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Chi-squared correction for overdispersed bins

nipter_chi_correct.Rd

Identifies bins in the control group with excess variance (overdispersion) using a chi-squared test and downweights them. This reduces the influence of noisy bins on downstream Z-scores and NCV calculations.

Usage

nipter_chi_correct(
  sample,
  control_group,
  chi_cutoff = 3.5,
  include_sex = FALSE
)

Arguments

sample

A NIPTeRSample object (the test sample).

control_group

A NIPTeRControlGroup object.

chi_cutoff

Normalised chi-squared threshold. Bins with (chi - df) / sqrt(2*df) > chi_cutoff are corrected. Default 3.5.

include_sex

Logical; correct sex chromosomes as well? Default FALSE. Sex chromosome bins use the same chi-squared weights derived from autosomes.

Value

A list with two elements:

sample

The corrected NIPTeRSample.

control_group

The corrected NIPTeRControlGroup.

Details

The correction is applied simultaneously to both the test sample and all control group samples, maintaining consistency.

The algorithm follows NIPTeR's chi-squared correction:

  1. For each control sample, scale autosomal bin counts so that total reads match the overall mean across all control samples.

  2. Compute the expected count per bin (mean of scaled counts).

  3. Compute chi-squared per bin: \(\chi^2 = \sum_i (expected - scaled_i)^2 / expected\).

  4. Normalise: \(z = (\chi^2 - df) / \sqrt{2 \cdot df}\) where \(df = n_{controls} - 1\).

  5. For bins where \(z > \text{chi\_cutoff}\): divide reads by \(\chi^2 / df\).

See also

nipter_gc_correct(), nipter_z_score(), nipter_as_control_group()

Examples

if (FALSE) { # \dontrun{
result <- nipter_chi_correct(sample, control_group)
sample_corrected <- result$sample
cg_corrected     <- result$control_group
} # }