Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.
Ontology highlight
ABSTRACT:
SUBMITTER: Oberg AL
PROVIDER: S-EPMC2528956 | biostudies-literature | 2008 Jan
REPOSITORIES: biostudies-literature
Publications
Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.
Oberg Ann L AL Mahoney Douglas W DW Eckel-Passow Jeanette E JE Malone Christopher J CJ Wolfinger Russell D RD Hill Elizabeth G EG Cooper Leslie T LT Onuma Oyere K OK Spiro Craig C Therneau Terry M TM Bergen H Robert HR
Journal of proteome research 20080104 1
Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol. ...[more]
Similar Datasets
Project description:Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.
| S-EPMC3166364 | biostudies-literature
Project description:Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.
| S-EPMC9860394 | biostudies-literature
Project description:ObjectiveTraditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.MethodsWe use a list of keywords specific to the phenotype of interest to generate noisy labeled training data. We train L1 penalized logistic regression models for a chronic and an acute disease and evaluate the performance of the models against a gold standard.ResultsOur models for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.90, 0.89, and 0.86, 0.89, respectively. Local implementations of the previously validated rule-based definitions for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.96, 0.92 and 0.84, 0.87, respectively.We have demonstrated feasibility of learning phenotype models using imperfectly labeled data for a chronic and acute phenotype. Further research in feature engineering and in specification of the keyword list can improve the performance of the models and the scalability of the approach.ConclusionsOur method provides an alternative to manual labeling for creating training sets for statistical models of phenotypes. Such an approach can accelerate research with large observational healthcare datasets and may also be used to create local phenotype models.
| S-EPMC5070523 | biostudies-literature
Project description:Touch spray, a spray-based ambient in situ ionization method, uses a small probe, e.g. a teasing needle to pick up sample and the application of voltage and solvent to cause field-induced droplet emission. Compounds extracted from the microsample are incorporated into the sprayed micro droplets. Performance tests include disease state of tissue, microorganism identification, and therapeutic drug quantitation. Chemical derivatization is performed simultaneously with ionization.
| S-EPMC4063212 | biostudies-literature
Project description:We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.
| S-EPMC3983037 | biostudies-literature
Project description:Amino acid sequences of immunodominant domains of hemagglutinin (HA) on the surface of influenza A virus (IAV) evolve rapidly, producing viral variants. HA mediates receptor recognition, binding and cell entry, and serves as the target for IAV vaccines. Glycosylation, a post-translational modification that places large branched polysaccharide molecules on proteins, can modulate the function of HA and shield antigenic regions allowing for viral evasion from immune responses. Our previous work showed that subtle changes in the HA protein sequence can have a measurable change in glycosylation. Thus, being able to quantitatively measure glycosylation changes in variants is critical for understanding how HA function may change throughout viral evolution. Moreover, understanding quantitatively how the choice of viral expression systems affects glycosylation can help in the process of vaccine design and manufacture. Although IAV vaccines are most commonly expressed in chicken eggs, cell-based vaccines have many advantages, and the adoption of more cell-based vaccines would be an important step in mitigating seasonal influenza and protecting against future pandemics. Here, we have investigated the use of data-independent acquisition (DIA) mass spectrometry for quantitative glycoproteomics. We found that DIA improved the sensitivity of glycopeptide detection for four variants of A/Switzerland/9715293/2013 (H3N2): WT and mutant, each expressed in embryonated chicken eggs and Madin-Darby canine kidney cells. We used the Tanimoto similarity metric to quantify changes in glycosylation between WT and mutant and between egg-expressed and cell-expressed virus. Our DIA site-specific glycosylation similarity comparison of WT and mutant expressed in eggs confirmed our previous analysis while achieving greater depth of coverage. We found that sequence variations and changing viral expression systems affected distinct glycosylation sites of HA. Our methods can be applied to track glycosylation changes in circulating IAV variants to bolster genomic surveillance already being done, for a more complete understanding of IAV evolution.
| S-EPMC9593740 | biostudies-literature
Project description:MotivationData normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.ResultsTo robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples.Availabilityand implementationhttps://github.com/mwgrassgreen/RobNorm.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8098025 | biostudies-literature
Project description:The conceptually simple step from cross-linking/mass spectrometry (CLMS) to quantitative cross-linking/mass spectrometry (QCLMS) is compounded by technical challenges. Currently, quantitative proteomics software is tightly integrated with the protein identification workflow. This prevents automatically quantifying other m/z features in a targeted manner including those associated with cross-linked peptides. Here we present a new release of MaxQuant that permits starting the quantification process from an m/z feature list. Comparing the automated quantification to a carefully manually curated test set of cross-linked peptides obtained by cross-linking C3 and C3b with BS(3) and isotope-labeled BS(3)-d4 revealed a number of observations: (1) Fully automated process using MaxQuant can quantify cross-links in our reference data set with 68% recall rate and 88% accuracy. (2) Hidden quantification errors can be converted into exposed failures by label-swap replica, which makes label-swap replica an essential part of QCLMS. (3) Cross-links that failed during automated quantification can be recovered by semi-automated re-quantification. The integrated workflow of MaxQuant and semi-automated assessment provides the maximum of quantified cross-links. In contrast, work on larger data sets or by less experienced users will benefit from full automation in MaxQuant.
| S-EPMC4974350 | biostudies-literature
Project description:Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.
| S-EPMC10806275 | biostudies-literature
Project description:Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3? dynamic interaction network and prostate cancer glycoproteome.The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics.
| S-EPMC4630088 | biostudies-literature