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")