Function to tabulate statistics for different methods of calculating the noise threshold

This function is used to tabulate and compare different combinations of similarity threshold and method to calculate the noise threshold for a given expression matrix.

calculate_noise_threshold_method_statistics(
  expression,
  similarity.threshold.sequence = 0.25,
  method.chosen.sequence = noisyr::get_methods_calculate_noise_threshold(),
  dump.stats = NULL,
  ...
)

Arguments

expression	either an expression summary (as calculated by calculate_expression_similarity_counts or calculate_expression_similarity_transcript), which should be a list with 3 slots: expression.matrix, expression.levels, expression.levels.similarity; alternatively, just an expression matrix; only density based methods are available for the latter case
similarity.threshold.sequence	similarity (correlation or inverse distance) threshold(s) to be used to find corresponding noise threshold; can be a single value or a numeric vector; the default, 0.25 is usually suitable for the Pearson correlation (the default similarity measure)
method.chosen.sequence	methods to use to calculate the noise thresholds, must be a subset of get_methods_calculate_noise_threshold; defaults to all
dump.stats	name of csv to export different thresholds calculated (optional)
...	other arguments (for the boxplot methods) passed to calculate_noise_threshold

Value

A tibble containing information on noise thresholds calculated using the input similarity thresholds and methods (optionally written in a csv file). The columns list the chosen method and similarity threshold, the minimum, mean, coefficient of variation, and maximum of the noise thresholds, and all the noise thresholds concatenated as a string.

See also

calculate_noise_threshold

Examples

expression.summary <- calculate_expression_similarity_counts(
    expression.matrix = matrix(1:100, ncol=5),
    method = "correlation_pearson",
    n.elements.per.window = 3)
#> The input matrix has 20 rows and 5 cols
#>     number of genes: 20
#>     number of samples: 5
#>     the number of elements per window is 3
#>     the step size is 1
#>     the selected similarity metric is correlation_pearson
#>   Working with sample 1
#>   Working with sample 2
#>   Working with sample 3
#>   Working with sample 4
#>   Working with sample 5
calculate_noise_threshold_method_statistics(expression.summary)
#> # A tibble: 10 x 8
#>    approach   method       similarity.thres~ noise.threshold.~ noise.threshold.~
#>    <chr>      <chr>                    <dbl>             <dbl>             <dbl>
#>  1 Density_b~ No_normalis~              0.25                 0                 0
#>  2 Density_b~ RPM_normali~              0.25                 0                 0
#>  3 Density_b~ Quantile_no~              0.25                 0                 0
#>  4 Line_plot  No_smoothing              0.25                 2                42
#>  5 Line_plot  loess10_smo~              0.25                 2                42
#>  6 Line_plot  loess25_smo~              0.25                 2                42
#>  7 Line_plot  loess50_smo~              0.25                 2                42
#>  8 Boxplot    Median                    0.25                 1                 1
#>  9 Boxplot    IQR                       0.25                 1                 1
#> 10 Boxplot    Quant5                    0.25                 1                 1
#> # ... with 3 more variables: noise.threshold.coefficient.of.variation <dbl>,
#> #   noise.threshold.max <dbl>, noise.thresholds.all <chr>