Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

Computational approaches in high-throughput proteomics data analysis

Proteins are key components in biological systems as they mediate the signaling responsible for information processing in a cell and organism. In biomedical research, one goal is to elucidate the mechanisms of cellular signal transduction pathways to identify possible defects that cause disease. Advancements in technologies such as mass spectrometry and flow cytometry enable the measurement of multiple proteins from a system. Proteomics, or the large-scale study of proteins of a system, thus plays an important role in biomedical research. The analysis of all high-throughput proteomics data requires the use of advanced computational methods. Thus, the combination of bioinformatics and proteomics has become an important part in research of signal transduction pathways. The main objective in this study was to develop and apply computational methods for the preprocessing, analysis and interpretation of high-throughput proteomics data. The methods focused on data from tandem mass spectrometry and single cell flow cytometry, and integration of proteomics data with gene expression microarray data and information from various biological databases. Overall, the methods developed and applied in this study have led to new ways of management and preprocessing of proteomics data. Additionally, the available tools have successfully been used to help interpret biomedical data and to facilitate analysis of data that would have been cumbersome to do without the use of computational methods.Proteiineilla on tärkeä merkitys biologisissa systeemeissä sillä ne koordinoivat erilaisia solujen ja organismien prosesseja. Yksi biolääketieteellisen tutkimuksen tavoitteista on valottaa solujen viestintäreittejä ja niiden toiminnassa tapahtuvia muutoksia eri sairauksien yhteydessä, jotta tällaisia muutoksia voitaisiin korjata. Proteomiikka on proteiinien laajamittaista tutkimista solusta, kudoksesta tai organismista. Proteomiikan menetelmät kuten massaspektrometria ja virtaussytometria ovat keskeisiä biolääketieteellisen tutkimuksen menetelmiä, joilla voidaan mitata näytteestä samanaikaisesti useita proteiineja. Nykyajan kehittyneet proteomiikan mittausteknologiat tuottavat suuria tulosaineistoja ja edellyttävät laskennallisten menetelmien käyttöä aineiston analyysissä. Bioinformatiikan menetelmät ovatkin nousseet tärkeäksi osaksi proteomiikka-analyysiä ja viestintäreittien tutkimusta. Tämän tutkimuksen päätavoite oli kehittää ja soveltaa tehokkaita laskennallisia menetelmiä laajamittaisten proteomiikka-aineistojen esikäsittelyyn, analyysiin ja tulkintaan. Tässä tutkimuksessa kehitettiin esikäsittelymenetelmä massaspektrometria-aineistolle sekä automatisoitu analyysimenetelmä virtaussytometria-aineistolle. Proteiinitason tietoa yhdistettiin mittauksiin geenien transkriptiotasoista ja olemassaolevaan biologisista tietokannoista poimittuun tietoon. Väitöskirjatyö osoittaa, että laskennallisilla menetelmillä on keskeinen merkitys proteomiikan aineistojen hallinnassa, esikäsittelyssä ja analyysissä. Tutkimuksessa kehitetyt analyysimenetelmät edistävät huomattavasti biolääketieteellisen tiedon laajempaa hyödyntämistä ja ymmärtämistä

Pre-processing of tandem mass spectra using machine learning methods

Protein identification has been more helpful than before in the diagnosis and treatment of many diseases, such as cancer, heart disease and HIV. Tandem mass spectrometry is a powerful tool for protein identification. In a typical experiment, proteins are broken into small amino acid oligomers called peptides. By determining the amino acid sequence of several peptides of a protein, its whole amino acid sequence can be inferred. Therefore, peptide identification is the first step and a central issue for protein identification. Tandem mass spectrometers can produce a large number of tandem mass spectra which are used for peptide identification. Two issues should be addressed to improve the performance of current peptide identification algorithms. Firstly, nearly all spectra are noise-contaminated. As a result, the accuracy of peptide identification algorithms may suffer from the noise in spectra. Secondly, the majority of spectra are not identifiable because they are of too poor quality. Therefore, much time is wasted attempting to identify these unidentifiable spectra. The goal of this research is to design spectrum pre-processing algorithms to both speedup and improve the reliability of peptide identification from tandem mass spectra. Firstly, as a tandem mass spectrum is a one dimensional signal consisting of dozens to hundreds of peaks, and majority of peaks are noisy peaks, a spectrum denoising algorithm is proposed to remove most noisy peaks of spectra. Experimental results show that our denoising algorithm can remove about 69% of peaks which are potential noisy peaks among a spectrum. At the same time, the number of spectra that can be identified by Mascot algorithm increases by 31% and 14% for two tandem mass spectrum datasets. Next, a two-stage recursive feature elimination based on support vector machines (SVM-RFE) and a sparse logistic regression method are proposed to select the most relevant features to describe the quality of tandem mass spectra. Our methods can effectively select the most relevant features in terms of performance of classifiers trained with the different number of features. Thirdly, both supervised and unsupervised machine learning methods are used for the quality assessment of tandem mass spectra. A supervised classifier, (a support vector machine) can be trained to remove more than 90% of poor quality spectra without removing more than 10% of high quality spectra. Clustering methods such as model-based clustering are also used for quality assessment to cancel the need for a labeled training dataset and show promising results

Chemometrics for ion mobility spectrometry data:Recent advances and future prospects

Contains fulltext : 161386.pdf (publisher's version ) (Open Access)Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.

Filtering Methods for Mass Spectrometry-based Peptide Identification Processes

Tandem mass spectrometry (MS/MS) is a powerful tool for identifying peptide sequences. In a typical experiment, incorrect peptide identifications may result due to noise contained in the MS/MS spectra and to the low quality of the spectra. Filtering methods are widely used to remove the noise and improve the quality of the spectra before the subsequent spectra identification process. However, existing filtering methods often use features and empirically assigned weights. These weights may not reflect the reality that the contribution (reflected by weight) of each feature may vary from dataset to dataset. Therefore, filtering methods that can adapt to different datasets have the potential to improve peptide identification results. This thesis proposes two adaptive filtering methods; denoising and quality assessment, both of which improve efficiency and effectiveness of peptide identification. First, the denoising approach employs an adaptive method for picking signal peaks that is more suitable for the datasets of interest. By applying the approach to two tandem mass spectra datasets, about 66% of peaks (likely noise peaks) can be removed. The number of peptides identified later by peptide identification on those datasets increased by 14% and 23%, respectively, compared to previous work (Ding et al., 2009a). Second, the quality assessment method estimates the probabilities of spectra being high quality based on quality assessments of the individual features. The probabilities are estimated by solving a constraint optimization problem. Experimental results on two datasets illustrate that searching only the high-quality tandem spectra determined using this method saves about 56% and 62% of database searching time and loses 9% of high-quality spectra. Finally, the thesis suggests future research directions including feature selection and clustering of peptides

The FAST-AIMS Clinical Mass Spectrometry Analysis System

Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use