Detect Drift in Analytical Data

Detects significant drift in feature responses across run order using trend tests and/or slope analysis.

Usage

measure_detect_drift(
  data,
  features,
  run_order_col = "run_order",
  sample_type_col = "sample_type",
  qc_type = NULL,
  method = c("slope", "mann_kendall", "both")
)

Arguments

data

A data frame containing the measurement data.

features

Character vector of feature column names to analyze.

run_order_col

Name of the run order column.

sample_type_col

Name of the sample type column.

qc_type

Value(s) identifying QC samples. If provided, analysis is restricted to QC samples.

method

Detection method:

• "slope" (default): Linear regression slope test

• "mann_kendall": Mann-Kendall trend test

• "both": Both methods

Value

A tibble with drift statistics for each feature:

• feature: Feature name

• slope: Regression slope (change per run)

• slope_pvalue: P-value for slope != 0

• percent_change: Total percent change over run

• significant: Logical, TRUE if drift is statistically significant

Examples

# Create data with drift
data <- data.frame(
  sample_type = rep("qc", 20),
  run_order = 1:20,
  feature1 = 100 + (1:20) * 0.5 + rnorm(20, sd = 2),
  feature2 = 50 + rnorm(20, sd = 1)  # No drift
)

measure_detect_drift(data, c("feature1", "feature2"))
#> # A tibble: 2 × 5
#>   feature    slope slope_pvalue percent_change significant
#>   <chr>      <dbl>        <dbl>          <dbl> <lgl>      
#> 1 feature1 0.351       0.000331         6.34   TRUE       
#> 2 feature2 0.00136     0.973            0.0519 FALSE