title: Tag-Count Analysis of Large-Scale Proteomic Data.
abstract: 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.
authors: The Ohio State Biochemistry Graduate Program, The Ohio State University , Columbus, Ohio 43210, United States.|Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, Ohio 43210, United States.|Comprehensive Cancer Center, The Ohio State University , Columbus, Ohio 43210, United States.
authors: Owen E
authors: OE
authors: Branson
authors: The Ohio State Biochemistry Graduate Program, The Ohio State University , Columbus, Ohio 43210, United States.|Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, Ohio 43210, United States.|Comprehensive Cancer Center, The Ohio State University , Columbus, Ohio 43210, United States.
authors: Michael A
authors: MA
authors: Freitas
mesh_terms: D030562:Databases, Protein; D020869:Gene Expression Profiling; D010455:Peptides / Q000032:analysis; D011506:Proteins / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D057188:Workflow
chemical_list: D010455:Peptides; D011506:Proteins
keywords: R; bioconductor; edgeR; label-free; mass spectrometry; proteomics; spectral counting; tag-count