Scales protein abundance data by optionally converting from log2 scale to linear scale and/or applying TPM-like normalization for proteomics data comparison.
Arguments
- data
A numeric matrix containing protein abundance data with identifiers as row names and samples as columns.
- unlog
A logical value indicating whether to unlog the data (convert from log2 scale). Default is FALSE.
- tpm
A logical value indicating whether to apply TPM-like normalization (adapting Transcripts Per Million for proteomics). Default is FALSE.
Details
This function combines the functionality of
unlog2_data and convert_to_tpm to provide a
flexible way to handle common scaling operations for proteomics data.
See also
unlog2_data for just converting from log2 scale,
convert_to_tpm for just applying TPM-like normalization
Examples
# Create log2-transformed protein abundance matrix
log2_mat <- matrix(rnorm(12, mean = 10, sd = 2), nrow = 4, ncol = 3)
rownames(log2_mat) <- paste0("Protein", 1:4)
colnames(log2_mat) <- paste0("Sample", 1:3)
# Convert from log2 to linear scale only
linear_mat <- handle_scaling(log2_mat, unlog = TRUE, tpm = FALSE)
# Convert from log2 to linear scale and then apply TPM-like normalization
tpm_mat <- handle_scaling(log2_mat, unlog = TRUE, tpm = TRUE)
# Apply TPM-like normalization to already linear protein abundance data
linear_data <- matrix(abs(rnorm(12, mean = 500, sd = 200)), nrow = 4, ncol = 3)
tpm_only <- handle_scaling(linear_data, unlog = FALSE, tpm = TRUE)