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Predicting new study
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@fouodo
fouodo committed Jul 17, 2024
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41 changes: 40 additions & 1 deletion README.Rmd
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Expand Up @@ -145,7 +145,7 @@ print(var_sel_res)

For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire study. However, users can opt to conduct variable selection on individual layers if desired.

#### B) Training
#### C) Training

We can now train our study using the subset of selected variables. Users can choose to set up layer-specific learners, but for illustration, we will use the same learner for all layers.

Expand Down Expand Up @@ -206,3 +206,42 @@ print(tl_ge)
model_ge <- tl_ge$getModel()
print(model_ge)
```

#### C) Predicting

Now, we have created a training study, performed variable selection and trained the study with the chosen variables. In this section, we create and predict a new study.

- Create a new study.

```{r new_study, include=TRUE, eval=TRUE}
new_study <- NewStudy$new(id = "new_study", ind_col = "IDS")
```

- Create new layers.

```{r new_layer, include=TRUE, eval=TRUE}
nl_ge <- NewLayer$new(id = "geneexpr", new_study = new_study)
nl_pr <- NewLayer$new(id = "proteinexpr", new_study = new_study)
nl_me <- NewLayer$new(id = "methylation", new_study = new_study)
```

- Instantiate and add new training data to new layers.

```{r new_data, include=TRUE, eval=TRUE}
new_data_ge <- NewData$new(id = "geneexpr",
new_layer = nl_ge,
data_frame = entities$testing$geneexpr)
new_data_pr <- NewData$new(id = "proteinexpr",
new_layer = nl_pr,
data_frame = entities$testing$proteinexpr)
new_data_me <- NewData$new(id = "methylation",
new_layer = nl_me,
data_frame = entities$testing$methylation)
```

- Predict the new study.

```{r new_pred, include=TRUE, eval=TRUE}
new_predictions <- train_study$predict(new_study = new_study)
print(new_predictions)
```
156 changes: 115 additions & 41 deletions README.md
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Expand Up @@ -227,52 +227,55 @@ print(var_sel_res)
## Layer variable
## 1 geneexpr ACACA
## 2 geneexpr BAP1
## 3 geneexpr CHEK2
## 4 geneexpr EIF4E
## 5 geneexpr MAP2K1
## 3 geneexpr EIF4E
## 4 geneexpr MAP2K1
## 5 geneexpr MAPK14
## 6 geneexpr PCNA
## 7 geneexpr YWHAE
## 8 proteinexpr Bap1.c.4
## 9 proteinexpr Bid
## 10 proteinexpr Cyclin_E2
## 11 proteinexpr P.Cadherin
## 12 proteinexpr Chk1
## 13 proteinexpr Chk1_pS345
## 14 proteinexpr EGFR
## 15 proteinexpr EGFR_pY1173
## 16 proteinexpr HER3_pY1289
## 17 proteinexpr MIG.6
## 18 proteinexpr ETS.1
## 19 proteinexpr MEK1_pS217_S221
## 20 proteinexpr p38_MAPK
## 21 proteinexpr c.Met_pY1235
## 22 proteinexpr N.Ras
## 23 proteinexpr PCNA
## 24 proteinexpr PEA15_pS116
## 25 proteinexpr PKC.delta_pS664
## 26 proteinexpr Rad50
## 27 proteinexpr C.Raf_pS338
## 28 proteinexpr p70S6K
## 29 proteinexpr p70S6K_pT389
## 30 proteinexpr Smad4
## 31 proteinexpr STAT3_pY705
## 32 proteinexpr 14.3.3_epsilon
## 33 methylation cg20139214
## 34 methylation cg18457775
## 35 methylation cg01306510
## 36 methylation cg02412050
## 37 methylation cg07566050
## 38 methylation cg02630105
## 39 methylation cg20849549
## 40 methylation cg25539131
## 41 methylation cg07064406
## 7 geneexpr SMAD4
## 8 geneexpr SQSTM1
## 9 geneexpr YWHAE
## 10 geneexpr YWHAZ
## 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
study. However, users can opt to conduct variable selection on
individual layers if desired.

#### B) Training
#### C) Training

We can now train our study using the subset of selected variables. Users
can choose to set up layer-specific learners, but for illustration, we
Expand Down Expand Up @@ -370,6 +373,77 @@ print(model_ge)
## Layer : geneexpr
## ind. id. : IDS
## target : disease
## n : 26
## n : 25
## Missing : 0
## p : 8
## p : 11

#### C) Predicting

Now, we have created a training study, performed variable selection and
trained the study with the chosen variables. In this section, we create
and predict a new study.

- Create a new study.

``` r
new_study <- NewStudy$new(id = "new_study", ind_col = "IDS")
```

- Create new layers.

``` r
nl_ge <- NewLayer$new(id = "geneexpr", new_study = new_study)
nl_pr <- NewLayer$new(id = "proteinexpr", new_study = new_study)
nl_me <- NewLayer$new(id = "methylation", new_study = new_study)
```

- Instantiate and add new training data to new layers.

``` r
new_data_ge <- NewData$new(id = "geneexpr",
new_layer = nl_ge,
data_frame = entities$testing$geneexpr)
new_data_pr <- NewData$new(id = "proteinexpr",
new_layer = nl_pr,
data_frame = entities$testing$proteinexpr)
new_data_me <- NewData$new(id = "methylation",
new_layer = nl_me,
data_frame = entities$testing$methylation)
```

- Predict the new study.

``` r
new_predictions <- train_study$predict(new_study = new_study)
print(new_predictions)
```

## $predicted_study
## PredictStudy : new_study
## Nb. layers : 4
##
## $predicted_values
## IDS geneexpr proteinexpr methylation meta_layer
## 1 subject4 0.6067187 0.6119083 0.33182817 0.5209286
## 2 subject7 0.4109321 0.2189310 0.61729762 0.4040821
## 3 subject8 0.6746929 0.8667262 0.80640714 0.7894835
## 4 subject10 0.6585460 0.7638556 0.66543492 0.7006365
## 5 subject13 0.4947683 0.2539440 0.08529286 0.2728232
## 6 subject15 0.6994488 0.8390187 0.32866032 0.6339475
## 7 subject16 0.6408147 0.2740290 0.32936230 0.4024482
## 8 subject18 0.5568742 0.2851813 0.05248452 0.2929357
## 9 subject23 0.6719992 0.1901524 0.71083929 0.5018748
## 10 subject24 0.4724123 0.5691786 0.53698690 0.5296822
## 11 subject27 0.4899246 0.2185917 0.59058452 0.4192783
## 12 subject31 0.3499429 0.7916210 0.50772579 0.5676212
## 13 subject32 0.5065488 0.7755845 0.73835317 0.6824607
## 14 subject35 0.4434528 0.7836210 0.60108056 0.6226296
## 15 subject36 0.3183730 0.1848798 0.52778135 0.3346572
## 16 subject50 0.6447103 0.5143079 0.77826746 0.6379506
## 17 subject54 0.5750107 0.5990496 0.82990119 0.6654878
## 18 subject55 0.6246929 0.2048667 0.56081071 0.4452689
## 19 subject59 0.3740976 0.2233389 0.55631111 0.3751600
## 20 subject62 0.4220766 0.3033536 0.40324762 0.3710933
## 21 subject63 0.3846024 0.7639377 0.85401865 0.6781510
## 22 subject66 0.6744151 0.6113643 0.94513651 0.7369564
## 23 subject70 0.2530921 0.3034790 0.37938611 0.3124967

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