diff --git a/README.Rmd b/README.Rmd index 1bad599..0e08d6e 100644 --- a/README.Rmd +++ b/README.Rmd @@ -40,10 +40,18 @@ devtools::install_github("imbs-hl/fuseMLR") The following example is based on simulated data available in `fuseMLR`. Data have been simulated using the R package `InterSIM`, version 2.2.0. +```{r libraries} +library(fuseMLR) +library(UpSetR) +library(ranger) +``` + - Let us inspect our simulated data. ```{r data_exam, include=TRUE, eval=TRUE} library(fuseMLR) +library(UpSetR) +library(ranger) data("entities") # This is a list containing two lists of data: training and test. # Each sublist contains three entities. diff --git a/README.md b/README.md index 588ae9c..5e06be2 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) @@ -52,10 +61,18 @@ devtools::install_github("imbs-hl/fuseMLR") The following example is based on simulated data available in `fuseMLR`. Data have been simulated using the R package `InterSIM`, version 2.2.0. +``` r +library(fuseMLR) +library(UpSetR) +library(ranger) +``` + - Let us inspect our simulated data. ``` r library(fuseMLR) +library(UpSetR) +library(ranger) data("entities") # This is a list containing two lists of data: training and test. # Each sublist contains three entities. @@ -222,45 +239,46 @@ print(var_sel_res) ## 4 geneexpr CHEK2 ## 5 geneexpr EIF4E ## 6 geneexpr MAP2K1 - ## 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 + ## 7 geneexpr MAPK14 + ## 8 geneexpr PCNA + ## 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 cg24747396 + ## 39 methylation cg01306510 + ## 40 methylation cg02412050 ## 41 methylation cg07566050 ## 42 methylation cg02630105 ## 43 methylation cg20849549 ## 44 methylation cg25539131 ## 45 methylation cg07064406 + ## 46 methylation cg11816577 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -365,9 +383,9 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 23 + ## n : 26 ## Missing : 0 - ## p : 10 + ## p : 11 #### C) Predicting @@ -416,29 +434,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 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 + ## 1 subject4 0.3973369 0.6258298 0.40468810 0.4747581 + ## 2 subject7 0.4970290 0.2082278 0.50162817 0.4045058 + ## 3 subject8 0.7201671 0.8443897 0.69956587 0.7528150 + ## 4 subject10 0.6627052 0.7825214 0.72233810 0.7247158 + ## 5 subject13 0.4550956 0.2703933 0.18150397 0.2897634 + ## 6 subject15 0.6651290 0.8695881 0.38473690 0.6242409 + ## 7 subject16 0.6968484 0.3446230 0.33281786 0.4421039 + ## 8 subject18 0.7117651 0.2481956 0.06102222 0.3106017 + ## 9 subject23 0.6176917 0.2434262 0.56311746 0.4745496 + ## 10 subject24 0.2775048 0.6383849 0.59248770 0.5162459 + ## 11 subject27 0.4095131 0.1903718 0.43144246 0.3463857 + ## 12 subject31 0.4308321 0.7984313 0.64784524 0.6341570 + ## 13 subject32 0.5034480 0.7836944 0.59301071 0.6293263 + ## 14 subject35 0.3417940 0.7738702 0.63193214 0.5942417 + ## 15 subject36 0.5922778 0.1949044 0.54351984 0.4438241 + ## 16 subject50 0.7657020 0.5289222 0.71462341 0.6687886 + ## 17 subject54 0.4741087 0.6518258 0.85331310 0.6777043 + ## 18 subject55 0.5646897 0.2087369 0.47323651 0.4133688 + ## 19 subject59 0.4569187 0.2231567 0.41613413 0.3649424 + ## 20 subject62 0.2487980 0.2618056 0.34592460 0.2903310 + ## 21 subject63 0.4214095 0.8128040 0.77892183 0.6864398 + ## 22 subject66 0.6874425 0.6665591 0.91744048 0.7689191 + ## 23 subject70 0.2074909 0.2879774 0.25336468 0.2513790 © 2024 Institute of Medical Biometry and Statistics (IMBS). 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