FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.Comment: To appear in IEEE VIS 2019 Short Paper

An unsupervised machine learning method for assessing quality of tandem mass spectra

<p>Abstract</p> <p>Background</p> <p>In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.</p> <p>Results</p> <p>This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.</p> <p>Conclusions</p> <p>Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.</p

MS-REDUCE: An ultrafast technique for reduction of Big Mass Spectrometry Data for high-throughput processing

Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big Mass Spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100x speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server. The developed tool and strategy will be available to wider proteomics and parallel computing community at the authors webpages after the paper is published

Protein inference based on peptides identified from tandem mass spectra

Protein inference is a critical computational step in the study of proteomics. It lays the foundation for further structural and functional analysis of proteins, based on which new medicine or technology can be developed. Today, mass spectrometry (MS) is the technique of choice for large-scale inference of proteins in proteomics. In MS-based protein inference, three levels of data are generated: (1) tandem mass spectra (MS/MS); (2) peptide sequences and their scores or probabilities; and (3) protein sequences and their scores or probabilities. Accordingly, the protein inference problem can be divided into three computational phases: (1) process MS/MS to improve the quality of the data and facilitate subsequent peptide identification; (2) postprocess peptide identification results from existing algorithms which match MS/MS to peptides; and (3) infer proteins by assembling identified peptides. The addressing of these computational problems consists of the main content of this thesis. The processing of MS/MS data mainly includes denoising, quality assessment, and charge state determination. Here, we discuss the determination of charge states from MS/MS data using low-resolution collision induced dissociation. Such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives. Hence, it is advantageous to determine the charge states of such spectra before the database search. A new approach is proposed to determine the charge states of low-resolution MS/MS. Four novel and discriminant features are adopted to describe each MS/MS and are used in Gaussian mixture model to distinguish doubly and triply charged peptides. The results have shown that this method can assign charge states to low-resolution MS/MS more accurately than existing methods. Many search engines are available for peptide identification. However, there is usually a high false positive rate (FPR) in the results. This can bring many false identifications to protein inference. As a result, it is necessary to postprocess peptide identification results. The most commonly used method is performing statistical analysis, which does not only make it possible to compare and combine the results from different search engines, but also facilitates subsequent protein inference. We proposed a new method to estimate the accuracy of peptide identification with logistic regression (LR) and exemplify it based on Sequest scores. Each peptide is characterized with the regularized Sequest scores ΔCn∗ and Xcorr∗. The score regularization is formulated as an optimization problem by applying two assumptions: the smoothing consistency between sibling peptides and the fitting consistency between original scores and new scores. The results have shown that the proposed method can robustly assign accurate probabilities to peptides and has a very high discrimination power, higher than that of PeptideProphet, to distinguish correctly and incorrectly identified peptides. Given identified peptides and their probabilities, protein inference is conducted by assembling these peptides. Existing methods to address this MS-based protein inference problem can be classified into two groups: twostage and one unified framework to identify peptides and infer proteins. In two-stage methods, protein inference is based on, but also separated from, peptide identification. Whereas in one unified framework, protein inference and peptide identification are integrated together. In this study, we proposed a unified framework for protein inference, and developed an iterative method accordingly to infer proteins based on Sequest peptide identification. The statistical analysis of peptide identification is performed with the LR previously introduced. Protein inference and peptide identification are iterated in one framework by adding a feedback from protein inference to peptide identification. The feedback information is a list of high-confidence proteins, which is used to update the adjacency matrix between peptides. The adjacency matrix is used in the regularization of peptide scores. The results have shown that the proposed method can infer more true positive proteins, while outputting less false positive proteins than ProteinProphet at the same FPR. The coverage of inferred proteins is also significantly increased due to the selection of multiple peptides for each MS/MS spectrum and the improvement of their scores by the feedback from the inferred proteins

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