From b63e376ae01eca289bf9edf2c53fd7477f5ae332 Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Wed, 17 Jul 2024 17:14:02 +0200 Subject: [PATCH] Predicting --- README.Rmd | 2 +- README.md | 144 ++++++++++++++++++++++++++++------------------------- 2 files changed, 77 insertions(+), 69 deletions(-) diff --git a/README.Rmd b/README.Rmd index e0671fd..1bad599 100644 --- a/README.Rmd +++ b/README.Rmd @@ -246,4 +246,4 @@ new_predictions <- train_study$predict(new_study = new_study) print(new_predictions) ``` -`©` 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved. +© 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved. diff --git a/README.md b/README.md index c538594..9057d9c 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,12 @@ +--- +title: "fuseMLR" +author: Cesaire J. K. Fouodo +output: + md_document: + variant: gfm + preserve_yaml: true +--- + [![R-CMD-check](https://github.com/imbs-hl/fuseMLR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/imbs-hl/fuseMLR/actions/workflows/R-CMD-check.yaml) @@ -222,46 +231,45 @@ print(var_sel_res) ## 4 geneexpr CHEK2 ## 5 geneexpr EIF4E ## 6 geneexpr MAP2K1 - ## 7 geneexpr MAPK14 - ## 8 geneexpr PCNA - ## 9 geneexpr SMAD4 - ## 10 geneexpr YWHAE - ## 11 proteinexpr Bap1.c.4 - ## 12 proteinexpr Bid - ## 13 proteinexpr Cyclin_E2 - ## 14 proteinexpr P.Cadherin - ## 15 proteinexpr Chk1 - ## 16 proteinexpr Chk1_pS345 - ## 17 proteinexpr EGFR - ## 18 proteinexpr EGFR_pY1173 - ## 19 proteinexpr HER3_pY1289 - ## 20 proteinexpr MIG.6 - ## 21 proteinexpr ETS.1 - ## 22 proteinexpr MEK1_pS217_S221 - ## 23 proteinexpr p38_MAPK - ## 24 proteinexpr c.Met_pY1235 - ## 25 proteinexpr N.Ras - ## 26 proteinexpr PCNA - ## 27 proteinexpr PEA15_pS116 - ## 28 proteinexpr PKC.delta_pS664 - ## 29 proteinexpr Rad50 - ## 30 proteinexpr C.Raf_pS338 - ## 31 proteinexpr p70S6K - ## 32 proteinexpr p70S6K_pT389 - ## 33 proteinexpr Smad4 - ## 34 proteinexpr STAT3_pY705 - ## 35 proteinexpr 14.3.3_epsilon - ## 36 methylation cg20139214 - ## 37 methylation cg18457775 - ## 38 methylation cg09637363 - ## 39 methylation cg01306510 - ## 40 methylation cg02412050 - ## 41 methylation cg25984124 - ## 42 methylation cg07566050 - ## 43 methylation cg02630105 - ## 44 methylation cg20849549 - ## 45 methylation cg25539131 - ## 46 methylation cg07064406 + ## 7 geneexpr PCNA + ## 8 geneexpr YWHAE + ## 9 geneexpr YWHAZ + ## 10 proteinexpr Bap1.c.4 + ## 11 proteinexpr Bid + ## 12 proteinexpr Cyclin_E2 + ## 13 proteinexpr P.Cadherin + ## 14 proteinexpr Chk1 + ## 15 proteinexpr Chk1_pS345 + ## 16 proteinexpr EGFR + ## 17 proteinexpr EGFR_pY1173 + ## 18 proteinexpr HER3_pY1289 + ## 19 proteinexpr MIG.6 + ## 20 proteinexpr ETS.1 + ## 21 proteinexpr MEK1_pS217_S221 + ## 22 proteinexpr p38_MAPK + ## 23 proteinexpr c.Met_pY1235 + ## 24 proteinexpr N.Ras + ## 25 proteinexpr PCNA + ## 26 proteinexpr PEA15_pS116 + ## 27 proteinexpr PKC.delta_pS664 + ## 28 proteinexpr Rad50 + ## 29 proteinexpr C.Raf_pS338 + ## 30 proteinexpr p70S6K + ## 31 proteinexpr p70S6K_pT389 + ## 32 proteinexpr Smad4 + ## 33 proteinexpr STAT3_pY705 + ## 34 proteinexpr 14.3.3_epsilon + ## 35 methylation cg20139214 + ## 36 methylation cg18457775 + ## 37 methylation cg24747396 + ## 38 methylation cg01306510 + ## 39 methylation cg02412050 + ## 40 methylation cg25984124 + ## 41 methylation cg07566050 + ## 42 methylation cg02630105 + ## 43 methylation cg20849549 + ## 44 methylation cg25539131 + ## 45 methylation cg07064406 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -366,9 +374,9 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 27 + ## n : 23 ## Missing : 0 - ## p : 11 + ## p : 10 #### C) Predicting @@ -417,29 +425,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 subject4 0.4725151 0.6599369 0.3956623 0.5086549 - ## 2 subject7 0.6207952 0.2535532 0.4593956 0.4343414 - ## 3 subject8 0.7858988 0.8617183 0.5170813 0.7124039 - ## 4 subject10 0.7736159 0.8144282 0.7356651 0.7736219 - ## 5 subject13 0.5496202 0.3388135 0.1889472 0.3432458 - ## 6 subject15 0.7520679 0.8456587 0.3008671 0.6170995 - ## 7 subject16 0.6741667 0.3009167 0.4049599 0.4456820 - ## 8 subject18 0.7936623 0.3036036 0.1069857 0.3701630 - ## 9 subject23 0.7684464 0.3103667 0.6047968 0.5498365 - ## 10 subject24 0.4487278 0.6280996 0.4865853 0.5246098 - ## 11 subject27 0.3982817 0.2857008 0.4592833 0.3820728 - ## 12 subject31 0.4925512 0.8178683 0.4383095 0.5846202 - ## 13 subject32 0.5856746 0.7545948 0.5088413 0.6154420 - ## 14 subject35 0.4182294 0.8120417 0.5838194 0.6153982 - ## 15 subject36 0.4785877 0.2373167 0.4264560 0.3760711 - ## 16 subject50 0.8579135 0.5765698 0.5744472 0.6558340 - ## 17 subject54 0.6482135 0.7085040 0.8919758 0.7593542 - ## 18 subject55 0.6911262 0.2668444 0.5258881 0.4835815 - ## 19 subject59 0.5783048 0.2353687 0.4648218 0.4179925 - ## 20 subject62 0.2776175 0.3502944 0.3815302 0.3411933 - ## 21 subject63 0.4978976 0.8386833 0.6942627 0.6881890 - ## 22 subject66 0.7175944 0.6737528 0.7879369 0.7285500 - ## 23 subject70 0.3779357 0.3989810 0.2578671 0.3406884 - -`©` 2024 Institute of Medical Biometry and Statistics (IMBS). All -rights reserved. + ## 1 subject4 0.5819528 0.6423877 0.4556944 0.5548049 + ## 2 subject7 0.5957044 0.2418123 0.4356028 0.4174470 + ## 3 subject8 0.8260016 0.7800290 0.6811825 0.7563455 + ## 4 subject10 0.7634992 0.7719115 0.7171159 0.7489190 + ## 5 subject13 0.5961583 0.3214702 0.3154683 0.3991494 + ## 6 subject15 0.6096869 0.7909095 0.4298448 0.6028022 + ## 7 subject16 0.7128421 0.3172734 0.2370698 0.4023062 + ## 8 subject18 0.7296052 0.2315063 0.1759512 0.3556151 + ## 9 subject23 0.7137321 0.2179992 0.5798913 0.4979338 + ## 10 subject24 0.4262175 0.5892270 0.5801833 0.5384033 + ## 11 subject27 0.3516631 0.2446571 0.4184683 0.3409613 + ## 12 subject31 0.4353333 0.7683369 0.5736972 0.5984620 + ## 13 subject32 0.5478357 0.6644591 0.6187845 0.6133989 + ## 14 subject35 0.3448282 0.6636111 0.6363849 0.5606428 + ## 15 subject36 0.5197944 0.1920357 0.4225405 0.3738314 + ## 16 subject50 0.8559476 0.4433349 0.7268147 0.6696837 + ## 17 subject54 0.5804028 0.6493194 0.8653012 0.7102443 + ## 18 subject55 0.6496242 0.2483901 0.5191702 0.4666664 + ## 19 subject59 0.4640766 0.2734353 0.3363806 0.3525088 + ## 20 subject62 0.2497897 0.2903052 0.2857087 0.2767925 + ## 21 subject63 0.3049687 0.6436583 0.8194611 0.6110195 + ## 22 subject66 0.6930524 0.6277417 0.8553694 0.7320909 + ## 23 subject70 0.2264119 0.2864357 0.2686020 0.2622834 + +© 2024 Institute of Medical Biometry and Statistics (IMBS). All rights +reserved.