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Load libraries in a separated chunk
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@fouodo
fouodo committed Jul 17, 2024
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8 changes: 8 additions & 0 deletions README.Rmd
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Expand Up @@ -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.
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136 changes: 77 additions & 59 deletions README.md
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@@ -1,3 +1,12 @@
---
title: "fuseMLR"
author: Cesaire J. K. Fouodo
output:
md_document:
variant: gfm
preserve_yaml: true
---

<!-- badges: start -->

[![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)
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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

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reserved.

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