Normalization and missing value imputation for label-free LC-MS analysis

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data

Enhanced label-free discovery proteomics through improved data analysis and knowledge enrichment

Mass spectrometry (MS)-based proteomics has evolved into an important tool applied in fundamental biological research as well as biomedicine and medical research. The rapid developments of technology have required the establishment of data processing algorithms, protocols and workflows. The successful application of such software tools allows for the maturation of instrumental raw data into biological and medical knowledge. However, as the choice of algorithms is vast, the selection of suitable processing tools for various data types and research questions is not trivial. In this thesis, MS data processing related to the label-free technology is systematically considered. Essential questions, such as normalization, choice of preprocessing software, missing values and imputation, are reviewed in-depth. Considerations related to preprocessing of the raw data are complemented with exploration of methods for analyzing the processed data into practical knowledge. In particular, longitudinal differential expression is reviewed in detail, and a novel approach well-suited for noisy longitudinal high-througput data with missing values is suggested. Knowledge enrichment through integrated functional enrichment and network analysis is introduced for intuitive and information-rich delivery of the results. Effective visualization of such integrated networks enables the fast screening of results for the most promising candidates (e.g. clusters of co-expressing proteins with disease-related functions) for further validation and research. Finally, conclusions related to the prepreprocessing of the raw data are combined with considerations regarding longitudinal differential expression and integrated knowledge enrichment into guidelines for a potential label-free discovery proteomics workflow. Such proposed data processing workflow with practical suggestions for each distinct step, can act as a basis for transforming the label-free raw MS data into applicable knowledge.Massaspektrometriaan (MS) pohjautuva proteomiikka on kehittynyt tehokkaaksi työkaluksi, jota hyödynnetään niin biologisessa kuin lääketieteellisessäkin tutkimuksessa. Alan nopea kehitys on synnyttänyt erikoistuneita algoritmeja, protokollia ja ohjelmistoja datan käsittelyä varten. Näiden ohjelmistotyökalujen oikeaoppinen käyttö lopulta mahdollistaa datan tehokkaan esikäsittelyn, analysoinnin ja jatkojalostuksen biologiseksi tai lääketieteelliseksi ymmärrykseksi. Mahdollisten vaihtoehtojen suuresta määrästä johtuen sopivan ohjelmistotyökalun valinta ei usein kuitenkaan ole yksiselitteistä ja ongelmatonta. Tässä väitöskirjassa tarkastellaan leimaamattomaan proteomiikkaan liittyviä laskennallisia työkaluja. Väitöskirjassa käydään läpi keskeisiä kysymyksiä datan normalisoinnista sopivan esikäsittelyohjelmiston valintaan ja puuttuvien arvojen käsittelyyn. Datan esikäsittelyn lisäksi tarkastellaan datan tilastollista jatkoanalysointia sekä erityisesti erilaisen ekspression havaitsemista pitkittäistutkimuksissa. Väitöskirjassa esitellään uusi, kohinaiselle ja puuttuvia arvoja sisältävälle suurikapasiteetti-pitkittäismittausdatalle soveltuva menetelmä erilaisen ekspression havaitsemiseksi. Uuden tilastollisen menetelmän lisäksi väitöskirjassa tarkastellaan havaittujen tilastollisten löydösten rikastusta käytännön ymmärrykseksi integroitujen rikastumis- ja verkkoanalyysien kautta. Tällaisten funktionaalisten verkkojen tehokas visualisointi mahdollistaa keskeisten tulosten nopean tulkinnan ja kiinnostavimpien löydösten valinnan jatkotutkimuksia varten. Lopuksi datan esikäsittelyyn ja pitkittäistutkimusten tilastollisen jatkokäsittelyyn liittyvät johtopäätökset yhdistetään tiedollisen rikastamisen kanssa. Näihin pohdintoihin perustuen esitellään mahdollinen työnkulku leimaamattoman MS proteomiikkadatan käsittelylle raakadatasta hyödynnettäviksi löydöksiksi sekä edelleen käytännön biologiseksi ja lääketieteelliseksi ymmärrykseksi

Data analysis tools for mass spectrometry proteomics

ABSTRACT Proteins are large biomolecules which consist of amino acid chains. They differ from one another in their amino acid sequences, which are mainly dictated by the nucleotide sequence of their corresponding genes. Proteins fold into specific threedimensional structures that determine their activity. Because many of the proteins act as catalytes in biochemical reactions, they are considered as the executive molecules in the cells and therefore their research is fundamental in biotechnology and medicine. Currently the most common method to investigate the activity, interactions, and functions of proteins on a large scale, is high-throughput mass spectrometry (MS). The mass spectrometers are used for measuring the molecule masses, or more specifically, their mass-to-charge ratios. Typically the proteins are digested into peptides and their masses are measured by mass spectrometry. The masses are matched against known sequences to acquire peptide identifications, and subsequently, the proteins from which the peptides were originated are quantified. The data that are gathered from these experiments contain a lot of noise, leading to loss of relevant information and even to wrong conclusions. The noise can be related, for example, to differences in the sample preparation or to technical limitations of the analysis equipment. In addition, assumptions regarding the data might be wrong or the chosen statistical methods might not be suitable. Taken together, these can lead to irreproducible results. Developing algorithms and computational tools to overcome the underlying issues is of most importance. Thus, this work aims to develop new computational tools to address these problems. In this PhD Thesis, the performance of existing label-free proteomics methods are evaluated and new statistical data analysis methods are proposed. The tested methods include several widely used normalization methods, which are thoroughly evaluated using multiple gold standard datasets. Various statistical methods for differential expression analysis are also evaluated. Furthermore, new methods to calculate differential expression statistic are developed and their superior performance compared to the existing methods is shown using a wide set of metrics. The tools are published as open source software packages.TIIVISTELMÄ Proteiinit ovat aminohappoketjuista muodostuvia isoja biomolekyylejä. Ne eroavat toisistaan aminohappojen järjestyksen osalta, mikä pääosin määräytyy proteiineja koodaavien geenien perusteella. Lisäksi proteiinit laskostuvat kolmiulotteisiksi rakenteiksi, jotka osaltaan määrittelevät niiden toimintaa. Koska proteiinit toimivat katalyytteinä biokemiallisissa reaktioissa, niillä katsotaan olevan keskeinen rooli soluissa ja siksi myös niiden tutkimusta pidetään tärkeänä. Tällä hetkellä yleisin menetelmä laajamittaiseen proteiinien aktiivisuuden, interaktioiden sekä funktioiden tutkimiseen on suurikapasiteettinen massaspektrometria (MS). Massaspektrometreja käytetään mittaamaan molekyylien massoja – tai tarkemmin massan ja varauksen suhdetta. Tyypillisesti proteiinit hajotetaan peptideiksi massojen mittausta varten. Massaspektrometrillä havaittuja massoja verrataan tunnetuista proteiinisekvensseistä koottua tietokantaa vasten, jotta peptidit voidaan tunnistaa. Peptidien myötä myös proteiinit on mahdollista päätellä ja kvantitoida. Kokeissa kerätty data sisältää normaalisti runsaasti kohinaa, joka saattaa johtaa olennaisen tiedon hukkumiseen ja jopa pahimmillaan johtaa vääriin johtopäätöksiin. Tämä kohina voi johtua esimerkiksi näytteen käsittelystä johtuvista eroista tai mittalaitteiden teknisistä rajoitteista. Lisäksi olettamukset datan luonteesta saattavat olla virheellisiä tai käytetään datalle soveltumattomia tilastollisia malleja. Pahimmillaan tämä johtaa tilanteisiin, joissa tutkimuksen tuloksia ei pystytä toistamaan. Erilaisten laskennallisten työkalujen sekä algoritmien kehittäminen näiden ongelmien ehkäisemiseksi onkin ensiarvoisen tärkeää tutkimusten luotettavuuden kannalta. Tässä työssä keskitytäänkin sovelluksiin, joilla pyritään ratkaisemaan tällä osa-alueella ilmeneviä ongelmia. Tutkimuksessa vertaillaan yleisesti käytössä olevia kvantitatiivisen proteomiikan ohjelmistoja ja yleisimpiä datan normalisointimenetelmiä, sekä kehitetään uusia datan analysointityökaluja. Menetelmien keskinäiset vertailut suoritetaan useiden sellaisten standardiaineistojen kanssa, joiden todellinen sisältö tiedetään. Tutkimuksessa vertaillaan lisäksi joukko tilastollisia menetelmiä näytteiden välisten erojen havaitsemiseen sekä kehitetään kokonaan uusia tehokkaita menetelmiä ja osoitetaan niiden parempi suorituskyky suhteessa aikaisempiin menetelmiin. Kaikki tutkimuksessa kehitetyt työkalut on julkaistu avoimen lähdekoodin sovelluksina

Designing and sample size calculation in presence of heterogeneity in biological studies involving high-throughput data.

The designing and determination of sample size are important for conducting high-throughput biological experiments such as proteomics experiments and RNA-Seq expression studies, thus leading to better understanding of complex mechanisms underlying various biological processes. The variations in the biological data or technical approaches to data collection lead to heterogeneity for the samples under study. We critically worked on the issues of technical and biological heterogeneity. The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values (MVs) and data heterogeneity. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS. We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We found that the FFPE is better than that of FR for tissue storage. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design. We introduce a web application, PWST (Proteomics Workflow Standardization Tool) to standardize the proteomics workflow. The tool will be helpful in deciding the most suitable choice for each step and studying the variability associated with technical steps as well as the effects of continuous variables. We have used the special cases of general linear model - ANCOVA and ANOVA with fixed effects to study the effects due to various sources of variability. We introduce an interactive tool, “SATP: Statistical Analysis Tool for Proteomics”, for analyzing proteomics expression data that is scalable to large clinical proteomic studies. The user can perform differential expression analysis of proteomics data either at the protein or peptide level using multiple approaches. We have developed statistical approaches for calculating sample size for proteomics experiments under allocation and cost constraints. We have developed R programs and a shiny app “SSCP: Sample Size Calculator for Proteomics Experiment” for computing sample sizes. We have proposed statistical approaches for calculating sample size for RNA-Seq experiments considering allocation and cost. We have developed R programs and shiny apps to calculate sample size for conducting RNA-Seq experiments

A flexible and versatile framework for statistical design and analysis of quantitative mass spectrometry-based proteomic experiments

Quantitative mass spectrometry (MS)-based proteomics is an indispensable technology for biological and clinical research. As the proteomics field grows, MS-based proteomic workflows are becoming more complex and diverse. The accuracy and the throughput of the MS measurements and of the signal processing tools dramatically increased. However, many existing statistical tools and workflows have not followed the technological development. Therefore, there is a need for flexible statistical tools, which reflect diverse and complex workflows, are computationally efficient for large datasets, and maximize the reproducibility of the results. We propose a family of linear mixed effects models, and a split-plot view of the experimental design, that represent measurements from quantitative mass spectrometry-based proteomics. The whole plot part of the design reflects the structure of the biological variation of the experiment, such as case-control design, paired design, or time-course design. The subplot part of the design reflects the structure of the technological variation, such as fragmentation patterns, labeling strategy, and presence of multiple peptides per protein. We propose an estimation procedure that separately estimates the parameters of the subplot and the whole plot parts of the design, to maximize the flexibility of the model, increase the speed of the analysis, and facilitate the interpretation. The proposed modeling framework was validated using 9 controlled mixtures and 10 experimental datasets from targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS), where signals were extracted with multiple signal processing tools. We implemented the proposed method in the software package MSstats, which checks the correctness of the user input, recognizes arbitrary complex experimental design, visualizes the data and performs statistical modeling and inference. It is interoperable with other existing computational tools such as Skyline

Evaluation of the relevance and impact of kinase dysfunction in neurological disorders through proteomics and phosphoproteomics bioinformatics

Phosphorylation is an important post-translational modification that is involved in various biological processes and its dysregulation has in particular been linked to diseases of the central nervous system including neurological disorders. The present thesis characterizes alterations in the phosphoproteome and protein abundance associated with schizophrenia and Parkinson's disease, with the goal of uncovering the underlying disease mechanisms. To support this goal, I eventually created an automated analysis pipeline in R to streamline the analysis process of proteomics and phosphoproteomics data. Mass spectrometry (MS) technology is utilized to generate proteomics and phosphoproteomics data. Study I of the thesis demonstrates an automated R pipeline, PhosPiR, created to perform multi-level functional analyses of MS data after the identification and quantification of the raw spectral data. The pipeline does not require coding knowledge to run. It supports 18 different organisms, and provides analyses of MS intensity data from preprocessing, normalization and imputation, through to figure overviews, statistical analysis, enrichment analysis, PTM-SEA, kinase prediction and activity analysis, network analysis, hub analysis, annotation mining, and homolog alignment. The LRRK2-G2019S mutation, a frequent genetic cause of late onset Parkinson's disease, was investigated in Study II and III. One study investigated the mechanism of LRRK2-G2019S function in brain, and the other identified proteins with significantly altered overall translation patterns in sporadic and LRRK2-G2019S patient samples. Specifically, study II identified that LRRK2 is localized to the small 40S ribosomal subunit and that LRRK2 activity suppresses RNA translation, as validated in cell and animal models of Parkinson's disease and in patient cells. Study III utilized bio-orthogonal non-canonical amino acid tagging to label newly translated proteins in order to identify which proteins were affected by repressed translation in patient samples, using mass spectrometry analysis. The analysis revealed 33 and 30 nascent proteins with reduced synthesis in sporadic and LRRK2-G2019S Parkinson’s cases, respectively. The biological process "cytosolic signal recognition particle (SRP)-dependent co-translational protein targeting to membrane" was functionally significantly affected in both sporadic and LRRK2-G2019S Parkinson's, while "Tubulin/FTsz C-terminal domain superfamily network" was only significantly enriched in LRRK2-G2019S Parkinson’s cases. The findings were validated bytargeted proteomics and immunoblotting. Study IV is conducted to investigate the role of JNK1 in schizophrenia. Wild type and Jnk1-/- mice were used to analyze the phosphorylation profile using LC-MS/MS analysis. 126 proteins associated with schizophrenia were identified to overlap with the significantly differentially phosphorylated proteins in Jnk1-/- mice brain. The NMDAR trafficking pathway was found to be highly enriched, and surface staining of NMDAR subunits in neurons showed that surface expression of both subunits in Jnk1-/- neurons was significantly decreased. Further behavioral tests conducted with MK801 treatment have associated the Jnk1-/- molecular and behavioral phenotype with schizophrenia and neuropsychiatric disease

Urinary Protein Profiles in a Rat Model for Diabetic Complications

Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin- induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings

DART-ID increases single-cell proteome coverage.

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net