Oxidative Markers

Scope

Compute the glutathione redox ratio (GSH_GSSG_Ratio) from reduced (GSH) and oxidized (GSSG) glutathione, with robust input validation, missing-data handling, and clear output. No unit conversion is performed.

When to use this

You need a reliable marker of oxidative stress for clinical or research data.
Your dataset includes both reduced (GSH) and oxidized (GSSG) glutathione measurements.
You want to handle missing or non-numeric values gracefully, with options to omit, propagate, or error on missing data.

What you need (detailed inputs)

Argument	Purpose/Options	Details/Notes
data	Data frame/tibble with GSH and GSSG columns	Columns can be any name, mapped via col_map
col_map	Named list: GSH, GSSG → your column names	Defaults to identity mapping
units	No conversion performed; must match (e.g., µmol/L)	Mismatched units distort results
na_action	“keep” (propagate NA), “omit” (drop rows), “error” (abort if missing)	Controls missing data handling
verbose	TRUE/FALSE: print progress and summary	Useful for diagnostics

Example input table

GSH	GSSG
5	1
3	0.5
NA	2
2	0

Handling and expectations

Input validation: Non-numeric columns are coerced; warnings are issued if NAs are introduced. Non-finite values become NA.
Missing data:
- na_action = "keep": Output is NA for rows with missing GSH or GSSG.
- na_action = "omit": Rows with missing GSH or GSSG are dropped.
- na_action = "error": Function aborts if any required value is missing.
Division by zero: If GSSG is zero or non-finite, the ratio is set to NA with a warning.
Verbose output: Shows counts of omitted/kept rows and NA ratios for troubleshooting.
Troubleshooting: If you see many NAs, check for zero or non-numeric GSSG values, or mismatched column names in col_map.

Output

Returns a tibble with one column: GSH_GSSG_Ratio.
NA values indicate missing or invalid input, or division by zero.

Worked example 1: Basic usage

library(HealthMarkers)
library(tibble)

df <- tibble::tibble(GSH = c(5, 3, NA), GSSG = c(1, 0.5, 2))
oxidative_markers(
  data = df,
  col_map = list(GSH = "GSH", GSSG = "GSSG"),
  na_action = "keep"
)
#> # A tibble: 3 × 1
#>   GSH_GSSG_Ratio
#>            <dbl>
#> 1              5
#> 2              6
#> 3             NA

Interpretation: The third row yields NA because GSH is missing. Ratios are computed as GSH/GSSG for each row.

Worked example 2: Omit missing data

oxidative_markers(
  data = df,
  col_map = list(GSH = "GSH", GSSG = "GSSG"),
  na_action = "omit"
)
#> # A tibble: 2 × 1
#>   GSH_GSSG_Ratio
#>            <dbl>
#> 1              5
#> 2              6

Interpretation: Only complete rows are returned; the third row (NA GSH) is dropped.

Troubleshooting & common pitfalls

Unit mismatch: If GSH and GSSG are in different units, the ratio is meaningless. Always check units.
Zero or near-zero GSSG: Division by zero yields NA. Inspect your data for implausible values.
Non-numeric input: Non-numeric columns are coerced; check for unexpected NAs after coercion.
High missingness: If many NAs are present, check your input data and consider using na_action = "omit".
Column mapping errors: If you get a missing column error, check your col_map and input names.

Verbose diagnostics

old_opt <- options(healthmarkers.verbose = "inform")
oxidative_markers(
  data = df,
  col_map = list(GSH = "GSH", GSSG = "GSSG"),
  verbose = TRUE
)
#> oxidative_markers(): preparing inputs
#> oxidative_markers(): column map: GSH -> 'GSH', GSSG -> 'GSSG'
#> oxidative_markers(): results: GSH_GSSG_Ratio 2/3
#> # A tibble: 3 × 1
#>   GSH_GSSG_Ratio
#>            <dbl>
#> 1              5
#> 2              6
#> 3             NA
options(old_opt)

Tips for best results

Use na_action = "omit" for modeling or summary statistics to avoid NA propagation.
Set verbose = TRUE to get a summary of omitted/kept rows and NA counts.
Always verify assay units before computing ratios.
Spot-check a few rows manually to confirm expected output.
If you see many NAs, check for zero or non-numeric GSSG values.
For large datasets, run a summary (e.g., summary(df$GSH), summary(df$GSSG)) before analysis.
Use dplyr::filter() to pre-screen for implausible values if needed.

Validation notes

Spot-check: For a row, GSH_GSSG_Ratio = GSH / GSSG.
NA output means missing or invalid input, or division by zero.
Compare summary statistics (mean, SD) to expected ranges for your population.
If output is all NA, check for column mapping errors or all-zero GSSG.