Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.
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ABSTRACT:
SUBMITTER: Cuklina J
PROVIDER: S-EPMC8447595 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionLiquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.ObjectiveOur aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.MethodsAlgorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.ResultsIn authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').ConclusionsThe developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.
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Project description:Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.
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Project description:Despite the availability of batch effect correcting algorithms (BECA), no comprehensive tool that combines batch correction and evaluation of the results exists for microbiome datasets. This work outlines the Microbiome Batch Effects Correction Suite development that integrates several BECAs and evaluation metrics into a software package for the statistical computation framework R.
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Project description:The protein ontology (PRO) is designed as a formal and well-principled open biomedical ontologies (OBO) foundry ontology for proteins. The components of PRO extend from the classification of proteins, on the basis of evolutionary relationships at the full-length level, to the representation of the multiple protein forms of a gene, such as those resulting from alternative splicing, cleavage and/or posttranslational modifications, and protein complexes. As an ontology, PRO differs from a database in that it provides description about the protein types and their relationships. In addition, the representation of specific protein types, such as a phosphorylated protein form, allows precise definition of objects in pathways, complexes, or in disease modeling. This is useful for proteomics studies where isoforms and modified forms must be differentiated, and for biological pathway/network representation where the cascade of events often depends on a specific protein modification. PRO is manually curated starting with content derived from scientific literature. Only annotation with experimental evidence is included, and is in the form of relationship to other ontologies. In this tutorial, you will learn how to use the PRO resources to gain information about proteins of interest, such as finding conserved isoforms (ortho-isoforms), and different modified forms and their attributes. In addition, it will provide some details on how you can contribute to the ontology via the rapid annotation interface RACE-PRO.
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Project description:Two proteomic methods were recently introduced to globally map proteolytic cleavage events in biological systems, one that characterized proteolyzed proteins by differential gel migration (PROTOMAP) and the other by enzymatic tagging and enrichment of the nascent N-terminal peptides generated by proteolysis (Subtiligase). Both technologies were applied to apoptosis, and each uncovered hundreds of novel proteolytic events. An initial survey, however, revealed only minimal overlap in the two data sets. In this article, we perform an in-depth comparative analysis of the PROTOMAP and Subtiligase results that assimilates the complementary information acquired by each method. This analysis uncovered substantial agreement between the PROTOMAP and Subtiligase data sets, which in integrated form yield a highly enriched portrait of the proteome-wide impact of proteolysis in apoptosis. We discuss the respective strengths of each proteomic method and the potential for these technologies to expand the scope and sensitivity of large-scale studies of proteolysis in biological systems.
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Project description:Liquid state nuclear magnetic resonance is the only class of magnetic resonance experiments for which the simulation problem is solved comprehensively for spin systems of any size. This paper contains a practical walkthrough for one of the many available simulation packages - Spinach. Its unique feature is polynomial complexity scaling: the ability to simulate large spin systems quantum mechanically and with accurate account of relaxation, diffusion, chemical processes, and hydrodynamics. This paper is a gentle introduction written with a PhD student in mind.
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Project description:ObjectiveTo identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications.MethodsCT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction.ResultsFifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness-kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20).ConclusionCT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.Key points• The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).
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Project description:Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer's Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.
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Project description:Every year, Agroscope examines nearly 300,000 tubers for the presence of viruses, as regulated for the certification of seed potatoes intended for Swiss growers. Since 2016, this examination has been performed via RT-qPCR on dormant tubers directly after harvest. This method offers fast results and eliminates the need for the use of Rindite, which is a toxic and polluting gaseous compound previously used in Switzerland to break the dormancy of seed tubers. The implementation of this molecular analytical method for the routine diagnosis of regulated viruses makes it possible to conduct additional analyses via Illumina sequencing to assess the conformity of the primers and probes used with the sequences of the different viral isolates. This form of quality control in routine diagnosis is a source of information that can answer more fundamental scientific questions related to the epidemiology of viral strains related to certification. The datasets produced in this framework can also be used to explore the diversity of rare or unknown virus species in potato crops.Supplementary informationThe online version contains supplementary material available at 10.1007/s11540-021-09491-3.
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Project description:Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.
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