buildBench: Execute BenchDesign
In SummarizedBenchmark: Classes and methods for performing benchmark comparisons

Description Usage Arguments Details Value Author(s) See Also Examples

Function to evaluate methods defined in a BenchDesign on a supplied data set to generate a SummarizedBenchmark of benchmarking results. In addition to the results of applying each method on the data, the returned SummarizedBenchmark also includes metadata for the methods in the colData of the returned object, metadata for the data in the rowData, and session information in the metadata.

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buildBench(bd, data = NULL, truthCols = NULL, ftCols = NULL,
  sortIDs = FALSE, keepData = FALSE, catchErrors = TRUE,
  parallel = FALSE, BPPARAM = bpparam())

`bd`	`BenchDesign` object.
`data`	Data set to be used for benchmarking, will take priority over data set specified to `BenchDesign` object. Ignored if `NULL`. (default = `NULL`)
`truthCols`	Character vector of column names in data set corresponding to ground truth values for each assay. If specified, column will be added to the `groundTruth` DataFrame of the returned `SummarizedBenchmark` object. If the `BenchDesign` includes only a single assay, the same name will be used for the assay. If the `BenchDesign` includes multiple assays, to map data set columns with assays, the vector must have names corresponding to the assay names specified to the `post` parameter at each `addMethod` call. (default = `NULL`)
`ftCols`	Vector of character names of columns in data set that should be included as feature data (row data) in the returned `SummarizedBenchmark` object. (default = `NULL`)
`sortIDs`	Whether the output of each method should be merged and sorted using IDs. See Details for more information. (default = `FALSE`)
`keepData`	Whether to store the data as part of the `BenchDesign` slot of the returned `SummarizedBenchmark` object. If `FALSE`, a MD5 hash of the data will be stored with the `BenchDesign` slot. (default = `FALSE`)
`catchErrors`	logical whether errors produced by methods during evaluation should be caught and printed as a message without stopping the entire build process. (default = `TRUE`)
`parallel`	Whether to use parallelization for evaluating each method. Parallel execution is performed using BiocParallel. Parameters for parallelization should be specified with `register` or through the `BPPARAM` parameter. (default = `FALSE`)
`BPPARAM`	Optional `BiocParallelParam` instance to be used when `parallel` is `TRUE`. If not specified, the default instance from the parameter registry is used.

Parallelization is performed across methods. Therefore, there is currently no benefit to specifying more cores than the total number of methods in the BenchDesign object.

By default, errors thrown by individual methods in the BenchDesign are caught during evaluation and handled in a way that allows buildBench to continue running with the other methods. The error is printed as a message, and the corresponding column in the returned SummarizedBenchmark object is set to NA. Since many benchmarking experiments can be time and computationally intensive, having to rerun the entire analysis due to a single failed method can be frustrating. Default error catching was included to alleviate these frustrations. However, if this behavior is not desired, setting catchErrors = FALSE will turn off error handling.

If sortIDs = TRUE, each method must return a named vector or list. The names will be used to align the output of each method in the returned SummarizedBenchmark. Missing values from each method will be set to NA. This can be useful if the different methods return overlapping, but not identical, results. If truthCols is also specified, and sorting by IDs is necessary, rather than specifying sortIDs = TRUE, specify the string name of a column in the data to use to sort the method output to match the order of truthCols.

When a method specified in the BenchDesign does not have a postprocessing function specified to post =, the trivial base::identity function is used as the default postprocessing function.

SummarizedBenchmark object.

Patrick Kimes

updateBench

## with toy data.frame
df <- data.frame(pval = rnorm(100))
bench <- BenchDesign(data = df)

## add methods
bench <- addMethod(bench, label = "bonf", func = p.adjust,
                   params = rlang::quos(p = pval, method = "bonferroni"))
bench <- addMethod(bench, label = "BH", func = p.adjust,
                   params = rlang::quos(p = pval, method = "BH"))

## evaluate benchmark experiment
sb <- buildBench(bench)

## evaluate benchmark experiment w/ data sepecified
sb <- buildBench(bench, data = df)