors: 48 | Avg selected/fit: 112.4 | p = 29829 OG_13066: PFER <= 1.301 | Stable predictors: 32 | Avg selected/fit: 88.1 | p = 29829 OG_13091: PFER <= 1.981 | Stable predictors: 43 | Avg selected/fit: 108.7 | p = 29829 OG_13153: PFER <= 1.497 | Stable predictors: 27 | Avg selected/fit: 94.5 | p = 29829 OG_13154: PFER <= 1.663 | Stable predictors: 42 | Avg selected/fit: 99.6 | p = 29829 OG_13191: PFER <= 1.519 | Stable predictors: 40 | Avg selected/fit: 95.2 | p = 29829 OG_13197: PFER <= 2.442 | Stable predictors: 64 | Avg selected/fit: 120.7 | p = 29829 OG_13201: PFER <= 2.302 | Stable predictors: 63 | Avg selected/fit: 117.2 | p = 29829 OG_13465: PFER <= 1.456 | Stable predictors: 40 | Avg selected/fit: 93.2 | p = 29829 OG_13484: PFER <= 2.058 | Stable predictors: 55 | Avg selected/fit: 110.8 | p = 29829 OG_13792: PFER <= 2.594 | Stable predictors: 65 | Avg selected/fit: 124.4 | p = 29829 OG_13886: PFER <= 1.656 | Stable predictors: 44 | Avg selected/fit: 99.4 | p = 29829 OG_14037: PFER <= 1.529 | Stable predictors: 36 | Avg selected/fit: 95.5 | p = 29829 OG_14038: PFER <= 2.159 | Stable predictors: 62 | Avg selected/fit: 113.5 | p = 29829 OG_14066: PFER <= 1.771 | Stable predictors: 38 | Avg selected/fit: 102.8 | p = 29829 OG_14276: PFER <= 1.696 | Stable predictors: 34 | Avg selected/fit: 100.6 | p = 29829 OG_14381: PFER <= 0.509 | Stable predictors: 12 | Avg selected/fit: 55.1 | p = 29829 OG_14382: PFER <= 1.734 | Stable predictors: 52 | Avg selected/fit: 101.7 | p = 29829 OG_14407: PFER <= 1.789 | Stable predictors: 52 | Avg selected/fit: 103.3 | p = 29829 OG_14505: PFER <= 1.901 | Stable predictors: 60 | Avg selected/fit: 106.5 | p = 29829 OG_14540: PFER <= 1.376 | Stable predictors: 26 | Avg selected/fit: 90.6 | p = 29829 OG_14563: PFER <= 1.043 | Stable predictors: 34 | Avg selected/fit: 78.9 | p = 29829 OG_14588: PFER <= 1.251 | Stable predictors: 34 | Avg selected/fit: 86.4 | p = 29829 OG_14615: PFER <= 1.751 | Stable predictors: 34 | Avg selected/fit: 102.2 | p = 29829 OG_14623: PFER <= 2.544 | Stable predictors: 72 | Avg selected/fit: 123.2 | p = 29829 OG_14626: PFER <= 2.118 | Stable predictors: 59 | Avg selected/fit: 112.4 | p = 29829 OG_14685: PFER <= 2.548 | Stable predictors: 54 | Avg selected/fit: 123.3 | p = 29829 OG_14710: PFER <= 1.388 | Stable predictors: 33 | Avg selected/fit: 91.0 | p = 29829 OG_14804: PFER <= 1.741 | Stable predictors: 57 | Avg selected/fit: 101.9 | p = 29829 OG_15070: PFER <= 1.580 | Stable predictors: 31 | Avg selected/fit: 97.1 | p = 29829 OG_15086: PFER <= 0.966 | Stable predictors: 24 | Avg selected/fit: 75.9 | p = 29829 OG_15137: PFER <= 0.945 | Stable predictors: 29 | Avg selected/fit: 75.1 | p = 29829 OG_15208: PFER <= 0.903 | Stable predictors: 20 | Avg selected/fit: 73.4 | p = 29829 OG_15303: PFER <= 3.423 | Stable predictors: 95 | Avg selected/fit: 142.9 | p = 29829 OG_15375: PFER <= 1.522 | Stable predictors: 26 | Avg selected/fit: 95.3 | p = 29829 OG_15544: PFER <= 1.506 | Stable predictors: 36 | Avg selected/fit: 94.8 | p = 29829 === Stable Predictor Summary (Selection Prob >= 60%) === Number of stable predictor-gene associations: 9205 Number of genes with at least one stable predictor: 218 Breakdown of EVER-SELECTED predictors by type: # A tibble: 3 × 4 Predictor_Type N_ever_selected N_stable Pct_stable 1 Gene Body Meth 25495 1014 3.98 2 lncRNA 68518 8159 11.9 3 miRNA 244 32 13.1 Breakdown of STABLE predictors by type: # A tibble: 3 × 5 Predictor_Type N_stable N_unique_predictors N_genes_affected 1 Gene Body Meth 1014 906 190 2 lncRNA 8159 4806 218 3 miRNA 32 22 26 # 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/Apul/Apul_stacked_bar_alphabetical_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_stacked_bar_predominant_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_stacked_bar_r2_sig.png Generating stacked bar charts (all non-zero predictors)... Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_stacked_bar_alphabetical_nosig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_stacked_bar_predominant_nosig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_stacked_bar_r2_nosig.png Generating predictor-type heatmaps... Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_predictor_heatmap_sig.png Saved: ../output/26.6-ElasticNet-stability-selection-2/Apul/Apul_predictor_heatmap_nosig.png === ANALYSIS COMPLETE === End time:2026-03-04 23:59:45.827971 Timing summary: - Bootstrapping: 8.97 minutes - Stability selection: 2.37 minutes - Total runtime: 11.34 minutes Output files generated: 1. Apul_stabsel_results_full.csv - All predictor-gene selection probabilities 2. Apul_stable_predictors.csv - Stable associations only (>= 60%) 3. Apul_gene_summary.csv - Summary per gene 4. Apul_predictor_type_summary.csv - Summary by predictor type 5. Apul_top_predictors_with_stability.csv - Top predictors with stability info 6. Apul_R2_round1*.png - R² per gene with error bars (all genes) 7. Apul_R2_round2*.png - R² per gene with error bars (well-predicted) 8. Apul_stacked_bar_*.png - Predictor type composition (3 orderings x 2 versions) 9. Apul_predictor_heatmap_*.png - Predictor type heatmap (2 versions) 10. Various per-gene plots in ../output/26.6-ElasticNet-stability-selection-2/Apul/ Key statistics: - Total genes analyzed: 469 - Well-predicted genes (R2 > 0.50): 218 - Stable predictor-gene associations: 9205 - 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/Apul/ElasticNet_stabsel_log_20260304_234442.txt === END OF SCRIPT === === Log closed: 2026-03-04 23:59:45.838004 ===