normalize_batch: Noramalize a Data Table by Group and Batch

In blaserlab/blaseRtools: R Tools for Blaser Lab Data Analysis

Noramalize a Data Table by Group and Batch

Description

Often you will have a data table with repeats or batches of the same experiment. An effective way to control for batch effects is to normalize the data from each batch to a control group present in all of the experiments. To use this function, provide such a data table, identify the column holding the experimental group data, the identity of the control group to normalize by, the column holding the batch data, and the column holding the numerical data to normalize. Also select the function to average by (mean or median). The function will return the data table with three new columns: the average of the control group by batch, fold change of each observation relative to the batch average and the log2-transformed fold change. This function is pipe-friendly.

Usage

normalize_batch(
  data,
  group_col,
  norm_group,
  batch_col,
  data_col,
  fun = c("mean", "median")
)

Arguments

data	a tibble
group_col	the column containing the experimental group identifier
norm_group	the experimental group you want to normalize to across batches
batch_col	the column containing the batch identifier
data_col	the column with your data
fun	averaging function to use, Default: c("mean", "median")

Value

A tibble with new columns indicating batch normalization group average, fold change for each observation relative to the batch average and log2 fold change

See Also

arg_match cli_abort filter, group_by, summarise, select, mutate-joins, mutate

blaserlab/blaseRtools documentation built on April 14, 2025, 6:04 p.m.