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Predicting
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@fouodo
fouodo committed Jul 17, 2024
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2 changes: 1 addition & 1 deletion README.Rmd
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Expand Up @@ -246,4 +246,4 @@ new_predictions <- train_study$predict(new_study = new_study)
print(new_predictions)
```

`&copy;` 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved.
&copy; 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved.
144 changes: 76 additions & 68 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 @@ -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
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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

`&copy;` 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.

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