normalize {proBatch} | R Documentation

## Data normalization methods

### Description

Normalization of raw (usually log-transformed) data. Normalization brings the samples to the same scale. Currently the following normalization functions are implemented: #'

1. Quantile normalization: 'quantile_normalize_dm()'. Quantile normalization of the data.

2. Median normalization: 'normalize_sample_medians_dm()'. Normalization by centering sample medians to global median of the data

Alternatively, one can call normalization function with 'normalize_data_dm()' wrapper.

### Usage

    quantile_normalize_dm(data_matrix)

    quantile_normalize_df(
      df_long,
      feature_id_col = "peptide_group_label",
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      no_fit_imputed = TRUE,
      qual_col = NULL,
      qual_value = 2,
      keep_all = "default"
    )

    normalize_sample_medians_dm(data_matrix)

    normalize_sample_medians_df(
      df_long,
      feature_id_col = "peptide_group_label",
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      no_fit_imputed = FALSE,
      qual_col = NULL,
      qual_value = 2,
      keep_all = "default"
    )

    normalize_data_dm(
      data_matrix,
      normalize_func = c("quantile", "medianCentering"),
      log_base = NULL,
      offset = 1
    )

    normalize_data_df(
      df_long,
      normalize_func = c("quantile", "medianCentering"),
      log_base = NULL,
      offset = 1,
      feature_id_col = "peptide_group_label",
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      no_fit_imputed = TRUE,
      qual_col = NULL,
      qual_value = 2,
      keep_all = "default"
    )

### Arguments

`data_matrix` | features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. See "example_proteome_matrix" for more details (to call the description, use `help("example_proteome_matrix")`)
`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.
`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.
`no_fit_imputed` | (logical) whether to use imputed (requant) values, as flagged in `qual_col` by `qual_value` for data transformation
`qual_col` | column to color point by certain value denoted by `color_by_qual_value`. Design with inferred/requant values in OpenSWATH output data, which means argument value has to be set to `m_score`.
`qual_value` | value in `qual_col` to color. For OpenSWATH data, this argument value has to be set to `2` (this is an `m_score` value for imputed values (requant values).
`keep_all` | when transforming the data (normalize, correct) - acceptable values: all/default/minimal (which set of columns be kept).
`normalize_func` | global batch normalization method ('quantile' or 'MedianCentering')
`log_base` | whether to log transform data matrix before normalization (e.g. 'NULL', '2' or '10')
`offset` | small positive number to prevent 0 conversion to `-Inf`

### Value

the data in the same format as input (`data_matrix` or `df_long`). For `df_long` the data frame stores the original values of `measure_col` in another column called "preNorm_intensity" if "intensity", and the normalized values in `measure_col` column.

### Examples

    #Quantile normalization:
    quantile_normalized_matrix <- quantile_normalize_dm(example_proteome_matrix)

    #Median centering:
    median_normalized_df <- normalize_sample_medians_df(example_proteome)

    #Transform the data in one go:
    quantile_normalized_matrix <- normalize_data_dm(example_proteome_matrix,
    normalize_func = "quantile", log_base = 2, offset = 1)

[Package proBatch version 1.10.0 Index]