Chapter title |
Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?
|
---|---|
Chapter number | 7 |
Book title |
Research in Computational Molecular Biology
|
Published in |
Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005-), May 2017
|
DOI | 10.1007/978-3-319-56970-3_7 |
Pubmed ID | |
Book ISBNs |
978-3-31-956969-7, 978-3-31-956970-3
|
Authors |
Uri Keich, William Stafford Noble |
Abstract |
Estimating the false discovery rate (FDR) among a list of tandem mass spectrum identifications is mostly done through target-decoy competition (TDC). Here we offer two new methods that can use an arbitrarily small number of additional randomly drawn decoy databases to improve TDC. Specifically, "Partial Calibration" utilizes a new meta-scoring scheme that allows us to gradually benefit from the increase in the number of identifications calibration yields and "Averaged TDC" (a-TDC) reduces the liberal bias of TDC for small FDR values and its variability throughout. Combining a-TDC with "Progressive Calibration" (PC), which attempts to find the "right" number of decoys required for calibration we see substantial impact in real datasets: when analyzing the Plasmodium falciparum data it typically yields almost the entire 17% increase in discoveries that "full calibration" yields (at FDR level 0.05) using 60 times fewer decoys. Our methods are further validated using a novel realistic simulation scheme and importantly, they apply more generally to the problem of controlling the FDR among discoveries from searching an incomplete database. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 3 | 25% |
Other | 2 | 17% |
Student > Master | 2 | 17% |
Student > Bachelor | 2 | 17% |
Student > Doctoral Student | 1 | 8% |
Other | 1 | 8% |
Unknown | 1 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 3 | 25% |
Agricultural and Biological Sciences | 2 | 17% |
Chemical Engineering | 1 | 8% |
Mathematics | 1 | 8% |
Unspecified | 1 | 8% |
Other | 2 | 17% |
Unknown | 2 | 17% |