In shotgun proteomics, database searching of tandem mass spectra results in a great number of peptide-spectrum matches (PSMs), many of which are false positives. Quality control of PSMs is a multiple hypothesis testing problem, and the false discovery rate (FDR) or the local FDR (fdr) is the commonly used statistical confidence measure. Different from FDR, fdr evaluates the confidence of individual PSMs and thus is more desirable. Estimation of fdr can be achieved by decomposing the null and alternative distributions of PSM scores as long as the given data is sufficient. However, in many proteomic studies, only a group (subset) of PSMs, e.g. those with specific post-translational modifications, are of interest. The group can be very small, making the direct fdr estimation by the group data inaccurate, especially for the high-score area where the score threshold is taken. Using the whole set of PSMs to estimate the group fdr is inappropriate either because the null and/or alternative distributions of the group can be very different from the global ones. The first grouped fdr estimation algorithm, named transfer fdr, is proposed for quality control of small groups of peptide identifications.Transfer fdr derives the group null distribution through its empirical relationship with the global null distribution, and estimates the group alternative distribution, as well as the null proportion, using an iterative semi-parametric method. Validated on both simulated data and real proteomic data, transfer fdr showed remarkably higher accuracy than the direct global and separate fdr estimation methods.
The transfer fdr algorithm was implemented in Matlab. The source codes and the user guide are available at https://github.com/XinpeiYi/Transfer-fdr.
A test data can be downloaded here.
Xinpei Yi, Fuzhou Gong, Yan Fu. Grouped local false discovery rate estimation for peptide identification. 2019. Submitted.
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