SUMMARY: Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. AVAILABILITY AND IMPLEMENTATION: sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.

MOTIVATION: Omics studies aim to find significant changes due to biological or functional perturbation. However, gene and protein expression profiling experiments contain inherent technical variation. In discovery proteomics studies where the number of samples is typically small, technical variation plays an important role because it contributes considerably to the observed variation. Previous methods place both technical and biological variations in tightly integrated mathematical models that are difficult to adapt for different technological platforms. Our aim is to derive a statistical framework that allows the inclusion of a wide range of technical variability. RESULTS: We introduce a new method called the simulated linear test, or the s-test, that is easy to implement and easy to adapt for different models of technical variation. It generates virtual data points from the observed values according to a pre-defined technical distribution and subsequently employs linear modeling for significance analysis. We demonstrate the flexibility of the proposed approach by deriving a new significance test for quantitative discovery proteomics for which missing values have been a major issue for traditional methods such as the t-test. We evaluate the result on two label-free (phospho) proteomics datasets based on ion-intensity quantitation. AVAILABILITY AND IMPLEMENTATION: Available at http://www.oncoproteomics.nl/software/stest.html CONTACT: : [email protected].

MOTIVATION: Recent technology platforms in proteomics and genomics produce count data for quantitative analysis. Previous works on statistical significance analysis for count data have mainly focused on the independent sample setting, which does not cover the case where pairs of measurements are taken from individual patients before and after treatment. This experimental setting requires paired sample testing such as the paired t-test often used for continuous measurements. A state-of-the-art method uses a negative binomial distribution in a generalized linear model framework for paired sample testing. A paired sample design assumes that the relative change within each pair is constant across biological samples. This model can be used as an approximation to the true model in cases of heterogeneity of response in complex biological systems. We aim to specify the variation in response explicitly in combination with the inherent technical variation. RESULTS: We formulate the problem of paired sample test for count data in a framework of statistical combination of multiple contingency tables. In particular, we specify explicitly a random distribution for the effect with an inverted beta model. The technical variation can be modeled by either a standard Poisson distribution or an exponentiated Poisson distribution, depending on the reproducibility of the acquisition workflow. The new statistical test is evaluated on both proteomics and genomics datasets, showing a comparable performance to the state-of-the-art method in general, and in several cases where the two methods differ, the proposed test returns more reasonable p-values. AVAILABILITY: Available for download at http://www.oncoproteomics.nl/. CONTACT: [email protected].

MOTIVATION: Spectral count data generated from label-free tandem mass spectrometry-based proteomic experiments can be used to quantify protein's abundances reliably. Comparing spectral count data from different sample groups such as control and disease is an essential step in statistical analysis for the determination of altered protein level and biomarker discovery. The Fisher's exact test, the G-test, the t-test and the local-pooled-error technique (LPE) are commonly used for differential analysis of spectral count data. However, our initial experiments in two cancer studies show that the current methods are unable to declare at 95% confidence level a number of protein markers that have been judged to be differential on the basis of the biology of the disease and the spectral count numbers. A shortcoming of these tests is that they do not take into account within- and between-sample variations together. Hence, our aim is to improve upon existing techniques by incorporating both the within- and between-sample variations. RESULT: We propose to use the beta-binomial distribution to test the significance of differential protein abundances expressed in spectral counts in label-free mass spectrometry-based proteomics. The beta-binomial test naturally normalizes for total sample count. Experimental results show that the beta-binomial test performs favorably in comparison with other methods on several datasets in terms of both true detection rate and false positive rate. In addition, it can be applied for experiments with one or more replicates, and for multiple condition comparisons. Finally, we have implemented a software package for parameter estimation of two beta-binomial models and the associated statistical tests. AVAILABILITY AND IMPLEMENTATION: A software package implemented in R is freely available for download at http://www.oncoproteomics.nl/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper offers a sparse, multiscale representation of objects. It captures the object appearance by selection from a very large dictionary of Gaussian differential basis functions. The learning procedure results from the matching pursuit algorithm, while the recognition is based on polynomial approximation to the bases, turning image matching into a problem of polynomial evaluation. The method is suited for coarse recognition between objects and, by adding more bases, also for fine recognition of the object pose. The advantages over the common representation using PCA include storing sampled points for recognition is not required, adding new objects to an existing data set is trivial because retraining other object models is not needed, and significantly in the important case where one has to scan an image over multiple locations in search for an object, the new representation is readily available as opposed to PCA projection at each location. The experimental result on the COIL-100 data set demonstrates high recognition accuracy with real-time performance.