calculate_feature_CV {proBatch} | R Documentation

## Calculate CV distribution for each feature

### Description

Calculate CV distribution for each feature

### Usage

    calculate_feature_CV(df_long, sample_annotation = NULL,
      feature_id_col = "peptide_group_label",
      sample_id_col = "FullRunName", measure_col = "Intensity",
      batch_col = NULL, biospecimen_id_col = NULL, unlog = TRUE,
      log_base = 2, offset = 1)

### Arguments

`df_long` | data frame where each row is a single feature in a single sample. It minimally has a `sample_id_col`, a `feature_id_col` and a `measure_col`, but usually also an `m_score` (in OpenSWATH output result file). See `help("example_proteome")` for more details.
`sample_annotation` | data frame with: `sample_id_col` (this can be repeated as row names) biological covariates technical covariates (batches etc) . See `help("example_sample_annotation")`
`feature_id_col` | name of the column with feature/gene/peptide/protein ID used in the long format representation `df_long`. In the wide formatted representation `data_matrix` this corresponds to the row names.
`sample_id_col` | name of the column in `sample_annotation` table, where the filenames (colnames of the `data_matrix` are found).
`measure_col` | if `df_long` is among the parameters, it is the column with expression/abundance/intensity; otherwise, it is used internally for consistency.
`batch_col` | column in `sample_annotation` that should be used for batch comparison (or other, non-batch factor to be mapped to color in plots).
`biospecimen_id_col` | column in `sample_annotation` that defines a unique bio ID, which is usually a combination of conditions or groups. Tip: if such ID is absent, but can be defined from several columns, create new `biospecimen_id` column
`unlog` | (logical) whether to reverse log transformation of the original data
`log_base` | base of the logarithm for transformation
`offset` | small positive number to prevent 0 conversion to `-Inf`

### Value

data frame with Total CV for each feature & (optionally) per-batch CV

### Examples

    CV_df = calculate_feature_CV(example_proteome,
    sample_annotation = example_sample_annotation,
    measure_col = 'Intensity',
    batch_col = 'MS_batch')

[Package proBatch version 1.3.0 Index]