process_ms: Process MS-like omics data

Description

This function performs common preprocessing steps for mass spectrometry (MS)-like omics datasets, including QC sample removal, zero-to-NA conversion, feature prevalence filtering, transformation, and feature-wise value imputation.

Usage

process_ms(
  X,
  remove_ids = NULL,
  min_prev = 0.8,
  rename_feat = TRUE,
  transform = c("none", "log", "sqrt"),
  log_base_num = 10,
  impute = c("none", "min_val", "QRILC"),
  min_val_factor = 1,
  platform = c("ms", "nmr"),
  seed = NULL,
  verbose = TRUE,
  ...
)

Value

A list:

X_names: Feature mapping original vs. new names.
X_processed: Processed numeric matrix.

Arguments

X: A numeric data frame or matrix (samples in rows, features in columns).
remove_ids: A regex or character vector to filter out rows in X (e.g. QCs). Set to NULL to skip.
min_prev: Numeric between 0 and 1. Minimum non-missing prevalence threshold. Zeros are first converted to NA.
rename_feat: Logical. If TRUE, features will be renamed as "feat_n" and original labels stored.
transform: One of "none", "log", or "sqrt".
log_base_num: Numeric logarithm base. Required if transform = "log".
impute: One of "none", "min_val", or "QRILC". Note: imputeLCMD::impute.QRILC() requires log-transformed data. Log-transform will be forced internally regardless of transform = setting.
min_val_factor: Numeric >= 1. Scaling factor for min value imputation.
platform: whether data was generated by mass spectrometry ("ms") or nuclear magnetic resonance spectroscopy ("nmr"), the latter allowing negative values in the matrix.
seed: Optional integer. If provided, sets the random seed for reproducible imputeLCMD::imputeQRILC() permutation results.
verbose: Logical. Show messages about the processing steps.
...: Extra arguments passed to imputeLCMD::impute.QRILC().

References

Lazar, C., Gatto, L., Ferro, M., Bruley, C., & Burger, T. (2016). Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. Journal of Proteome Research, 15(4), 1116–1125. tools:::Rd_expr_doi("10.1021/acs.jproteome.5b00981")

Wei, R., Wang, J., Su, M., Jia, E., Chen, S., Chen, T., & Ni, Y. (2018). Missing value imputation approach for mass spectrometry-based metabolomics data. Scientific Reports, 8, 663. tools:::Rd_expr_doi("10.1038/s41598-017-19120-0")

Examples

Run this code

X <- matrix(sample(c(0:10), size = 80, replace = TRUE),
            nrow = 20, ncol = 4,
            dimnames = list(paste0("sample", 1:20),
                            paste0("feat", 1:4)))

result <- process_ms(X, verbose = FALSE) # Generates NA warning