Tag-Count Analysis of Large-Scale Proteomic Data

Label-free quantitative
methods are advantageous in bottom-up (shotgun)
proteomics because they are robust and can easily be applied to different
workflows without additional cost. Both label-based and label-free
approaches are routinely applied to discovery-based proteomics experiments
and are widely accepted as semiquantitative. Label-free quantitation
approaches are segregated into two distinct approaches: peak-abundance-based
approaches and spectral counting (SpC). Peak abundance approaches
like MaxLFQ, which is integrated into the MaxQuant environment, require
precursor peak alignment that is computationally intensive and cannot
be routinely applied to low-resolution data. Not limited by these
constraints, SpC approaches simply use the number of peptide identifications
corresponding to a given protein as a measurement of protein abundance.
We show here that spectral counts from multidimensional proteomic
data sets have a mean-dispersion relationship that can be modeled
in edgeR. Furthermore, by simulating spectral counts, we show that
this approach can routinely be applied to large-scale discovery proteomics
data sets to determine differential protein expression.