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do_xgboost() runs the XGBoost classification model pipeline. It splits the data into training and test sets, creates class-balanced case-control groups, and fits the model. It also performs hyperparameter optimization, fits the best model, tests it, and plots useful the feature variable importance.

Usage

do_xgboost(
  olink_data,
  metadata,
  variable = "Disease",
  case,
  control,
  wide = TRUE,
  strata = TRUE,
  balance_groups = TRUE,
  only_female = NULL,
  only_male = NULL,
  exclude_cols = "Sex",
  ratio = 0.75,
  cor_threshold = 0.9,
  normalize = TRUE,
  cv_sets = 5,
  grid_size = 50,
  ncores = 4,
  hypopt_vis = TRUE,
  palette = NULL,
  vline = TRUE,
  subtitle = c("accuracy", "sensitivity", "specificity", "auc", "features",
    "top-features"),
  varimp_yaxis_names = FALSE,
  nfeatures = 9,
  points = TRUE,
  boxplot_xaxis_names = FALSE,
  seed = 123
)

Arguments

Olink data.

metadata

Metadata.

variable

The variable to predict. Default is "Disease".

case

The case group.

control

The control groups.

wide

Whether the data is wide format. Default is TRUE.

strata

Whether to stratify the data. Default is TRUE.

balance_groups

Whether to balance the groups. Default is TRUE.

only_female

Vector of diseases.

only_male

Vector of diseases.

exclude_cols

Columns to exclude from the data before the model is tuned.

ratio

Ratio of training data to test data. Default is 0.75.

cor_threshold

Threshold of absolute correlation values. This will be used to remove the minimum number of features so that all their resulting absolute correlations are less than this value.

normalize

Whether to normalize numeric data to have a standard deviation of one and a mean of zero. Default is TRUE.

cv_sets

Number of cross-validation sets. Default is 5.

grid_size

Size of the hyperparameter optimization grid. Default is 50.

ncores

Number of cores to use for parallel processing. Default is 4.

hypopt_vis

Whether to visualize hyperparameter optimization results. Default is TRUE.

palette

The color palette for the plot. If it is a character, it should be one of the palettes from get_hpa_palettes(). Default is NULL.

vline

Whether to add a vertical line at 50% importance. Default is TRUE.

subtitle

Vector of subtitle elements to include in the plot. Default is a list with all.

varimp_yaxis_names

Whether to add y-axis names to the plot. Default is FALSE.

nfeatures

Number of top features to include in the boxplot. Default is 9.

points

Whether to add points to the boxplot. Default is TRUE.

boxplot_xaxis_names

Whether to add x-axis names to the boxplot. Default is FALSE.

seed

Seed for reproducibility. Default is 123.

Value

A list with results for each disease. The list contains:

  • hypopt_res: Hyperparameter optimization results.

  • finalfit_res: Final model fitting results.

  • testfit_res: Test model fitting results.

  • var_imp_res: Variable importance results.

  • boxplot_res: Boxplot results.

Examples

do_xgboost(example_data,
           example_metadata,
           case = "AML",
           control = c("CLL", "MYEL"),
           balance_groups = TRUE,
           wide = FALSE,
           palette = "cancers12",
           cv_sets = 5,
           grid_size = 10,
           ncores = 1)
#> Joining with `by = join_by(DAid)`
#> Sets and groups are ready. Model fitting is starting...
#> Classification model for AML as case is starting...
#> $hypopt_res
#> $hypopt_res$xgboost_tune
#> # Tuning results
#> # 5-fold cross-validation using stratification 
#> # A tibble: 5 × 5
#>   splits          id    .metrics           .notes           .predictions       
#>   <list>          <chr> <list>             <list>           <list>             
#> 1 <split [59/16]> Fold1 <tibble [10 × 10]> <tibble [0 × 3]> <tibble [160 × 11]>
#> 2 <split [59/16]> Fold2 <tibble [10 × 10]> <tibble [0 × 3]> <tibble [160 × 11]>
#> 3 <split [60/15]> Fold3 <tibble [10 × 10]> <tibble [0 × 3]> <tibble [150 × 11]>
#> 4 <split [61/14]> Fold4 <tibble [10 × 10]> <tibble [0 × 3]> <tibble [140 × 11]>
#> 5 <split [61/14]> Fold5 <tibble [10 × 10]> <tibble [0 × 3]> <tibble [140 × 11]>
#> 
#> $hypopt_res$xgboost_wf
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: boost_tree()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 4 Recipe Steps
#> 
#> • step_normalize()
#> • step_nzv()
#> • step_corr()
#> • step_impute_knn()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Boosted Tree Model Specification (classification)
#> 
#> Main Arguments:
#>   mtry = tune::tune()
#>   trees = 1000
#>   min_n = tune::tune()
#>   tree_depth = tune::tune()
#>   learn_rate = tune::tune()
#>   loss_reduction = tune::tune()
#>   sample_size = tune::tune()
#> 
#> Computational engine: xgboost 
#> 
#> 
#> $hypopt_res$train_set
#> # A tibble: 75 × 102
#>    DAid    AARSD1   ABL1  ACAA1   ACAN    ACE2  ACOX1    ACP5    ACP6  ACTA2
#>    <chr>    <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl>
#>  1 DA00003  NA    NA     NA      0.989 NA       0.330  1.37   NA      NA    
#>  2 DA00004   3.41  3.38   1.69  NA      1.52   NA      0.841   0.582   1.70 
#>  3 DA00005   5.01  5.05   0.128  0.401 -0.933  -0.584  0.0265  1.16    2.73 
#>  4 DA00007  NA    NA      3.96   0.682  3.14    2.62   1.47    2.25    2.01 
#>  5 DA00008   2.78  0.812 -0.552  0.982 -0.101  -0.304  0.376  -0.826   1.52 
#>  6 DA00009   4.39  3.34  -0.452 -0.868  0.395   1.71   1.49   -0.0285  0.200
#>  7 DA00010   1.83  1.21  -0.912 -1.04  -0.0918 -0.304  1.69    0.0920  2.04 
#>  8 DA00011   3.48  4.96   3.50  -0.338  4.48    1.26   2.18    1.62    1.79 
#>  9 DA00012   4.31  0.710 -1.44  -0.218 -0.469  -0.361 -0.0714 -1.30    2.86 
#> 10 DA00013   1.31  2.52   1.11   0.997  4.56   -1.35   0.833   2.33    3.57 
#> # ℹ 65 more rows
#> # ℹ 92 more variables: ACTN4 <dbl>, ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>,
#> #   ADAM15 <dbl>, ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>,
#> #   ADAMTS16 <dbl>, ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>,
#> #   ADGRG1 <dbl>, ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>,
#> #   AGR3 <dbl>, AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>,
#> #   AIF1 <dbl>, AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, …
#> 
#> $hypopt_res$test_set
#> # A tibble: 27 × 102
#>    DAid    AARSD1       ABL1  ACAA1    ACAN  ACE2   ACOX1   ACP5  ACP6 ACTA2
#>    <chr>    <dbl>      <dbl>  <dbl>   <dbl> <dbl>   <dbl>  <dbl> <dbl> <dbl>
#>  1 DA00001   3.39  2.76       1.71   0.0333 1.76  -0.919   1.54  2.15  2.81 
#>  2 DA00002   1.42  1.25      -0.816 -0.459  0.826 -0.902   0.647 1.30  0.798
#>  3 DA00006   6.83  1.18      -1.74  -0.156  1.53  -0.721   0.620 0.527 0.772
#>  4 DA00016   1.79  1.36       0.106 -0.372  3.40  -1.19    1.77  1.07  2.00 
#>  5 DA00022   7.07  5.67       3.68  -0.458  3.09   0.690   0.649 2.17  1.83 
#>  6 DA00023   2.92 -0.0000706  0.602  1.59   0.198  1.61    0.283 2.35  2.11 
#>  7 DA00034   3.45  2.91       1.31   0.423  0.647  1.40    0.691 0.720 1.95 
#>  8 DA00035   4.39  3.31       0.454  0.290  2.68   0.116  -1.32  0.945 2.14 
#>  9 DA00038   2.23  1.42       0.484  1.72   1.46   0.0747  1.82  0.109 4.27 
#> 10 DA00039   4.26  0.572     -1.97  -0.433  0.208  0.790  -0.236 1.52  0.652
#> # ℹ 17 more rows
#> # ℹ 92 more variables: ACTN4 <dbl>, ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>,
#> #   ADAM15 <dbl>, ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>,
#> #   ADAMTS16 <dbl>, ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>,
#> #   ADGRG1 <dbl>, ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>,
#> #   AGR3 <dbl>, AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>,
#> #   AIF1 <dbl>, AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, …
#> 
#> $hypopt_res$hypopt_vis

#> 
#> 
#> $finalfit_res
#> $finalfit_res$final
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: boost_tree()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 4 Recipe Steps
#> 
#> • step_normalize()
#> • step_nzv()
#> • step_corr()
#> • step_impute_knn()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> ##### xgb.Booster
#> raw: 774.6 Kb 
#> call:
#>   xgboost::xgb.train(params = list(eta = 0.00681292069057962, max_depth = 4L, 
#>     gamma = 1.89573565240638e-09, colsample_bytree = 1, colsample_bynode = 0.23, 
#>     min_child_weight = 2L, subsample = 0.5), data = x$data, nrounds = 1000, 
#>     watchlist = x$watchlist, verbose = 0, nthread = 1, objective = "binary:logistic")
#> params (as set within xgb.train):
#>   eta = "0.00681292069057962", max_depth = "4", gamma = "1.89573565240638e-09", colsample_bytree = "1", colsample_bynode = "0.23", min_child_weight = "2", subsample = "0.5", nthread = "1", objective = "binary:logistic", validate_parameters = "TRUE"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.evaluation.log()
#> # of features: 100 
#> niter: 1000
#> nfeatures : 100 
#> evaluation_log:
#>   iter training_logloss
#>  <num>            <num>
#>      1        0.6911923
#>      2        0.6893847
#>    ---              ---
#>    999        0.1824523
#>   1000        0.1823977
#> 
#> $finalfit_res$best
#> # A tibble: 1 × 6
#>    mtry min_n tree_depth learn_rate loss_reduction sample_size
#>   <int> <int>      <int>      <dbl>          <dbl>       <dbl>
#> 1    23     2          4    0.00681  0.00000000190         0.5
#> 
#> $finalfit_res$final_wf
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: boost_tree()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 4 Recipe Steps
#> 
#> • step_normalize()
#> • step_nzv()
#> • step_corr()
#> • step_impute_knn()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Boosted Tree Model Specification (classification)
#> 
#> Main Arguments:
#>   mtry = 23
#>   trees = 1000
#>   min_n = 2
#>   tree_depth = 4
#>   learn_rate = 0.00681292069057962
#>   loss_reduction = 1.89573565240638e-09
#>   sample_size = 0.5
#> 
#> Computational engine: xgboost 
#> 
#> 
#> 
#> $testfit_res
#> $testfit_res$metrics
#> $testfit_res$metrics$accuracy
#> [1] 0.63
#> 
#> $testfit_res$metrics$sensitivity
#> [1] 0.5
#> 
#> $testfit_res$metrics$specificity
#> [1] 0.77
#> 
#> $testfit_res$metrics$auc
#> [1] 0.77
#> 
#> $testfit_res$metrics$conf_matrix
#>           Truth
#> Prediction  0  1
#>          0  7  3
#>          1  7 10
#> 
#> $testfit_res$metrics$roc_curve

#> 
#> 
#> $testfit_res$mixture
#> [1] NA
#> 
#> 
#> $var_imp_res
#> $var_imp_res$features
#> # A tibble: 96 × 3
#>    Variable Importance Scaled_Importance
#>    <fct>         <dbl>             <dbl>
#>  1 AZU1         0.127              100  
#>  2 ADA          0.0931              73.4
#>  3 AMY2B        0.0722              56.9
#>  4 ANGPT2       0.0646              50.9
#>  5 AGR2         0.0557              43.9
#>  6 ADAM8        0.0428              33.7
#>  7 ATOX1        0.0352              27.7
#>  8 ANG          0.0322              25.3
#>  9 ACP6         0.0295              23.2
#> 10 ANGPTL3      0.0251              19.7
#> # ℹ 86 more rows
#> 
#> $var_imp_res$var_imp_plot

#> 
#> 
#> $boxplot_res
#> Warning: Removed 46 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).
#> Warning: Removed 8 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).
#> Warning: Removed 38 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_point()`).

#>