Benchmark R6 class

Source: R/Benchmark.R

Benchmark R6 class

Benchmark R6 class

Public fields

.data

data.frame

is_complete

todo

contrast

column name

toscale

which columns to scale

avgInt

average Intensity

fcestimate

estimate column

benchmark

todo

model_description

describe model

model_name

model description

hierarchy

todo

smc

summarize missing contrasts

summarizeNA

statistic to use for missigness summarization (e.g. statistic, or p-value)

confusion

todo

species

todo

FDRvsFDP

todo

Methods

Public methods

Method new()

create Benchmark

Usage

Benchmark$new(
  data,
  toscale = c("p.value"),
  fcestimate = "diff",
  avgInt = "avgInt",
  benchmark = list(list(score = "diff", desc = TRUE), list(score = "statistic", desc =
    TRUE), list(score = "scaled.p.value", desc = TRUE)),
  FDRvsFDP = list(list(score = "FDR", desc = FALSE)),
  model_description = "protein level measurments, linear model",
  model_name = "medpolish_lm",
  contrast = "contrast",
  species = "species",
  hierarchy = c("protein_Id"),
  summarizeNA = "statistic"
)

Arguments

data

data.frame

toscale

columns ot scale

fcestimate

column with fold change estimates

avgInt

average protein/peptide/metabolite intensity

benchmark

columns to benchmark

FDRvsFDP

score for which to generate FDR vs FDP

model_description

describe model

model_name

model name

contrast

contrast

species

species (todo rename)

hierarchy

e.g. protein_Id

summarizeNA

examine this column to determine the proportion of missing values default statistic

columns

to create FPR vs FDP analysis for

Method data()

get data

Usage

Benchmark$data()

Returns

data.frame

Method missing_contrasts()

summarize missing contrasts

Usage

Benchmark$missing_contrasts()

Returns

data.frame

Method complete()

set or get complete. If true only proteins for which all contrasts are determinable are examined.

Usage

Benchmark$complete(value)

Arguments

value

TRUE if data should be complete (no missing contrasts)

Method .get_confusion()

get confusion data

Usage

Benchmark$.get_confusion(arrange)

Arguments

arrange

todo

Method get_confusion_benchmark()

get FDR summaries

Usage

Benchmark$get_confusion_benchmark()

Method n_confusion_benchmark()

nr of elements used to determine ROC curve

Usage

Benchmark$n_confusion_benchmark()

Method plot_ROC()

plot FDR summaries

Usage

Benchmark$plot_ROC(xlim = 0.5)

Arguments

xlim

limit x axis

Returns

ggplot

Method pAUC_summaries()

AUC summaries

Usage

Benchmark$pAUC_summaries()

Method pAUC()

AUC summaries as table

Usage

Benchmark$pAUC()

Method summary_metrics()

Summary metrics: geometric mean of per-contrast AUC/AP scores

Computes per-contrast metrics (excluding the pooled "all" row), then returns the geometric mean of each metric and arithmetic mean of n_total, one row per score.

Usage

Benchmark$summary_metrics()

Returns

data.frame with columns: model_name, score, AUC, pAUC_10, pAUC_20, AP, pAP_50, pAP_80, n_total

Method get_confusion_FDRvsFDP()

FDR vs FDP data

Usage

Benchmark$get_confusion_FDRvsFDP()

Method n_confusion_FDRvsFDP()

nr of elements used to determine ROC curve

Usage

Benchmark$n_confusion_FDRvsFDP()

Method calibration_metrics()

FDR calibration metrics: measures how well the nominal FDR matches the actual false discovery proportion (FDP).

For each contrast and score, computes:

  • FDP_cal: mean absolute deviation mean(|FDP - FDR|) for FDR <= fdr_threshold (lower is better, 0 = perfect)

  • FDP_bias: mean signed deviation mean(FDP - FDR) (positive = anticonservative, negative = conservative)

Also returns an "average" row with arithmetic mean across contrasts.

Usage

Benchmark$calibration_metrics(fdr_threshold = 0.2)

Arguments

fdr_threshold

maximum nominal FDR to evaluate (default 0.2)

Returns

data.frame with columns: model_name, contrast, score, FDP_cal, FDP_bias

Method plot_FDRvsFDP()

plot FDR vs FDP data

Usage

Benchmark$plot_FDRvsFDP()

Returns

ggplot

Method plot_score_distribution()

plot distributions of scores

Usage

Benchmark$plot_score_distribution(score)

Arguments

score

the distribution of which scores to plot (list)

Returns

ggplot

Method plot_scatter()

plot intensity vs scores

Usage

Benchmark$plot_scatter(score)

Arguments

score

the distribution of which scores to plot (list)

Returns

ggplot

Method plot_precision_recall()

plot precision vs recall

Usage

Benchmark$plot_precision_recall(precision_lim = 0.7, recall_lim = 1)

Arguments

precision_lim

limit shown precision from

recall_lim

limit shown recall to

Returns

ggplot

Method to_summary_table()

Export benchmark summary as a flat data.frame

Returns one row per contrast x score with AUC metrics and counts.

Usage

Benchmark$to_summary_table(dataset = "unknown")

Arguments

dataset

dataset identifier string (default "unknown")

Returns

data.frame with columns: model_name, model_description, dataset, contrast, score, AUC, pAUC_10, pAUC_20, n_TP, n_TN, n_total, n_missing_contrasts

Method clone()

The objects of this class are cloneable with this method.

Usage

Benchmark$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples


dd <- dplyr::filter(prolfqua::prolfqua_data('data_benchmarkExample'), !is.na(statistic))
#> Registered S3 method overwritten by 'prolfqua':
#>   method         from    
#>   print.pheatmap pheatmap
dd <- dd |> dplyr::mutate(avgInt = (c1 + c2)/2)
ttd <- ionstar_bench_preprocess(dd)
medpol_benchmark <- make_benchmark(ttd$data,
benchmark = list(
list(score = "estimate", desc = TRUE),
list(score = "statistic", desc = TRUE),
list(score = "scaled.p.value", desc = TRUE)
),
    fcestimate = "estimate",
    model_description = "med. polish and lm. density",
    model_name = "prot_med_lm"
)
#> p.value
medpol_benchmark$plot_score_distribution(list(list(score = "estimate", xlim = c(-1,2) ),
 list(score = "statistic", xlim = c(-3,10) )))
#> Picking joint bandwidth of 0.023
#> Warning: Removed 20 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Picking joint bandwidth of 0.023
#> Warning: Removed 20 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Picking joint bandwidth of 0.37
#> Warning: Removed 172 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).

medpol_benchmark$get_confusion_benchmark()
#> # A tibble: 97,398 × 19
#>    protein_Id       scorecol TP    what     F_    T_     R   FDP TP_hits FN_hits
#>    <chr>               <dbl> <lgl> <chr> <int> <int> <int> <dbl>   <int>   <int>
#>  1 sp|P0AB61|YCIN_…     2.94 TRUE  esti… 13840  2393     1 0           1    2392
#>  2 sp|P13024|FDHE_…     2.30 TRUE  esti… 13840  2393     2 0           2    2391
#>  3 sp|P0ADG4|SUHB_…     2.10 TRUE  esti… 13840  2393     3 0           3    2390
#>  4 sp|Q15714|T22D1…     1.87 FALSE esti… 13840  2393     4 0.25        3    2390
#>  5 sp|P16456|SELD_…     1.70 TRUE  esti… 13840  2393     5 0.2         4    2389
#>  6 sp|Q9UBP9|GULP1…     1.63 FALSE esti… 13840  2393     6 0.333       4    2389
#>  7 sp|P76116|YNCE_…     1.60 TRUE  esti… 13840  2393     7 0.286       5    2388
#>  8 sp|P42641|OBG_E…     1.51 TRUE  esti… 13840  2393     8 0.25        6    2387
#>  9 sp|P32157|YIIM_…     1.50 TRUE  esti… 13840  2393     9 0.222       7    2386
#> 10 sp|O94763|RMP_H…     1.45 FALSE esti… 13840  2393    10 0.3         7    2386
#> # ℹ 97,388 more rows
#> # ℹ 9 more variables: FP_hits <int>, TN_hits <int>, PREC <dbl>, FPR <dbl>,
#> #   TPR <dbl>, ACC <dbl>, FDP_ <dbl>, model_name <chr>, contrast <chr>

benchmark <- make_benchmark(
  ttd$data,
  toscale =  c("moderated.p.value", "moderated.p.value.adjusted"),
  fcestimate = "estimate",
  benchmark = list(list(score = "estimate", desc = TRUE),
                   list(score = "statistic", desc = TRUE),
                   list(score = "scaled.moderated.p.value", desc = TRUE),
                   list(score = "scaled.moderated.p.value.adjusted", desc = TRUE)
  ),
  FDRvsFDP =
    list(list(score = "moderated.p.value", desc = FALSE),
         list(score = "moderated.p.value.adjusted", desc = FALSE)),
  model_description = "protein level measurments, lm model",
  model_name = "prot_lm"
)
#> moderated.p.value
#> moderated.p.value.adjusted

bb <- benchmark$pAUC_summaries()
benchmark$complete(FALSE)
benchmark$smc$summary
#> # A tibble: 4 × 2
#>   nr_missing protein_Id
#>        <int>      <int>
#> 1          0       4024
#> 2          1          8
#> 3          2         51
#> 4          3         11
benchmark$plot_score_distribution(list(list(score = "estimate", xlim = c(-1,2) ),
  list(score = "statistic", xlim = c(-3,10) )))
#> Picking joint bandwidth of 0.023
#> Warning: Removed 20 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Picking joint bandwidth of 0.023
#> Warning: Removed 20 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Picking joint bandwidth of 0.37
#> Warning: Removed 172 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).

benchmark$plot_score_distribution()
#> Picking joint bandwidth of 0.0231
#> Picking joint bandwidth of 0.0231
#> Picking joint bandwidth of 0.416
#> Picking joint bandwidth of 0.0575
#> Picking joint bandwidth of 0.0426



bb <- benchmark$get_confusion_FDRvsFDP()
xb <- dplyr::filter(bb, contrast ==  "dilution_(4.5/3)_1.5")
bb <- benchmark$get_confusion_benchmark()


benchmark$plot_ROC(xlim = 0.1)
#> Warning: Removed 102224 rows containing missing values or values outside the scale
#> range (`geom_path()`).

benchmark$plot_precision_recall()
#> Warning: Removed 107894 rows containing missing values or values outside the scale
#> range (`geom_path()`).


benchmark$plot_FDRvsFDP()

benchmark$plot_scatter(list(list(score = "estimate", ylim = c(-1,2) ),
  list(score = "statistic", ylim = c(-3,10) )))
#> Warning: Removed 20 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 20 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 172 rows containing missing values or values outside the scale
#> range (`geom_point()`).

benchmark$complete(FALSE)
benchmark$missing_contrasts()
#> $summary
#> # A tibble: 4 × 2
#>   nr_missing protein_Id
#>        <int>      <int>
#> 1          0       4024
#> 2          1          8
#> 3          2         51
#> 4          3         11
#> 
#> $nr_na
#> # A tibble: 4,094 × 3
#>    protein_Id                                   n nr_na
#>    <chr>                                    <int> <int>
#>  1 sp|A0A0U1RRL7|MMPOS_HUMAN~123~A0A0U1RRL7     4     2
#>  2 sp|A0AVT1|UBA6_HUMAN~124~A0AVT1              4     0
#>  3 sp|A0FGR8|ESYT2_HUMAN~125~A0FGR8             4     0
#>  4 sp|A0MZ66|SHOT1_HUMAN~126~A0MZ66             4     0
#>  5 sp|A1L0T0|ILVBL_HUMAN~127~A1L0T0             4     0
#>  6 sp|A1X283|SPD2B_HUMAN~128~A1X283             4     0
#>  7 sp|A2RRP1|NBAS_HUMAN~129~A2RRP1              4     0
#>  8 sp|A3KN83|SBNO1_HUMAN~130~A3KN83             4     0
#>  9 sp|A4D1E9|GTPBA_HUMAN~131~A4D1E9             4     0
#> 10 sp|A5PLL7|TM189_HUMAN~132~A5PLL7             4     0
#> # ℹ 4,084 more rows
#> 
stopifnot(nrow(benchmark$pAUC_summaries()$ftable$content) == 4 * (4 + 1))
benchmark$complete(TRUE)
stopifnot(nrow(benchmark$pAUC_summaries()$ftable$content) == 4 * (4+1))
missum <- benchmark$missing_contrasts()$summary
stopifnot(nrow(missum) == 4)
stopifnot(ncol(missum) == 2)
# returns number of statistics
stopifnot(nrow(benchmark$n_confusion_benchmark()) == 4 * (4 + 1))
stopifnot(nrow(benchmark$n_confusion_FDRvsFDP()) == 2 * (4 + 1))
benchmark$pAUC()
#> # A tibble: 20 × 9
#> # Groups:   contrast [5]
#>    contrast                what    AUC pAUC_10 pAUC_20    AP pAP_50 pAP_80 Name 
#>    <chr>                   <chr> <dbl>   <dbl>   <dbl> <dbl>  <dbl>  <dbl> <chr>
#>  1 all                     esti…  94.3    72.8    83.1  75.6   79.8   80.4 prot…
#>  2 all                     scal…  94.6    77.9    83.8  86.5   99.5   96.2 prot…
#>  3 all                     scal…  94.6    77.7    83.6  86.3   99.5   96.1 prot…
#>  4 all                     stat…  94.4    76.6    82.7  85.5   99.2   95.4 prot…
#>  5 dilution_(4.5/3)_1.5    esti…  94.6    84.4    88.7  86.0   90.9   91.9 prot…
#>  6 dilution_(4.5/3)_1.5    scal…  94.8    85.4    88.2  90.3   99.3   99.1 prot…
#>  7 dilution_(4.5/3)_1.5    scal…  94.8    85.4    88.2  90.3   99.3   99.1 prot…
#>  8 dilution_(4.5/3)_1.5    stat…  94.7    84.6    87.7  89.8   99.3   98.8 prot…
#>  9 dilution_(6/4.5)_1.3(3) esti…  93.7    74.8    83.9  75.1   76.7   80.1 prot…
#> 10 dilution_(6/4.5)_1.3(3) scal…  94.2    78.7    84.1  86.6   99.1   96.6 prot…
#> 11 dilution_(6/4.5)_1.3(3) scal…  94.2    78.7    84.1  86.6   99.1   96.6 prot…
#> 12 dilution_(6/4.5)_1.3(3) stat…  94.0    77.6    83.3  85.9   98.8   96.0 prot…
#> 13 dilution_(7.5/6)_1.25   esti…  93.6    64.4    78.6  66.8   66.6   70.3 prot…
#> 14 dilution_(7.5/6)_1.25   scal…  93.9    72.4    79.9  82.7   98.0   92.5 prot…
#> 15 dilution_(7.5/6)_1.25   scal…  94.0    72.5    80.0  82.8   98.0   92.6 prot…
#> 16 dilution_(7.5/6)_1.25   stat…  93.6    70.4    78.4  80.9   96.8   90.7 prot…
#> 17 dilution_(9/7.5)_1.2    esti…  95.1    66.5    81.1  67.9   64.9   69.5 prot…
#> 18 dilution_(9/7.5)_1.2    scal…  95.5    75.0    83.1  85.3   98.5   94.0 prot…
#> 19 dilution_(9/7.5)_1.2    scal…  95.5    75.1    83.2  85.3   98.5   94.0 prot…
#> 20 dilution_(9/7.5)_1.2    stat…  95.2    73.3    81.8  84.2   98.3   93.1 prot…