Evaluates how pre-filtering thresholds affect the number of discoveries, helping users select an informed minimum count.

Usage

determine_filter_threshold(
  se_ln,
  count_thresholds = c(0, 1, 5, 10, 20, 50, 100, 200, 500),
  assay_name = "counts",
  ref_level = "Tconv",
  group_var = "cell_type",
  p_threshold = 0.05
)

Arguments

se_ln: A SummarizedExperiment object
count_thresholds: Numeric vector of thresholds to evaluate (default: c(0, 1, 5, 10, 20, 50, 100, 200, 500))
assay_name: Name of assay to use (default: "counts")
ref_level: Sets the reference for comparison (default: "Tconv")
group_var: metadata for grouping (default: "cell_type")
p_threshold: Significance threshold for adjusted p-value(default: 0.05)

Value

A dataframe summarizing the number of significant genes per threshold

Examples

data(example_se)

# Step 1: Evaluate how model preforms using different threshold values
example_se_filtering_assessment <- determine_filter_threshold(
  se_ln = example_se,
  count_thresholds = c(0, 1, 5, 10, 20, 50, 100, 200, 500),
  assay_name = "counts",
  ref_level = "Tconv",
  group_var = "cell_type",
  p_threshold = 0.05
)
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.

# Step 2: Choose the optimal min count threshold.
  # Insert into min_count_per_group (default: 10)