omicsTools (version 1.1.7)

flag_underexpressed_features: Flag Underexpressed Features in Samples Based on Blank Samples

Description

Flags features in samples based on their abundance in blank samples. If a feature is NA in the first blank sample, all samples for this feature are marked as TRUE. Otherwise, for each sample and feature, if the peak area is at least 10 times the area of the first blank sample, it is marked as TRUE, else FALSE. NA values in the samples remain unchanged.

Usage

flag_underexpressed_features(
  data,
  sample_id_col = "sample_id",
  feature_cols,
  threshold = 10
)

Value

A tibble with the same dimensions and column names as the input data, containing TRUE, FALSE, or NA based on the criteria.

Arguments

data

A tibble containing the MRM transition data.

sample_id_col

Name of the column containing sample ID information.

feature_cols

A vector of column names representing the features.

threshold

A numeric value representing the threshold multiplier (default is 10).

Author

Yaoxiang Li

Examples

if (FALSE) {
area_data <- tibble::tibble(
  sample_id = c(
    "011_Blank", "012_sample_002", "013_NIST_Plasma", "014_Blank",
    "015_sample_006", "016_sample_003"
  ),
  `2-Deoxyglucose-6-Phosphate_neg` = c(NA, 345423.96, NA, NA, 125889.80, 323818.25),
  `2-Oxoisoleucine_neg` = c(NA, 53004.06, 124669.80, NA, 23650.90, 118364.36),
  `3-(4-Hydroxyphenyl)propionate_neg` = c(NA, 53004.06, 124669.80, NA, 23650.90, 118364.36)
)
flagged_data <- flag_underexpressed_features(
  area_data,
  sample_id_col = "sample_id",
  feature_cols = names(area_data)[-1]
)
print(flagged_data)
}