=== Log started: 2026-03-05 00:55:46.839412 === 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:2026-03-05 00:55:46.875084 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 38.32 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 200 subsample iterations each Total predictors per gene: 29829 Using 10 core(s) for gene-level parallelization Complementary pairs: each iteration produces 2 fits (400 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): 342599 (5.27%) Average predictors with any selection per gene: 1571.6 Subsample iterations per gene: 200 (producing 400 complementary-pair fits) Stability selection completed in 151.64 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 <= 1.947 | Stable predictors: 53 | Avg selected/fit: 107.8 | p = 29829 OG_00297: PFER <= 1.737 | Stable predictors: 34 | Avg selected/fit: 101.8 | p = 29829 OG_00430: PFER <= 1.608 | Stable predictors: 31 | Avg selected/fit: 97.9 | p = 29829 OG_00460: PFER <= 1.616 | Stable predictors: 25 | Avg selected/fit: 98.2 | p = 29829 OG_00643: PFER <= 1.954 | Stable predictors: 48 | Avg selected/fit: 108.0 | p = 29829 OG_00807: PFER <= 1.141 | Stable predictors: 20 | Avg selected/fit: 82.5 | p = 29829 OG_00843: PFER <= 1.097 | Stable predictors: 28 | Avg selected/fit: 80.9 | p = 29829 OG_01023: PFER <= 2.431 | Stable predictors: 40 | Avg selected/fit: 120.4 | p = 29829 OG_01117: PFER <= 2.329 | Stable predictors: 38 | Avg selected/fit: 117.9 | p = 29829 OG_01147: PFER <= 1.397 | Stable predictors: 38 | Avg selected/fit: 91.3 | p = 29829 OG_01155: PFER <= 1.648 | Stable predictors: 28 | Avg selected/fit: 99.1 | p = 29829 OG_01277: PFER <= 1.714 | Stable predictors: 33 | Avg selected/fit: 101.1 | p = 29829 OG_01317: PFER <= 2.324 | Stable predictors: 49 | Avg selected/fit: 117.7 | p = 29829 OG_01354: PFER <= 0.715 | Stable predictors: 16 | Avg selected/fit: 65.3 | p = 29829 OG_01452: PFER <= 2.202 | Stable predictors: 46 | Avg selected/fit: 114.6 | p = 29829 OG_01475: PFER <= 1.303 | Stable predictors: 27 | Avg selected/fit: 88.2 | p = 29829 OG_01486: PFER <= 1.523 | Stable predictors: 34 | Avg selected/fit: 95.3 | p = 29829 OG_01489: PFER <= 1.566 | Stable predictors: 26 | Avg selected/fit: 96.7 | p = 29829 OG_01611: PFER <= 0.945 | Stable predictors: 27 | Avg selected/fit: 75.1 | p = 29829 OG_01630: PFER <= 1.611 | Stable predictors: 36 | Avg selected/fit: 98.0 | p = 29829 OG_01724: PFER <= 1.199 | Stable predictors: 26 | Avg selected/fit: 84.6 | p = 29829 OG_01727: PFER <= 1.770 | Stable predictors: 28 | Avg selected/fit: 102.8 | p = 29829 OG_01747: PFER <= 1.751 | Stable predictors: 33 | Avg selected/fit: 102.2 | p = 29829 OG_01758: PFER <= 1.866 | Stable predictors: 36 | Avg selected/fit: 105.5 | p = 29829 OG_01981: PFER <= 0.490 | Stable predictors: 10 | Avg selected/fit: 54.1 | p = 29829 OG_02012: PFER <= 1.107 | Stable predictors: 29 | Avg selected/fit: 81.3 | p = 29829 OG_02070: PFER <= 1.943 | Stable predictors: 29 | Avg selected/fit: 107.7 | p = 29829 OG_02088: PFER <= 2.018 | Stable predictors: 33 | Avg selected/fit: 109.7 | p = 29829 OG_02090: PFER <= 1.875 | Stable predictors: 24 | Avg selected/fit: 105.8 | p = 29829 OG_02102: PFER <= 2.444 | Stable predictors: 37 | Avg selected/fit: 120.7 | p = 29829 OG_02124: PFER <= 2.151 | Stable predictors: 44 | Avg selected/fit: 113.3 | p = 29829 OG_02146: PFER <= 1.972 | Stable predictors: 48 | Avg selected/fit: 108.5 | p = 29829 OG_02163: PFER <= 1.909 | Stable predictors: 42 | Avg selected/fit: 106.7 | p = 29829 OG_02190: PFER <= 1.455 | Stable predictors: 39 | Avg selected/fit: 93.2 | p = 29829 OG_02220: PFER <= 1.265 | Stable predictors: 21 | Avg selected/fit: 86.9 | p = 29829 OG_02317: PFER <= 1.771 | Stable predictors: 38 | Avg selected/fit: 102.8 | p = 29829 OG_02358: PFER <= 1.438 | Stable predictors: 29 | Avg selected/fit: 92.6 | p = 29829 OG_02408: PFER <= 1.683 | Stable predictors: 41 | Avg selected/fit: 100.2 | p = 29829 OG_02666: PFER <= 1.983 | Stable predictors: 35 | Avg selected/fit: 108.8 | p = 29829 OG_02690: PFER <= 2.035 | Stable predictors: 26 | Avg selected/fit: 110.2 | p = 29829 OG_03267: PFER <= 2.011 | Stable predictors: 41 | Avg selected/fit: 109.5 | p = 29829 OG_03268: PFER <= 1.096 | Stable predictors: 29 | Avg selected/fit: 80.8 | p = 29829 OG_03269: PFER <= 1.966 | Stable predictors: 46 | Avg selected/fit: 108.3 | p = 29829 OG_03270: PFER <= 1.452 | Stable predictors: 25 | Avg selected/fit: 93.1 | p = 29829 OG_03288: PFER <= 1.397 | Stable predictors: 31 | Avg selected/fit: 91.3 | p = 29829 OG_03298: PFER <= 1.912 | Stable predictors: 40 | Avg selected/fit: 106.8 | p = 29829 OG_03330: PFER <= 1.401 | Stable predictors: 30 | Avg selected/fit: 91.4 | p = 29829 OG_03429: PFER <= 1.483 | Stable predictors: 28 | Avg selected/fit: 94.1 | p = 29829 OG_03451: PFER <= 2.006 | Stable predictors: 42 | Avg selected/fit: 109.4 | p = 29829 OG_03484: PFER <= 1.814 | Stable predictors: 28 | Avg selected/fit: 104.0 | p = 29829 OG_03628: PFER <= 1.673 | Stable predictors: 35 | Avg selected/fit: 99.9 | p = 29829 OG_03671: PFER <= 0.941 | Stable predictors: 15 | Avg selected/fit: 74.9 | p = 29829 OG_03673: PFER <= 1.125 | Stable predictors: 19 | Avg selected/fit: 81.9 | p = 29829 OG_03780: PFER <= 2.839 | Stable predictors: 48 | Avg selected/fit: 130.1 | p = 29829 OG_04115: PFER <= 1.941 | Stable predictors: 43 | Avg selected/fit: 107.6 | p = 29829 OG_04238: PFER <= 2.244 | Stable predictors: 39 | Avg selected/fit: 115.7 | p = 29829 OG_04247: PFER <= 1.208 | Stable predictors: 23 | Avg selected/fit: 84.9 | p = 29829 OG_04331: PFER <= 1.400 | Stable predictors: 26 | Avg selected/fit: 91.4 | p = 29829 OG_04514: PFER <= 1.568 | Stable predictors: 18 | Avg selected/fit: 96.7 | p = 29829 OG_04538: PFER <= 1.169 | Stable predictors: 31 | Avg selected/fit: 83.5 | p = 29829 OG_04547: PFER <= 1.373 | Stable predictors: 21 | Avg selected/fit: 90.5 | p = 29829 OG_04560: PFER <= 0.524 | Stable predictors: 11 | Avg selected/fit: 55.9 | p = 29829 OG_04651: PFER <= 1.367 | Stable predictors: 36 | Avg selected/fit: 90.3 | p = 29829 OG_04694: PFER <= 1.795 | Stable predictors: 23 | Avg selected/fit: 103.5 | p = 29829 OG_04723: PFER <= 1.715 | Stable predictors: 26 | Avg selected/fit: 101.1 | p = 29829 OG_05010: PFER <= 2.140 | Stable predictors: 40 | Avg selected/fit: 113.0 | p = 29829 OG_05121: PFER <= 1.416 | Stable predictors: 32 | Avg selected/fit: 91.9 | p = 29829 OG_05210: PFER <= 2.097 | Stable predictors: 49 | Avg selected/fit: 111.9 | p = 29829 OG_05250: PFER <= 1.688 | Stable predictors: 40 | Avg selected/fit: 100.3 | p = 29829 OG_05303: PFER <= 1.472 | Stable predictors: 34 | Avg selected/fit: 93.7 | p = 29829 OG_05366: PFER <= 1.932 | Stable predictors: 29 | Avg selected/fit: 107.3 | p = 29829 OG_05498: PFER <= 1.367 | Stable predictors: 26 | Avg selected/fit: 90.3 | p = 29829 OG_05514: PFER <= 1.735 | Stable predictors: 24 | Avg selected/fit: 101.8 | p = 29829 OG_05637: PFER <= 1.625 | Stable predictors: 32 | Avg selected/fit: 98.5 | p = 29829 OG_05952: PFER <= 1.275 | Stable predictors: 26 | Avg selected/fit: 87.2 | p = 29829 OG_06004: PFER <= 1.437 | Stable predictors: 23 | Avg selected/fit: 92.6 | p = 29829 OG_06119: PFER <= 1.762 | Stable predictors: 29 | Avg selected/fit: 102.5 | p = 29829 OG_06181: PFER <= 1.271 | Stable predictors: 15 | Avg selected/fit: 87.1 | p = 29829 OG_06198: PFER <= 1.961 | Stable predictors: 31 | Avg selected/fit: 108.2 | p = 29829 OG_06279: PFER <= 2.112 | Stable predictors: 54 | Avg selected/fit: 112.3 | p = 29829 OG_06592: PFER <= 1.593 | Stable predictors: 32 | Avg selected/fit: 97.5 | p = 29829 OG_06599: PFER <= 2.031 | Stable predictors: 35 | Avg selected/fit: 110.1 | p = 29829 OG_06655: PFER <= 1.323 | Stable predictors: 26 | Avg selected/fit: 88.8 | p = 29829 OG_06670: PFER <= 1.472 | Stable predictors: 31 | Avg selected/fit: 93.7 | p = 29829 OG_06875: PFER <= 2.323 | Stable predictors: 67 | Avg selected/fit: 117.7 | p = 29829 OG_06931: PFER <= 1.897 | Stable predictors: 38 | Avg selected/fit: 106.4 | p = 29829 OG_06955: PFER <= 1.455 | Stable predictors: 18 | Avg selected/fit: 93.2 | p = 29829 OG_06998: PFER <= 2.055 | Stable predictors: 36 | Avg selected/fit: 110.7 | p = 29829 OG_07000: PFER <= 1.271 | Stable predictors: 33 | Avg selected/fit: 87.1 | p = 29829 OG_07042: PFER <= 1.926 | Stable predictors: 36 | Avg selected/fit: 107.2 | p = 29829 OG_07150: PFER <= 1.830 | Stable predictors: 33 | Avg selected/fit: 104.5 | p = 29829 OG_07330: PFER <= 1.992 | Stable predictors: 31 | Avg selected/fit: 109.0 | p = 29829 OG_07331: PFER <= 1.846 | Stable predictors: 25 | Avg selected/fit: 105.0 | p = 29829 OG_07394: PFER <= 1.741 | Stable predictors: 39 | Avg selected/fit: 101.9 | p = 29829 OG_07404: PFER <= 1.865 | Stable predictors: 32 | Avg selected/fit: 105.5 | p = 29829 OG_07508: PFER <= 2.233 | Stable predictors: 37 | Avg selected/fit: 115.4 | p = 29829 OG_07641: PFER <= 0.964 | Stable predictors: 21 | Avg selected/fit: 75.8 | p = 29829 OG_07772: PFER <= 2.470 | Stable predictors: 37 | Avg selected/fit: 121.4 | p = 29829 OG_07892: PFER <= 2.084 | Stable predictors: 43 | Avg selected/fit: 111.5 | p = 29829 OG_07958: PFER <= 2.093 | Stable predictors: 32 | Avg selected/fit: 111.7 | p = 29829 OG_08143: PFER <= 1.128 | Stable predictors: 29 | Avg selected/fit: 82.0 | p = 29829 OG_08271: PFER <= 2.429 | Stable predictors: 47 | Avg selected/fit: 120.4 | p = 29829 OG_08347: PFER <= 2.067 | Stable predictors: 60 | Avg selected/fit: 111.0 | p = 29829 OG_08432: PFER <= 1.947 | Stable predictors: 32 | Avg selected/fit: 107.8 | p = 29829 OG_08497: PFER <= 2.063 | Stable predictors: 44 | Avg selected/fit: 110.9 | p = 29829 OG_08528: PFER <= 0.407 | Stable predictors: 3 | Avg selected/fit: 49.3 | p = 29829 OG_08545: PFER <= 1.079 | Stable predictors: 24 | Avg selected/fit: 80.2 | p = 29829 OG_08622: PFER <= 2.288 | Stable predictors: 40 | Avg selected/fit: 116.8 | p = 29829 OG_08639: PFER <= 1.619 | Stable predictors: 37 | Avg selected/fit: 98.3 | p = 29829 OG_08663: PFER <= 1.609 | Stable predictors: 25 | Avg selected/fit: 98.0 | p = 29829 OG_08856: PFER <= 1.597 | Stable predictors: 27 | Avg selected/fit: 97.6 | p = 29829 OG_08878: PFER <= 1.864 | Stable predictors: 29 | Avg selected/fit: 105.5 | p = 29829 OG_08914: PFER <= 1.246 | Stable predictors: 22 | Avg selected/fit: 86.2 | p = 29829 OG_09024: PFER <= 1.566 | Stable predictors: 46 | Avg selected/fit: 96.7 | p = 29829 OG_09106: PFER <= 1.173 | Stable predictors: 22 | Avg selected/fit: 83.7 | p = 29829 OG_09174: PFER <= 1.537 | Stable predictors: 24 | Avg selected/fit: 95.7 | p = 29829 OG_09423: PFER <= 1.638 | Stable predictors: 24 | Avg selected/fit: 98.8 | p = 29829 OG_09424: PFER <= 2.041 | Stable predictors: 32 | Avg selected/fit: 110.3 | p = 29829 OG_09455: PFER <= 2.914 | Stable predictors: 58 | Avg selected/fit: 131.9 | p = 29829 OG_09596: PFER <= 1.988 | Stable predictors: 41 | Avg selected/fit: 108.9 | p = 29829 OG_09597: PFER <= 1.686 | Stable predictors: 44 | Avg selected/fit: 100.3 | p = 29829 OG_09598: PFER <= 2.022 | Stable predictors: 36 | Avg selected/fit: 109.8 | p = 29829 OG_09657: PFER <= 1.740 | Stable predictors: 34 | Avg selected/fit: 101.9 | p = 29829 OG_09796: PFER <= 1.513 | Stable predictors: 33 | Avg selected/fit: 95.0 | p = 29829 OG_09806: PFER <= 0.991 | Stable predictors: 14 | Avg selected/fit: 76.9 | p = 29829 OG_09896: PFER <= 2.102 | Stable predictors: 48 | Avg selected/fit: 112.0 | p = 29829 OG_09942: PFER <= 2.008 | Stable predictors: 54 | Avg selected/fit: 109.4 | p = 29829 OG_09969: PFER <= 2.098 | Stable predictors: 24 | Avg selected/fit: 111.9 | p = 29829 OG_10004: PFER <= 2.627 | Stable predictors: 58 | Avg selected/fit: 125.2 | p = 29829 OG_10007: PFER <= 2.179 | Stable predictors: 42 | Avg selected/fit: 114.0 | p = 29829 OG_10008: PFER <= 1.455 | Stable predictors: 27 | Avg selected/fit: 93.2 | p = 29829 OG_10205: PFER <= 1.611 | Stable predictors: 25 | Avg selected/fit: 98.0 | p = 29829 OG_10355: PFER <= 2.594 | Stable predictors: 42 | Avg selected/fit: 124.4 | p = 29829 OG_10450: PFER <= 1.454 | Stable predictors: 27 | Avg selected/fit: 93.1 | p = 29829 OG_10475: PFER <= 2.035 | Stable predictors: 37 | Avg selected/fit: 110.2 | p = 29829 OG_10480: PFER <= 1.461 | Stable predictors: 43 | Avg selected/fit: 93.4 | p = 29829 OG_10509: PFER <= 1.520 | Stable predictors: 36 | Avg selected/fit: 95.2 | p = 29829 OG_10516: PFER <= 1.513 | Stable predictors: 40 | Avg selected/fit: 95.0 | p = 29829 OG_10523: PFER <= 2.089 | Stable predictors: 36 | Avg selected/fit: 111.6 | p = 29829 OG_10812: PFER <= 1.687 | Stable predictors: 21 | Avg selected/fit: 100.3 | p = 29829 OG_10818: PFER <= 1.321 | Stable predictors: 28 | Avg selected/fit: 88.8 | p = 29829 OG_11038: PFER <= 1.434 | Stable predictors: 18 | Avg selected/fit: 92.5 | p = 29829 OG_11074: PFER <= 1.383 | Stable predictors: 31 | Avg selected/fit: 90.8 | p = 29829 OG_11156: PFER <= 1.845 | Stable predictors: 32 | Avg selected/fit: 104.9 | p = 29829 OG_11234: PFER <= 1.985 | Stable predictors: 40 | Avg selected/fit: 108.8 | p = 29829 OG_11307: PFER <= 1.822 | Stable predictors: 27 | Avg selected/fit: 104.3 | p = 29829 OG_11360: PFER <= 1.533 | Stable predictors: 21 | Avg selected/fit: 95.6 | p = 29829 OG_11365: PFER <= 0.948 | Stable predictors: 21 | Avg selected/fit: 75.2 | p = 29829 OG_11379: PFER <= 0.558 | Stable predictors: 9 | Avg selected/fit: 57.7 | p = 29829 OG_11381: PFER <= 1.486 | Stable predictors: 16 | Avg selected/fit: 94.1 | p = 29829 OG_11419: PFER <= 2.235 | Stable predictors: 39 | Avg selected/fit: 115.5 | p = 29829 OG_11429: PFER <= 1.923 | Stable predictors: 31 | Avg selected/fit: 107.1 | p = 29829 OG_11794: PFER <= 1.554 | Stable predictors: 32 | Avg selected/fit: 96.3 | p = 29829 OG_11812: PFER <= 1.476 | Stable predictors: 26 | Avg selected/fit: 93.8 | p = 29829 OG_11813: PFER <= 1.268 | Stable predictors: 24 | Avg selected/fit: 87.0 | p = 29829 OG_11831: PFER <= 2.252 | Stable predictors: 52 | Avg selected/fit: 115.9 | p = 29829 OG_11839: PFER <= 1.309 | Stable predictors: 20 | Avg selected/fit: 88.4 | p = 29829 OG_11852: PFER <= 1.670 | Stable predictors: 28 | Avg selected/fit: 99.8 | p = 29829 OG_11870: PFER <= 2.173 | Stable predictors: 44 | Avg selected/fit: 113.9 | p = 29829 OG_11957: PFER <= 1.349 | Stable predictors: 28 | Avg selected/fit: 89.7 | p = 29829 OG_11964: PFER <= 1.956 | Stable predictors: 34 | Avg selected/fit: 108.0 | p = 29829 OG_11984: PFER <= 2.078 | Stable predictors: 28 | Avg selected/fit: 111.3 | p = 29829 OG_12046: PFER <= 1.598 | Stable predictors: 33 | Avg selected/fit: 97.6 | p = 29829 OG_12050: PFER <= 1.809 | Stable predictors: 35 | Avg selected/fit: 103.9 | p = 29829 OG_12107: PFER <= 1.295 | Stable predictors: 37 | Avg selected/fit: 87.9 | p = 29829 OG_12125: PFER <= 1.791 | Stable predictors: 47 | Avg selected/fit: 103.4 | p = 29829 OG_12165: PFER <= 1.760 | Stable predictors: 33 | Avg selected/fit: 102.5 | p = 29829 OG_12320: PFER <= 1.307 | Stable predictors: 24 | Avg selected/fit: 88.3 | p = 29829 OG_12331: PFER <= 1.383 | Stable predictors: 26 | Avg selected/fit: 90.8 | p = 29829 OG_12369: PFER <= 1.042 | Stable predictors: 29 | Avg selected/fit: 78.8 | p = 29829 OG_12371: PFER <= 1.501 | Stable predictors: 34 | Avg selected/fit: 94.6 | p = 29829 OG_12496: PFER <= 1.169 | Stable predictors: 25 | Avg selected/fit: 83.5 | p = 29829 OG_12518: PFER <= 1.421 | Stable predictors: 20 | Avg selected/fit: 92.1 | p = 29829 OG_12568: PFER <= 1.701 | Stable predictors: 38 | Avg selected/fit: 100.7 | p = 29829 OG_12611: PFER <= 1.848 | Stable predictors: 30 | Avg selected/fit: 105.0 | p = 29829 OG_12612: PFER <= 1.759 | Stable predictors: 12 | Avg selected/fit: 102.4 | p = 29829 OG_12616: PFER <= 1.874 | Stable predictors: 36 | Avg selected/fit: 105.7 | p = 29829 OG_12785: PFER <= 2.459 | Stable predictors: 48 | Avg selected/fit: 121.1 | p = 29829 OG_12878: PFER <= 1.723 | Stable predictors: 43 | Avg selected/fit: 101.4 | p = 29829 OG_12880: PFER <= 1.591 | Stable predictors: 30 | Avg selected/fit: 97.4 | p = 29829 OG_12888: PFER <= 1.874 | Stable predictors: 36 | Avg selected/fit: 105.7 | p = 29829 OG_12990: PFER <= 0.567 | Stable predictors: 9 | Avg selected/fit: 58.1 | p = 29829 OG_13050: PFER <= 2.195 | Stable predictors: 43 | Avg selected/fit: 114.4 | p = 29829 OG_13066: PFER <= 1.591 | Stable predictors: 31 | Avg selected/fit: 97.4 | p = 29829 OG_13091: PFER <= 1.971 | Stable predictors: 35 | Avg selected/fit: 108.4 | p = 29829 OG_13153: PFER <= 1.715 | Stable predictors: 26 | Avg selected/fit: 101.1 | p = 29829 OG_13154: PFER <= 1.687 | Stable predictors: 34 | Avg selected/fit: 100.3 | p = 29829 OG_13191: PFER <= 1.504 | Stable predictors: 24 | Avg selected/fit: 94.7 | p = 29829 OG_13197: PFER <= 2.317 | Stable predictors: 51 | Avg selected/fit: 117.6 | p = 29829 OG_13201: PFER <= 2.410 | Stable predictors: 55 | Avg selected/fit: 119.9 | p = 29829 OG_13465: PFER <= 1.641 | Stable predictors: 30 | Avg selected/fit: 99.0 | p = 29829 OG_13484: PFER <= 2.017 | Stable predictors: 38 | Avg selected/fit: 109.7 | p = 29829 OG_13792: PFER <= 2.614 | Stable predictors: 47 | Avg selected/fit: 124.9 | p = 29829 OG_13886: PFER <= 1.793 | Stable predictors: 31 | Avg selected/fit: 103.4 | p = 29829 OG_14037: PFER <= 1.584 | Stable predictors: 30 | Avg selected/fit: 97.2 | p = 29829 OG_14038: PFER <= 2.119 | Stable predictors: 42 | Avg selected/fit: 112.4 | p = 29829 OG_14066: PFER <= 1.798 | Stable predictors: 30 | Avg selected/fit: 103.6 | p = 29829 OG_14276: PFER <= 1.673 | Stable predictors: 27 | Avg selected/fit: 99.9 | p = 29829 OG_14381: PFER <= 0.522 | Stable predictors: 7 | Avg selected/fit: 55.8 | p = 29829 OG_14382: PFER <= 1.833 | Stable predictors: 37 | Avg selected/fit: 104.6 | p = 29829 OG_14407: PFER <= 1.701 | Stable predictors: 36 | Avg selected/fit: 100.8 | p = 29829 OG_14505: PFER <= 2.022 | Stable predictors: 35 | Avg selected/fit: 109.8 | p = 29829 OG_14540: PFER <= 1.475 | Stable predictors: 16 | Avg selected/fit: 93.8 | p = 29829 OG_14563: PFER <= 1.165 | Stable predictors: 25 | Avg selected/fit: 83.4 | p = 29829 OG_14588: PFER <= 1.107 | Stable predictors: 26 | Avg selected/fit: 81.3 | p = 29829 OG_14615: PFER <= 1.595 | Stable predictors: 30 | Avg selected/fit: 97.6 | p = 29829 OG_14623: PFER <= 2.273 | Stable predictors: 58 | Avg selected/fit: 116.4 | p = 29829 OG_14626: PFER <= 2.211 | Stable predictors: 39 | Avg selected/fit: 114.8 | p = 29829 OG_14685: PFER <= 2.410 | Stable predictors: 41 | Avg selected/fit: 119.9 | p = 29829 OG_14710: PFER <= 1.414 | Stable predictors: 21 | Avg selected/fit: 91.8 | p = 29829 OG_14804: PFER <= 1.585 | Stable predictors: 42 | Avg selected/fit: 97.2 | p = 29829 OG_15070: PFER <= 1.923 | Stable predictors: 25 | Avg selected/fit: 107.1 | p = 29829 OG_15086: PFER <= 1.067 | Stable predictors: 21 | Avg selected/fit: 79.8 | p = 29829 OG_15137: PFER <= 1.159 | Stable predictors: 23 | Avg selected/fit: 83.2 | p = 29829 OG_15208: PFER <= 1.065 | Stable predictors: 22 | Avg selected/fit: 79.7 | p = 29829 OG_15303: PFER <= 3.456 | Stable predictors: 84 | Avg selected/fit: 143.6 | p = 29829 OG_15375: PFER <= 1.251 | Stable predictors: 13 | Avg selected/fit: 86.4 | p = 29829 OG_15544: PFER <= 1.620 | Stable predictors: 30 | Avg selected/fit: 98.3 | p = 29829 === Stable Predictor Summary (Selection Prob >= 60%) === Number of stable predictor-gene associations: 7091 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 120769 644 0.533 2 lncRNA 220994 6424 2.91 3 miRNA 836 23 2.75 Breakdown of STABLE predictors by type: # A tibble: 3 × 5 Predictor_Type N_stable N_unique_predictors N_genes_affected 1 Gene Body Meth 644 595 170 2 lncRNA 6424 4023 218 3 miRNA 23 15 20 # 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:2026-03-05 04:09:42.622545 Timing summary: - Bootstrapping: 38.32 minutes - Stability selection: 151.64 minutes - Total runtime: 189.96 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: 7091 - Bootstrap replicates: 10 (round 1), 10 (round 2) - Subsample iterations: 200 (producing 400 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_20260305_005546.txt === END OF SCRIPT === === Log closed: 2026-03-05 04:09:42.632324 ===