Abstract
A profile likelihood algorithm is proposed for quantitative
shotgun proteomics to infer the abundance ratios of
proteins from the abundance ratios of isotopically labeled
peptides derived from proteolysis. Previously, we have
shown that the estimation variability and bias of peptide
abundance ratios can be predicted from their profile
signal-to-noise ratios. Given multiple quantified peptides
for a protein, the profile likelihood algorithm probabilistically
weighs the peptide abundance ratios by their
inferred estimation variability, accounts for their expected
estimation bias, and suppresses contribution from outliers.
This algorithm yields maximum likelihood point
estimation and profile likelihood confidence interval
estimation of protein abundance ratios. This point estimator
is more accurate than an estimator based on the
average of peptide abundance ratios. The confidence
interval estimation provides an "error bar" for each
protein abundance ratio that reflects its estimation precision
and statistical uncertainty. The accuracy of the point
estimation and the precision and confidence level of the
interval estimation were benchmarked with standard
mixtures of isotopically labeled proteomes. The profile
likelihood algorithm was integrated into a quantitative
proteomics program, called ProRata, freely available at
www.MSProRata.org.
