Plot Power Sensitivity for Sequencing Designs

Source: R/ngs_plots.R

Plots prospective analytic power against equal fixed sequencing coverage for either a case-control sequencing trend design or a TDT1-NGS design. Exact values are obtained by repeated calls to the corresponding validated public design function.

plot_ngs_power(
  design = c("cc", "tdt"),
  coverage,
  seq_error,
  target_power = NULL,
  return_data = FALSE,
  ...
)

Arguments

design

Either "cc" for cc_ngs_power() or "tdt" for tdt_ngs_power().

coverage

Non-empty numeric vector of integer fixed sequencing depths. TDT1-NGS requires coverage of at least 2.

seq_error

Non-empty numeric vector of symmetric per-read sequencing error probabilities in \([0,0.5)\). Multiple values produce separate curves.

target_power

Optional finite scalar in \((0,1)\). If supplied, a dashed horizontal reference line is added without changing calculations.

return_data

Logical. If TRUE, return the exact plotting data rather than a ggplot object.

...

Fixed arguments passed to cc_ngs_power() or tdt_ngs_power(). For case-control designs, locus_het = TRUE permits a vector of pi values. TDT1-NGS accepts a fixed locus_het and heter_rate through ...; its public convention is heter_rate = 1 - pi.

Value

A ggplot object with exact results in plot$data, or a data frame when return_data = TRUE.

Details

Case-control output retains coverage, sequencing error, locus-homogeneity fraction, sample sizes, NCP, and power. At pi = 0, the exact null point is retained with NCP zero and power equal to alpha. TDT1-NGS uses raw read-count probabilities and does not use the case-control hard-call model.

Coverage is fixed and equal for the relevant study members. These plots do not model variable/BGE coverage distributions, cost optimization, or sample-specific depth. They introduce no simulation and delegate all statistical calculations to public PAWEH sequencing design APIs.

Examples

plot_ngs_power(
  design = "cc", coverage = c(4, 12, 20), seq_error = c(0, 0.01),
  N_case = 1000, alpha = 0.05, prev = 0.05, pd = 0.30, R2 = 1.8,
  MOI = "M", verbose = FALSE
)