Dataset Information

Mitigating the adverse impact of batch effects in sample pattern detection.

ABSTRACT: Motivation:It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data. Results:In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and construction of networks between subjects. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices and improved the performance of clustering algorithms. Availability and implementation:The R package is available at: https://github.com/tengfei-emory/QuantNorm. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Fei T

PROVIDER: S-EPMC6061843 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Mitigating the adverse impact of batch effects in sample pattern detection.

Fei Teng T Zhang Tengjiao T Shi Weiyang W Yu Tianwei T

Bioinformatics (Oxford, England) 20180801 15

<h4>Motivation</h4>It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.<h4>Results</h4>In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and ...[more]

PMID: 29506177

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Project description:BackgroundLarge sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.ResultsWe describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.ConclusionsResearchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.

Project description:BackgroundCaffeine containing energy drinks (EDs) are heavily consumed, particularly among young adults. The number of reports of caffeine intoxication from caffeinated EDs and problems related to caffeine dependence and withdrawal is increasing. The objective was to assess the knowledge and perceived beneficial effects of EDs consumers, to assess consumption patterns and determine the adverse effects experienced by different EDs consumer groups residing in Saudi Arabia.MethodsAn observational cross-sectional study with data from a randomly selected Saudi population was conducted during the period of January 15th, 2015, to April 15th, 2015, using a pre-tested 43-item questionnaire. The data were obtained and collected using interview questionnaires. Sociodemographic characteristics and data on EDs consumption patterns, the level of awareness among study subjects, and the purported benefits and reported adverse effects of EDs were collected. Frequency, percentage, and arithmetic means were calculated using Chi-square and ANOVA tests, and data with p?<?0.05 were considered significant.ResultsOf the 816 individuals invited to participate in the study, 783 participants responded and completed interviews, response rate was 96%. Consumers attributed the popularity of EDs to massive advertising media (46.7%) and their stimulating and invigorating effects (37.5%). EDs are consumed by subjects for their effects on fatigue reduction (64.6%), increased alertness and focus (75.8%), and assistance during long driving trips (75.7%). Study subjects reported suffering from adverse effects, including mainly diuresis (53.7%), palpitations (50.7%), insomnia (50.7%). Importantly, an inverse association was identified between knowledge of EDs and consumption rate, and a proportional association was identified between experienced adverse effects and consumption frequency. Lower knowledge scores were identified in daily consumers than in 1-3 times monthly consumers; higher adverse events were experienced by daily consumers than by 1-3 times monthly consumers. The majority of consumers (84.6%) recommended that authorities should regulate EDs consumption.ConclusionsExcessive EDs consumption is associated with an increased risk of experiencing several adverse events, which is commensurate with published studies. Increasing knowledge about EDs and their possible risks could decrease their consumption by the general public.

Project description:BACKGROUND:More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends. OBJECTIVE:This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI). METHODS:Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation. RESULTS:Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001). CONCLUSIONS:Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

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.

Dataset Information

Mitigating the adverse impact of batch effects in sample pattern detection.

Publications

Mitigating the adverse impact of batch effects in sample pattern detection.

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