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Automatic Data Preprocessing

auto_clean.Rd

Perform automatic data preprocessing on glycoproteomics or glycomics data. This function applies an intelligent preprocessing pipeline that includes normalization, missing value handling, imputation, aggregation (for glycoproteomics data), and batch effect correction.

For glycomics data, this function calls these functions in sequence:

For glycoproteomics data, this function calls these functions in sequence:

Usage

auto_clean(
  exp,
  group_col = "group",
  batch_col = "batch",
  remove_preset = "discovery",
  batch_prop_threshold = 0.3,
  check_batch_confounding = TRUE,
  batch_confounding_threshold = 0.4,
  standardize_variable = TRUE
)

Arguments

exp

A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object.

group_col

The column name in sample_info for groups. Default is "group". Can be NULL when no group information is available.

batch_col

The column name in sample_info for batches. Default is "batch". Can be NULL when no batch information is available.

remove_preset

The preset for removing variables. Default is "discovery". Available presets:

  • "simple": remove variables with more than 50% missing values.

  • "discovery": more lenient, remove variables with more than 80% missing values, but ensure less than 50% of missing values in at least one group.

  • "biomarker": more strict, remove variables with more than 40% missing values, and ensure less than 60% of missing values in all groups.

batch_prop_threshold

The proportion of variables that must show significant batch effects to perform batch correction. Default is 0.3 (30%).

check_batch_confounding

Whether to check for confounding between batch and group variables. Default to TRUE.

batch_confounding_threshold

The threshold for Cramer's V to consider batch and group variables highly confounded. Only used when check_batch_confounding is TRUE. Default to 0.4.

standardize_variable

Whether to call glyexp::standardize_variable() after aggregation. Set to FALSE to skip network calls for faster testing. Default is TRUE.

Value

A modified container with the same subclass as exp.

See also

auto_normalize(), auto_remove(), auto_impute(), auto_aggregate(), auto_correct_batch_effect()

Examples

library(glyexp)
exp <- real_experiment
auto_clean(exp)
#> 
#> ── Removing variables with too many missing values ──
#> 
#> ℹ Applying preset "discovery"...
#> ℹ Total removed: 24 (0.56%) variables.
#> ✔ Variable removal completed.
#> 
#> ── Normalizing data ──
#> 
#> ℹ Normalization method: `normalize_median()`
#> ℹ Reason: default for "glycoproteomics".
#> ✔ Normalization completed.
#> 
#> ── Imputing missing values ──
#> 
#> ℹ Imputation method: `impute_min_prob()`
#> ℹ Reason: default for "glycoproteomics" with n_samples < 30.
#> ✔ Imputation completed.
#> 
#> ── Aggregating data ──
#> 
#> ℹ Aggregating to "gfs" level
#> ✔ Aggregation completed.
#> 
#> ── Normalizing data again ──
#> 
#> ℹ Normalization method: `normalize_median()`
#> ℹ Reason: default for "glycoproteomics".
#> ✔ Normalization completed.
#> 
#> ── Correcting batch effects ──
#> 
#> ℹ Batch column batch not found in sample_info. Skipping batch correction.
#> ✔ Batch correction completed.
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 3979 variables
#> ℹ Glycan type: N
#> ℹ Row data fields: protein <chr>, glycan_composition <comp>, glycan_structure <struct>, protein_site <int>, gene <chr>
#> ℹ Column data fields: group <fct>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>