An experimentally-derived measure of inter-replicate variation in reference samples: the same-same permutation methodology

Abstract

An experimentally-derived measure of inter-replicate variation in reference samples: 4 the same-same permutation methodology 5 6 Abstract 25The multiple testing problem is a well-known statistical stumbling block in high-26 throughput data analysis, where large scale repetition of statistical methods introduces 27 unwanted noise into the results. While approaches exist to overcome the multiple testing 28 problem, these methods focus on theoretical statistical clarification rather than incorporating 29 experimentally-derived measures to ensure appropriately tailored analysis parameters. Here, 30we introduce a method for estimating inter-replicate variability in reference samples for a 31 quantitative proteomics experiment using permutation analysis. This can function as a 32 modulator to multiple testing corrections such as the Benjamini-Hochberg ordered Q value 33 test. We refer to this as a 'same-same' analysis, since this method incorporates the use of six 34 biological replicates of the reference sample and determines, through non-redundant triplet 35 pairwise comparisons, the level of quantitative noise inherent within the system. The method 36 can be used to produce an experiment-specific Q value cut-off that achieves a specified false 37 discovery rate at the quantitation level, such as 1%. The same-same method is applicable to 38 any experimental set that incorporates six replicates of a reference sample. To facilitate 39 access to this approach, we have developed a same-same analysis R module that is freely 40 available and ready to use via the internet. 41 42Keywords: Label-free shotgun proteomics, false discovery rates, data quality, data 43 validation, statistics 44 45 46 47 48

Comparison of protein and peptide fractionation approaches in protein identification and quantification from Saccharomyces cerevisiae

“…Low stringency search data from peptide to spectrum matching outputs from X! Tandem for individual replicates was transformed into high stringency data by combining results from three biological replicate experiments into a single list of reproducibly identified proteins using Scrappy [34] and PeptideWitch [35]. The criteria for a reproducibly identified protein was that a protein must be present in all three replicates, with a minimum peptide spectral count of five [36][37][38].…”

PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

“…PeptideWitch not only produces high-stringency MSC and NSAF data from a range of PSM search engine outputs, it also expands greatly upon the functionality of the Scrappy R software [ 7 ] by producing additional statistical and graphical outputs for inter-and intra-replicate analysis, including Venn diagrams, volcano plots, heatmaps, and p -value histograms. PeptideWitch also incorporates a newly developed multiple testing corrections method [ 22 ] that utilises internal replicate permutation analysis of six replicates of a reference sample to produce an experimentally derived Benjamini-Hochberg (BH) corrected p -value threshold for subsequent application in control vs. treatment quantitation [ 23 , 24 , 25 ]. Lastly, PeptideWitch is freely web accessible so it can easily be used in any laboratory, since it does not require expertise in R (or Python) programming and implementation.…”