From 470e0baf8337bd7233ee1790dd2f7d8ab391687f Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Wed, 17 Jul 2024 17:45:36 +0200 Subject: [PATCH] Load libraries in a separated chunk --- README.Rmd | 2 +- README.md | 146 ++++++++++++++++++++++++++++------------------------- 2 files changed, 78 insertions(+), 70 deletions(-) diff --git a/README.Rmd b/README.Rmd index aa274bf..00a2e93 100644 --- a/README.Rmd +++ b/README.Rmd @@ -26,7 +26,7 @@ Recent technological advances have enabled the simultaneous targeting of multipl We introduce the fuseMLR package for late integration modeling in R. This package allows users to define studies with multiple layers, data entities, and layer-specific machine learning methods. FuseMLR is user-friendly, enabling the training of different models across layers and automatically conducting meta-analysis once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities. -`fuseMLR` object oriented based on the `R6` package version 2.5.1. +`fuseMLR` is an object-oriented package based on `R6` version 2.5.1. ### Installation diff --git a/README.md b/README.md index 1f87be1..4a470dd 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,11 @@ +--- +title: "fuseMLR" +author: Cesaire J. K. Fouodo +output: + md_document: + variant: gfm + preserve_yaml: true +--- @@ -38,7 +46,7 @@ and automatically conducting meta-analysis once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities. -`fuseMLR` object oriented based on the `R6` package version 2.5.1. +`fuseMLR` is an object-oriented package based on `R6` version 2.5.1. ### Installation @@ -224,49 +232,49 @@ print(var_sel_res) ## Layer variable ## 1 geneexpr ACACA - ## 2 geneexpr ASNS - ## 3 geneexpr BAP1 - ## 4 geneexpr CHEK2 - ## 5 geneexpr EIF4E - ## 6 geneexpr MAP2K1 - ## 7 geneexpr MAPK14 - ## 8 geneexpr PCNA - ## 9 geneexpr YWHAE - ## 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 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 - ## 44 methylation cg11816577 + ## 2 geneexpr BAP1 + ## 3 geneexpr CHEK2 + ## 4 geneexpr EIF4E + ## 5 geneexpr MAP2K1 + ## 6 geneexpr MAPK14 + ## 7 geneexpr PCNA + ## 8 geneexpr SMAD4 + ## 9 geneexpr SQSTM1 + ## 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 cg01306510 + ## 39 methylation cg02412050 + ## 40 methylation cg07566050 + ## 41 methylation cg02630105 + ## 42 methylation cg20849549 + ## 43 methylation cg25539131 + ## 44 methylation cg07064406 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -371,9 +379,9 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 25 + ## n : 30 ## Missing : 0 - ## p : 10 + ## p : 11 #### C) Predicting @@ -422,29 +430,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 subject4 0.3486429 0.7254452 0.39821270 0.5065696 - ## 2 subject7 0.6563234 0.1913813 0.60077143 0.4633997 - ## 3 subject8 0.7697984 0.8252417 0.72493373 0.7749673 - ## 4 subject10 0.6770103 0.8271147 0.75742738 0.7609400 - ## 5 subject13 0.4164071 0.2048258 0.09259405 0.2251955 - ## 6 subject15 0.5557313 0.9379647 0.36718214 0.6331977 - ## 7 subject16 0.5701992 0.3528000 0.26069683 0.3817718 - ## 8 subject18 0.6497238 0.2932206 0.02371310 0.2996893 - ## 9 subject23 0.6612794 0.2485218 0.60419841 0.4873196 - ## 10 subject24 0.3336988 0.5391282 0.46320079 0.4552991 - ## 11 subject27 0.2748909 0.2293552 0.55319563 0.3542913 - ## 12 subject31 0.4272877 0.8598790 0.59272540 0.6461933 - ## 13 subject32 0.4388905 0.8694258 0.76771111 0.7136272 - ## 14 subject35 0.2083437 0.7910008 0.56550437 0.5497232 - ## 15 subject36 0.6707909 0.1662433 0.56591627 0.4459862 - ## 16 subject50 0.7561679 0.6132381 0.79458849 0.7160860 - ## 17 subject54 0.5675159 0.7214897 0.78051508 0.6988200 - ## 18 subject55 0.5815325 0.2319464 0.58723532 0.4529200 - ## 19 subject59 0.4597877 0.2770992 0.50990794 0.4089069 - ## 20 subject62 0.2760032 0.2478115 0.41481548 0.3135595 - ## 21 subject63 0.5786218 0.8238242 0.89740000 0.7806484 - ## 22 subject66 0.5528552 0.6825976 0.94541984 0.7373127 - ## 23 subject70 0.2690111 0.3042032 0.27120873 0.2829184 + ## 1 subject4 0.4284929 0.6236087 0.54089762 0.5366185 + ## 2 subject7 0.3945603 0.1347798 0.39843810 0.3031986 + ## 3 subject8 0.7740583 0.7649012 0.72124563 0.7526983 + ## 4 subject10 0.8138460 0.7197560 0.68419048 0.7358568 + ## 5 subject13 0.3990690 0.1749690 0.04975873 0.1994421 + ## 6 subject15 0.6226012 0.8045405 0.53131865 0.6562456 + ## 7 subject16 0.7231187 0.2866393 0.18147381 0.3818238 + ## 8 subject18 0.4848552 0.1877734 0.02350040 0.2208036 + ## 9 subject23 0.7821425 0.2133167 0.52539960 0.4912287 + ## 10 subject24 0.3355567 0.6086540 0.55799802 0.5092029 + ## 11 subject27 0.5103222 0.1461369 0.48094762 0.3703170 + ## 12 subject31 0.2733171 0.7369151 0.60301230 0.5516776 + ## 13 subject32 0.6264103 0.7453123 0.67544286 0.6856325 + ## 14 subject35 0.3754468 0.7400325 0.55803492 0.5680832 + ## 15 subject36 0.5930313 0.1646988 0.46518294 0.3964029 + ## 16 subject50 0.7730298 0.5586726 0.83126032 0.7164955 + ## 17 subject54 0.6100532 0.6796512 0.87089802 0.7242468 + ## 18 subject55 0.5157575 0.1851198 0.46629841 0.3808416 + ## 19 subject59 0.4551202 0.2035905 0.46137937 0.3675182 + ## 20 subject62 0.3280798 0.2101849 0.25384048 0.2605816 + ## 21 subject63 0.3223774 0.7519175 0.92001825 0.6803795 + ## 22 subject66 0.7428627 0.6247083 0.98381746 0.7832520 + ## 23 subject70 0.2834738 0.2386155 0.31945357 0.2797943 © 2024 Institute of Medical Biometry and Statistics (IMBS). 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