Normalization

Overview

Normalization is a crucial step in proteomics data analysis to correct for systematic biases and ensure comparability across samples. msmu provides several normalization methods to address different experimental designs and data characteristics.

log2_transform()

The log2_transform() function applies a log2 transformation to the quantification data in the specified modality. This transformation helps stabilize variance and make the data more normally distributed, which is beneficial for downstream statistical analyses. msmu assumes that log2_transform() is applied on basal level of data before applying normalization methods.

mdata = mm.pp.log2_transform(
    mdata,
    modality="psm",  # or "peptide", "protein"
    layer=None,      # optional; default None transforms .X
)

normalize() (or normalise())

The normalize() function offers multiple normalization methods: median (median), quantile (quantile), and total-intensity / constant-sum (total_sum, which rescales each sample so its summed intensity equals the median of the per-sample totals). Users can select the method that best suits their data and experimental design. All methods assume log2-transformed input. Normalization can also be performed independently within groups: pass group_obs (an adata.obs column, e.g. sample batch or type) and/or group_var (an adata.var column, e.g. "filename" for fractionated runs) to normalize within each group.

mdata = mm.pp.normalize(
    mdata,
    modality="psm",           # or "peptide", "protein"
    method="median",          # options: "median", "quantile", "total_sum"; default "median"
    group_obs=None,           # optional adata.obs column: normalize within each sample group
    group_var=None,           # optional adata.var column (e.g. "filename"): normalize within each feature group
    layer=None,               # optional; default None normalizes .X
)

adjust_ptm_by_protein()

The adjust_ptm_by_protein() function normalizes PTM site quantifications by their corresponding protein abundances from global proteome (if available) data to account for changes in protein expression levels.

For ridge regression method, PTM site intensities are adjusted based on the fitted values from a ridge regression model that predicts PTM abundance using protein abundance as a predictor variable. This approach helps to isolate PTM-specific changes from overall protein expression variations.

And for ratio method, PTM site intensities are normalized by calculating the ratio of PTM abundance to protein abundance, providing a simple measure of PTM changes relative to protein levels.

mdata = mm.pp.adjust_ptm_by_protein(
    mdata,
    global_mdata=global_mdata,   # MuData object for global proteome
    modality="phospho_site",     # ptm modality
    method="ridge",              # options: "ridge", "ratio". default "ridge"
    rescale=True,                # whether to rescale adjusted values. default True
    layer=None,                  # optional; default None adjusts .X
)