Partition clustering of high dimensional low sample size data based on p-values

The field of proteomics is exploding with statistical problems waiting to be explored To obtain information on protein complexes, interactions between protein pairs is initially examined This exploration is performed using ‘bait-prey’ protein pull-down assays that use a protein affinity agent and an LC-MS/MS (liquid chromatography-tandem mass-spectrometry)-based protein identification method An experiment generates a protein association matrix wherein each column represents a sample from one bait protein, each row represents one prey protein and each cell contains a presence/absence association indicator The prey protein presence/absence pattern is assessed with a Likelihood Ratio Test (LRT) and simulated LRT p-values Fisher’s Exact Test and a conditional frequency distribution test using generating functions are also used to assess the prey protein observation pattern Based on the p-value, each prey protein is assigned a category (Specific or Non-Specific) and appraised with respect to the goal and design of the experiment The Bayes’ Odds is calculated for each prey-bait pair in the ‘Specific’ category to estimate the posterior probability that two proteins interact and compared to an approach used by Gilchrist et al [23] The method is illustrated using an experiment investigating protein complexes of Shewanella oneidensis MR-1 at the Proteomics Facility of Pacific Northwest National Laboratory (PNNL) The example analysis shows the results to be biologically sensible and more realistic than methods previously used to infer protein protein associations While inferring protein-protein associations is of great importance in proteomic studies, the quality of the data is of equal or greater importance Protein-protein interactions may be inferred incorrectly or not at all depending on the quality of the data Prior to this thesis, statistical quality control measures have not been incorporated into these experiments The implementation of traditional Individual/Moving Range (IMR) charts and cumulative sum (cusum) quality control methods for use with pull-down experiment data is studied These methodologies are illustrated using a standard protein mixture from PNNL The joint application of IMR and cusum charts promises to provide researchers with information on changes in the mean and variability of the data resulting from control samples run through the mass spectrometer process

New statistical methods for analyzing proteomics data from affinity isolation LC-MS/MS experiments

From affinity isolation lc-ms/ms experiments

http://link.springer.com/content/pdf/10.1007/3-540-29804-5_47.pdf

Toxicogenomics and Toxicoproteomics

In the classical approach to statistical hypothesis testing the role of the null hypothesis H0 and the alternative H1 is very asymmetric. Power, calculated from the distribution of the test statistic under H1, is treated as a theoretical construct that can be used to guide the choice of an appropriate test statistic or sample size, but power calculations do not explicitly enter the testing process in practice. In a significance test a decision to accept or reject H0 is driven solely by an examination of the strength of evidence against H0, summarized in the P-value calculated from the distribution of the test statistic under H0. A small P-value is taken to represent strong evidence against H0, but it need not necessarily indicate strong evidence in favour of H1. More recently, Moerkerke et al. (2006) have suggested that the special status of H0 is often unwarranted or inappropriate, and argue that evidence against H1 can be equally meaningful. They propose a balanced treatment of both H0 and H1 in which the classical P-value is supplemented by the P-value derived under H1. The alternative P-value is the dual of the null P-value and summarizes the evidence against a target alternative. Here we review how the dual P-values are used to assess the evidential tension between H0 and H1, and use decision theoretic arguments to explore a balanced hypothesis testing technique that exploits this evidential tension. The operational characteristics of balanced hypothesis tests is outlined and their relationship to conventional notions of optimal tests is laid bare. The use of balanced hypothesis tests as a conceptual tool is illustrated via model selection in linear regression and their practical implementation is demonstrated by application to the detection of cancer-specific protein markers in mass spectroscopy.

Dual P-Values, Evidential Tension and Balanced Tests

Figure 1. Silver stainedTwo-Dimensional PAGE ofa mousebrainproteome.Theannotated proteinspotsare identified byPeptide Mass Fingerprint Analysis. N the complete genomes of several organisms have been sequenced, Proteomics, the characterization of the complete protein complement of a cell, tissue, or organism (a proteome), has moved to the forefront of \"life sciences.\" But, if the complete genetic code is known, why study the proteins? Proteins define the function of cells, tissues and even organisms. Every cell contains equivalent genes, but their expression differs. In addition, each gene can give rise to multiple proteins, either through alternative splicing or post-translational modifications. Posttranslational modifications produce variations of the same protein that have different reactivities. Moreover, the genetically predicted translation does not tell how proteins interact with other proteins, DNA, and metabolites; or how their three-dimensional (3-D) structure affects activity. This protein information helps us understand healthy versus diseased states and develop effective treatments for diseases. For these reasons, the field of proteomics is booming. The most widelyadopted strategy for identifying numerous proteins from a cell is to load the cellular material on to a two-dimensional (2-D) gel. In the first dimension, their isoelectric point (pI) separates proteins across a pH gradient. In the second dimension on the gel,proteins are separated mainly based upon their molecular size as they migrate across an electric field gradient. The gel is then stained in order for the proteins to become visible.The 2-D map in Figure I shows numerous proteins and their approximate molecular weight. Because of the unrivaled ability to separate simultaneously and rapidly up to 10,000 proteins, 2-D gel electrophoresis has emerged as a key technology in proteomics. Mass Spectrometry

Mass Spectrometry and Bioinformatics in Proteomics

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Mass Spectrometry and Bioinformatics in Proteomics