diff --git a/README.md b/README.md index 2ff6f66..fb4ea68 100644 --- a/README.md +++ b/README.md @@ -235,44 +235,47 @@ print(var_sel_res) ## Layer variable ## 1 geneexpr ACACA ## 2 geneexpr BAP1 - ## 3 geneexpr EIF4E - ## 4 geneexpr MAP2K1 - ## 5 geneexpr PCNA - ## 6 geneexpr YWHAE - ## 7 proteinexpr Bap1.c.4 - ## 8 proteinexpr Bid - ## 9 proteinexpr Cyclin_E2 - ## 10 proteinexpr P.Cadherin - ## 11 proteinexpr Chk1 - ## 12 proteinexpr Chk1_pS345 - ## 13 proteinexpr EGFR - ## 14 proteinexpr EGFR_pY1173 - ## 15 proteinexpr HER3_pY1289 - ## 16 proteinexpr MIG.6 - ## 17 proteinexpr ETS.1 - ## 18 proteinexpr MEK1_pS217_S221 - ## 19 proteinexpr p38_MAPK - ## 20 proteinexpr c.Met_pY1235 - ## 21 proteinexpr N.Ras - ## 22 proteinexpr PCNA - ## 23 proteinexpr PEA15_pS116 - ## 24 proteinexpr PKC.delta_pS664 - ## 25 proteinexpr Rad50 - ## 26 proteinexpr C.Raf_pS338 - ## 27 proteinexpr p70S6K - ## 28 proteinexpr p70S6K_pT389 - ## 29 proteinexpr Smad4 - ## 30 proteinexpr STAT3_pY705 - ## 31 proteinexpr 14.3.3_epsilon - ## 32 methylation cg20139214 - ## 33 methylation cg18457775 - ## 34 methylation cg24747396 - ## 35 methylation cg01306510 - ## 36 methylation cg02412050 - ## 37 methylation cg07566050 - ## 38 methylation cg20849549 - ## 39 methylation cg25539131 - ## 40 methylation cg07064406 + ## 3 geneexpr CHEK2 + ## 4 geneexpr EIF4E + ## 5 geneexpr MAP2K1 + ## 6 geneexpr MAPK14 + ## 7 geneexpr PCNA + ## 8 geneexpr YWHAE + ## 9 proteinexpr Bap1.c.4 + ## 10 proteinexpr Bid + ## 11 proteinexpr Cyclin_E2 + ## 12 proteinexpr P.Cadherin + ## 13 proteinexpr Chk1 + ## 14 proteinexpr Chk1_pS345 + ## 15 proteinexpr EGFR + ## 16 proteinexpr EGFR_pY1173 + ## 17 proteinexpr HER3_pY1289 + ## 18 proteinexpr MIG.6 + ## 19 proteinexpr ETS.1 + ## 20 proteinexpr MEK1_pS217_S221 + ## 21 proteinexpr p38_MAPK + ## 22 proteinexpr c.Met_pY1235 + ## 23 proteinexpr N.Ras + ## 24 proteinexpr PCNA + ## 25 proteinexpr PEA15_pS116 + ## 26 proteinexpr PKC.delta_pS664 + ## 27 proteinexpr Rad50 + ## 28 proteinexpr C.Raf_pS338 + ## 29 proteinexpr p70S6K + ## 30 proteinexpr p70S6K_pT389 + ## 31 proteinexpr Smad4 + ## 32 proteinexpr STAT3_pY705 + ## 33 proteinexpr 14.3.3_epsilon + ## 34 methylation cg20139214 + ## 35 methylation cg18457775 + ## 36 methylation cg24747396 + ## 37 methylation cg01306510 + ## 38 methylation cg02412050 + ## 39 methylation cg07566050 + ## 40 methylation cg02630105 + ## 41 methylation cg20849549 + ## 42 methylation cg25539131 + ## 43 methylation cg07064406 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -377,9 +380,9 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 26 + ## n : 27 ## Missing : 0 - ## p : 7 + ## p : 9 #### C) Predicting @@ -428,29 +431,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 subject4 0.7366615 0.6384437 0.32332937 0.5639329 - ## 2 subject7 0.6361365 0.2253671 0.58885000 0.4649861 - ## 3 subject8 0.8359429 0.9122571 0.57208492 0.7783530 - ## 4 subject10 0.8332671 0.8405218 0.82245556 0.8324714 - ## 5 subject13 0.7866813 0.2582524 0.15968333 0.3808876 - ## 6 subject15 0.5889984 0.8418933 0.23018810 0.5671874 - ## 7 subject16 0.9577306 0.2872087 0.30760516 0.4905130 - ## 8 subject18 0.8485437 0.1967528 0.01865159 0.3294865 - ## 9 subject23 0.9654341 0.1157877 0.64309841 0.5378101 - ## 10 subject24 0.4120274 0.6668254 0.58165516 0.5641876 - ## 11 subject27 0.1468226 0.2008639 0.40676190 0.2525217 - ## 12 subject31 0.6630813 0.8202754 0.42525714 0.6446734 - ## 13 subject32 0.7359627 0.7580425 0.42763452 0.6432427 - ## 14 subject35 0.3883032 0.7742742 0.74610714 0.6518612 - ## 15 subject36 0.3309869 0.1619488 0.44883571 0.3055726 - ## 16 subject50 0.8563032 0.5694786 0.60321230 0.6646439 - ## 17 subject54 0.6911706 0.7139881 0.91125198 0.7719709 - ## 18 subject55 0.6768560 0.1810726 0.42335754 0.4058851 - ## 19 subject59 0.6293425 0.2157679 0.37993492 0.3908631 - ## 20 subject62 0.2332067 0.1793825 0.28952937 0.2312774 - ## 21 subject63 0.3095397 0.7660052 0.83139325 0.6535934 - ## 22 subject66 0.6125536 0.6647683 0.89241111 0.7240907 - ## 23 subject70 0.6169607 0.2390425 0.27369524 0.3612205 + ## 1 subject4 0.5729984 0.6283444 0.37592262 0.5238763 + ## 2 subject7 0.4546028 0.1430000 0.41540397 0.3306247 + ## 3 subject8 0.6938500 0.8740036 0.58768135 0.7206080 + ## 4 subject10 0.8614921 0.8670433 0.71899286 0.8137715 + ## 5 subject13 0.4828944 0.3183373 0.17456349 0.3171327 + ## 6 subject15 0.6921825 0.8668000 0.43054325 0.6627722 + ## 7 subject16 0.7396742 0.2816353 0.26842897 0.4132131 + ## 8 subject18 0.7437706 0.1875627 0.02909325 0.2976783 + ## 9 subject23 0.7547107 0.2127790 0.57286706 0.4994574 + ## 10 subject24 0.3501337 0.5554821 0.59494167 0.5081872 + ## 11 subject27 0.5125694 0.2528040 0.35571071 0.3659174 + ## 12 subject31 0.5171909 0.8374758 0.52997817 0.6350335 + ## 13 subject32 0.5383274 0.7887183 0.54169921 0.6281439 + ## 14 subject35 0.3027032 0.7871099 0.72650159 0.6219553 + ## 15 subject36 0.5808337 0.1891099 0.46222103 0.4008025 + ## 16 subject50 0.8612718 0.5800012 0.73700675 0.7183713 + ## 17 subject54 0.5155119 0.7002710 0.89052937 0.7116772 + ## 18 subject55 0.5351155 0.2145329 0.49238333 0.4067265 + ## 19 subject59 0.3558187 0.2316774 0.28723532 0.2879582 + ## 20 subject62 0.2787401 0.2784730 0.27884127 0.2786808 + ## 21 subject63 0.2825139 0.7363254 0.85223413 0.6418141 + ## 22 subject66 0.6464357 0.6408508 0.92384881 0.7411852 + ## 23 subject70 0.2521048 0.3248405 0.20570397 0.2616752 © 2024 Institute of Medical Biometry and Statistics (IMBS). 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