ReSASC: A resampling-based algorithm to determine differential protein expression from spectral count data

Label-free methods for MS/MS quantification of protein expression are becoming more prevalent as instrument sensitivity increases. Spectral counts (SCs) are commonly used, readily obtained, and increase linearly with protein abundance; however, a statistical framework has been lacking. To accommodate the highly non-normal distribution of SCs, we developed ReSASC (resampling-based significance analysis for spectral counts), which evaluates differential expression between two conditions by pooling similarly expressed proteins and sampling from this pool to create permutation-based synthetic sets of SCs for each protein. At a set confidence level and corresponding p-value cutoff, ReSASC defines a new p-value, p', as the number of synthetic SC sets with P>P-cutoff divided by the total number of sets. We have applied ReSASC to two published SC data sets and found that ReSASC compares favorably with existing methods while being easy to operate and requiring only standard computing resources.