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Load libraries in a separated chunk
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@fouodo
fouodo committed Jul 17, 2024
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11 changes: 6 additions & 5 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ library(ranger)
data("entities")
# This is a list containing two lists of data: training and test.
# Each sublist contains three entities.
str(object = entities, max.level = 2)
str(object = entities, max.level = 2L)
```

Variable selection, training and prediction are the main functionalities of `fuseMLR`. As variable selection and training are performed for a training study, predictions are made for a new study.
Expand Down Expand Up @@ -115,7 +115,8 @@ We need to set up variable selection methods to our training study. Note that th

```{r varsel_param, include=TRUE, eval=TRUE}
same_param_varsel <- ParamVarSel$new(id = "ParamVarSel",
param_list = list(num.trees = 1000, mtry = 3))
param_list = list(num.trees = 1000L,
mtry = 3L))
print(same_param_varsel)
```

Expand Down Expand Up @@ -159,8 +160,8 @@ We can now train our study using the subset of selected variables. Users can cho
```{r lrner_param, include=TRUE, eval=TRUE}
same_param <- ParamLrner$new(id = "ParamRanger",
param_list = list(probability = TRUE,
mtry = 1),
hyperparam_list = list(num.trees = 1000))
mtry = 2L),
hyperparam_list = list(num.trees = 1000L))
```

- Set up learners for each layer. We will use a weighted sum for the meta-analysis.
Expand Down Expand Up @@ -197,7 +198,7 @@ lrner_meta <- Lrner$new(id = "weighted",
disease <- train_study$getTargetValues()$disease
trained_study <- train_study$train(resampling_method = "caret::createFolds",
resampling_arg = list(y = disease,
k = 2),
k = 2L),
use_var_sel = TRUE)
# Let us now check the status of our study.
print(trained_study)
Expand Down
159 changes: 81 additions & 78 deletions README.md
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Original file line number Diff line number Diff line change
@@ -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 @@ -64,7 +73,7 @@ library(ranger)
data("entities")
# This is a list containing two lists of data: training and test.
# Each sublist contains three entities.
str(object = entities, max.level = 2)
str(object = entities, max.level = 2L)
```

## List of 2
Expand Down Expand Up @@ -178,7 +187,8 @@ For simplicity, we will set up the same method on all layers.

``` r
same_param_varsel <- ParamVarSel$new(id = "ParamVarSel",
param_list = list(num.trees = 1000, mtry = 3))
param_list = list(num.trees = 1000L,
mtry = 3L))
print(same_param_varsel)
```

Expand Down Expand Up @@ -224,54 +234,47 @@ print(var_sel_res)
## 1 geneexpr ACACA
## 2 geneexpr ASNS
## 3 geneexpr BAP1
## 4 geneexpr CDH3
## 5 geneexpr CHEK2
## 6 geneexpr EIF4E
## 7 geneexpr MAP2K1
## 8 geneexpr MAPK14
## 9 geneexpr PCNA
## 10 geneexpr SMAD4
## 11 geneexpr SQSTM1
## 12 geneexpr YWHAE
## 13 geneexpr YWHAZ
## 14 proteinexpr Bap1.c.4
## 15 proteinexpr Bid
## 16 proteinexpr Cyclin_E2
## 17 proteinexpr P.Cadherin
## 18 proteinexpr Chk1
## 19 proteinexpr Chk1_pS345
## 20 proteinexpr EGFR
## 21 proteinexpr EGFR_pY1173
## 22 proteinexpr HER3_pY1289
## 23 proteinexpr MIG.6
## 24 proteinexpr ETS.1
## 25 proteinexpr MEK1_pS217_S221
## 26 proteinexpr p38_MAPK
## 27 proteinexpr c.Met_pY1235
## 28 proteinexpr N.Ras
## 29 proteinexpr PCNA
## 30 proteinexpr PEA15_pS116
## 31 proteinexpr PKC.delta_pS664
## 32 proteinexpr Rad50
## 33 proteinexpr C.Raf_pS338
## 34 proteinexpr p70S6K
## 35 proteinexpr p70S6K_pT389
## 36 proteinexpr Smad4
## 37 proteinexpr STAT3_pY705
## 38 proteinexpr 14.3.3_epsilon
## 39 methylation cg20139214
## 40 methylation cg18457775
## 41 methylation cg24747396
## 42 methylation cg01306510
## 43 methylation cg11861730
## 44 methylation cg02412050
## 45 methylation cg07566050
## 46 methylation cg02630105
## 47 methylation cg20849549
## 48 methylation cg00547829
## 49 methylation cg25539131
## 50 methylation cg07064406
## 51 methylation cg11816577
## 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

For each layer, the variable selection results show the chosen
variables. In this example, we perform variable selection on the entire
Expand All @@ -289,8 +292,8 @@ will use the same learner for all layers.
``` r
same_param <- ParamLrner$new(id = "ParamRanger",
param_list = list(probability = TRUE,
mtry = 1),
hyperparam_list = list(num.trees = 1000))
mtry = 2L),
hyperparam_list = list(num.trees = 1000L))
```

- Set up learners for each layer. We will use a weighted sum for the
Expand Down Expand Up @@ -328,7 +331,7 @@ lrner_meta <- Lrner$new(id = "weighted",
disease <- train_study$getTargetValues()$disease
trained_study <- train_study$train(resampling_method = "caret::createFolds",
resampling_arg = list(y = disease,
k = 2),
k = 2L),
use_var_sel = TRUE)
# Let us now check the status of our study.
print(trained_study)
Expand Down Expand Up @@ -376,9 +379,9 @@ print(model_ge)
## Layer : geneexpr
## ind. id. : IDS
## target : disease
## n : 22
## n : 25
## Missing : 0
## p : 14
## p : 10

#### C) Predicting

Expand Down Expand Up @@ -427,29 +430,29 @@ print(new_predictions)
##
## $predicted_values
## IDS geneexpr proteinexpr methylation meta_layer
## 1 subject4 0.6484115 0.6232698 0.31071389 0.5184565
## 2 subject7 0.4769810 0.2258972 0.54508016 0.4157126
## 3 subject8 0.7140984 0.8477976 0.66553690 0.7423027
## 4 subject10 0.6940758 0.7736833 0.64894563 0.7050413
## 5 subject13 0.5576877 0.3196278 0.12406032 0.3205452
## 6 subject15 0.7106389 0.8446940 0.27681587 0.6005008
## 7 subject16 0.6214357 0.3434575 0.24932183 0.3927695
## 8 subject18 0.6862171 0.2557313 0.09267262 0.3258879
## 9 subject23 0.6038460 0.2769857 0.55668968 0.4752774
## 10 subject24 0.4790024 0.6149226 0.45314762 0.5161260
## 11 subject27 0.4629325 0.2620349 0.51638849 0.4135396
## 12 subject31 0.4329060 0.8181782 0.47140833 0.5783116
## 13 subject32 0.5698889 0.7577599 0.65391865 0.6642461
## 14 subject35 0.4604948 0.7791980 0.45331230 0.5667484
## 15 subject36 0.3806321 0.1859825 0.46996190 0.3462660
## 16 subject50 0.7118357 0.5917563 0.64517460 0.6468713
## 17 subject54 0.5519837 0.6745282 0.67802460 0.6391312
## 18 subject55 0.6578337 0.2442829 0.44351508 0.4395823
## 19 subject59 0.4506401 0.2893857 0.45415952 0.3968377
## 20 subject62 0.4784972 0.3452619 0.33005794 0.3796489
## 21 subject63 0.4671940 0.8077119 0.82196270 0.7109832
## 22 subject66 0.6660956 0.7094044 0.85581270 0.7490700
## 23 subject70 0.3423341 0.3459401 0.31369524 0.3332727
## 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

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