Simulation-based comprehensive study of batch effects in metabolomics studies

AbstractBatch effects will influence the interpretation of metabolomics data. In order to avoid misleading results, batch effects should be corrected and normalized prior to statistical analysis. Metabolomics studies are usually performed without targeted compounds (e.g., internal standards) and it is a challenging task to validate batch effects correction methods. In addition, statistical properties of metabolomics data are quite different from genomics data (where most of the currently used batch correction methods have originated from). In this study, we firstly analyzed already published metabolomics datasets so as to summarize and discuss their statistical properties. Then, based on available datasets, we developed novel statistical properties-based in silico simulations of metabolomics peaks’ intensity data so as to analyze the influence of batch effects on metabolomic data with the use of currently available batch correction strategies. Overall, 252000 batch corrections on 14000 different in silico simulated datasets and related differential analyses were performed in order to evaluate and validate various batch correction methods. The obtained results indicate that log transformations strongly influence the performance of all investigated batch correction methods. False positive rates increased after application of batch correction methods with almost no improvement on true positive rates among the analyzed batch correction methods. Hence, in metabolomic studies it is recommended to implement preliminary experiments to simulate batch effects from real data in order to select adequate batch correction method, based on a given distribution of peaks intensity. The presented study is reproducible and related R package mzrtsim software can be found online (https://github.com/yufree/mzrtsim).

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powmic: an R package for power assessment in microbiome case–control studies

Bioinformatics ◽

10.1093/bioinformatics/btaa197 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3563-3565

Author(s):

Li Chen

Keyword(s):

Power Analysis ◽

Real Data ◽

Analytical Form ◽

R Package ◽

Case Control ◽

Supplementary Information ◽

Metagenomic Sequencing ◽

Case Control Studies ◽

Simulation Based ◽

Over Dispersion

Abstract Summary Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a case–control study for identifying differentially abundant (DA) microbes. However, the complexity of microbial data characteristics, such as excessive zeros, over-dispersion, compositionality, intrinsically microbial correlations and variable sequencing depths, makes the power analysis particularly challenging because the analytical form is usually unavailable. Here, we develop a simulation-based power assessment strategy and R package powmic, which considers the complexity of microbial data characteristics. A real data example demonstrates the usage of powmic. Availability and implementation powmic R package and online tutorial are available at https://github.com/lichen-lab/powmic. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits

Bioinformatics ◽

10.1093/bioinformatics/btz667 ◽

2019 ◽

Vol 36 (3) ◽

pp. 842-850 ◽

Cited By ~ 4

Author(s):

Cheng Peng ◽

Jun Wang ◽

Isaac Asante ◽

Stan Louie ◽

Ran Jin ◽

...

Keyword(s):

Real Data ◽

R Package ◽

Integrative Model ◽

Supplementary Information ◽

Phenotypic Traits ◽

Omics Data ◽

Data Types ◽

Specific Effects ◽

Metabolomic Data ◽

Future Prediction

Abstract Motivation Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups. Results We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data. Availability and Implementation The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus). Supplementary information Supplementary materials are available at Bioinformatics online.

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A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification

10.1101/2022.01.14.476352 ◽

2022 ◽

Author(s):

Stephen Coleman ◽

Xaquin Castro Dopico ◽

Gunilla B Karlsson Hedestam ◽

Paul DW Kirk ◽

Chris Wallace

Keyword(s):

Mixture Model ◽

R Package ◽

Biological Data ◽

Enzyme Linked Immunosorbent Assay ◽

Batch Effects ◽

Classification Problems ◽

Batch Correction ◽

Dimensional Classification ◽

Class Labels ◽

Low Dimensional

Systematic differences between batches of samples present significant challenges when analysing biological data. Such batch effects are well-studied and are liable to occur in any setting where multiple batches are assayed. Many existing methods for accounting for these have focused on high-dimensional data such as RNA-seq and have assumptions that reflect this. Here we focus on batch-correction in low-dimensional classification problems. We propose a semi-supervised Bayesian generative classifier based on mixture models that jointly predicts class labels and models batch effects. Our model allows observations to be probabilistically assigned to classes in a way that incorporates uncertainty arising from batch effects. We explore two choices for the within-class densities: the multivariate normal and the multivariate t. A simulation study demonstrates that our method performs well compared to popular off-the-shelf machine learning methods and is also quick; performing 15,000 iterations on a dataset of 500 samples with 2 measurements each in 7.3 seconds for the MVN mixture model and 11.9 seconds for the MVT mixture model. We apply our model to two datasets generated using the enzyme-linked immunosorbent assay (ELISA), a spectrophotometric assay often used to screen for antibodies. The examples we consider were collected in 2020 and measure seropositivity for SARS-CoV-2. We use our model to estimate seroprevalence in the populations studied. We implement the models in C++ using a Metropolis-within-Gibbs algorithm; this is available in the R package at https://github.com/stcolema/BatchMixtureModel. Scripts to recreate our analysis are at https://github.com/stcolema/BatchClassifierPaper.

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Projected t-SNE for batch correction

Bioinformatics ◽

10.1093/bioinformatics/btaa189 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3522-3527 ◽

Cited By ~ 3

Author(s):

Emanuele Aliverti ◽

Jeffrey L Tilson ◽

Dayne L Filer ◽

Benjamin Babcock ◽

Alejandro Colaneri ◽

...

Keyword(s):

Single Cell ◽

High Dimensional Data ◽

Cell Types ◽

R Package ◽

High Dimensional ◽

Batch Effects ◽

Batch Correction ◽

Fundamental Information ◽

Cell Gene Expression ◽

Low Dimensional

Abstract Motivation Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data. Results The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours. Availability and implementation Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies. Contact [email protected]

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A multivariate method to correct for batch effects in microbiome data

10.1101/2020.10.27.358283 ◽

2020 ◽

Author(s):

Yiwen Wang ◽

Kim-Anh Lê Cao

Keyword(s):

Correction Method ◽

Expression Data ◽

Batch Effects ◽

Batch Correction ◽

Treatment Variation ◽

Corrected Data ◽

Component Estimation ◽

Microbiome Data ◽

Treatment Designs ◽

Latent Components

AbstractMicrobial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to, and obscure any factors of interest. Existing batch correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce a new multivariate and non-parametric batch correction method based on Partial Least Squares Discriminant Analysis. PLSDA-batch first estimates treatment and batch variation with latent components to then subtract batch variation from the data. The resulting batch effect corrected data can then be input in any downstream statistical analysis. Two variants are also proposed to handle unbalanced batch x treatment designs and to include variable selection during component estimation. We compare our approaches with existing batch correction methods removeBatchEffect and ComBat on simulated and three case studies. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, and especially when batch effects have high variability. Reproducible code and vignettes are available on GitHub.

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Assessment of batch-correction methods for scRNA-seq data with a new test metric

10.1101/200345 ◽

2017 ◽

Cited By ~ 13

Author(s):

Maren Büttner ◽

Zhichao Miao ◽

F Alexander Wolf ◽

Sarah A Teichmann ◽

Fabian J Theis

Keyword(s):

Data Integration ◽

Principal Component ◽

R Package ◽

Batch Effect ◽

Data Sets ◽

Batch Effects ◽

Visual Evaluation ◽

Batch Correction ◽

Multiple Data Sets ◽

Reduced Representations

AbstractSingle-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension-reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.[Before final publication, we will upload the R package to Bioconductor]

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The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Symmetry ◽

10.3390/sym13030474 ◽

2021 ◽

Vol 13 (3) ◽

pp. 474

Author(s):

Abdulhakim A. Al-Babtain ◽

Ibrahim Elbatal ◽

Hazem Al-Mofleh ◽

Ahmed M. Gemeay ◽

Ahmed Z. Afify ◽

...

Keyword(s):

Numerical Simulations ◽

Exponential Distribution ◽

Real Data ◽

Exponential Model ◽

Statistical Properties ◽

Engineering Science ◽

Data Sets ◽

Engineering Sciences ◽

General Statistical ◽

Anderson Darling

In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

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Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

Bioinformatics ◽

10.1093/bioinformatics/btz885 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2017-2024

Author(s):

Weiwei Zhang ◽

Ziyi Li ◽

Nana Wei ◽

Hua-Jun Wu ◽

Xiaoqi Zheng

Keyword(s):

Real Data ◽

R Package ◽

Differential Methylation ◽

Least Square ◽

Epigenetic Mechanism ◽

Supplementary Information ◽

Cpg Sites ◽

Tumor Purity ◽

Different Sources ◽

Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

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mixIndependR: a R package for statistical independence testing of loci in database of multi-locus genotypes

BMC Bioinformatics ◽

10.1186/s12859-020-03945-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Bing Song ◽

August E. Woerner ◽

John Planz

Keyword(s):

Population Genetics ◽

Linkage Disequilibrium ◽

Genetic Markers ◽

Software Package ◽

Tandem Repeats ◽

Population Data ◽

Real Data ◽

R Package ◽

Nucleotide Polymorphisms ◽

Mutual Independence

Abstract Background Multi-locus genotype data are widely used in population genetics and disease studies. In evaluating the utility of multi-locus data, the independence of markers is commonly considered in many genomic assessments. Generally, pairwise non-random associations are tested by linkage disequilibrium; however, the dependence of one panel might be triplet, quartet, or other. Therefore, a compatible and user-friendly software is necessary for testing and assessing the global linkage disequilibrium among mixed genetic data. Results This study describes a software package for testing the mutual independence of mixed genetic datasets. Mutual independence is defined as no non-random associations among all subsets of the tested panel. The new R package “mixIndependR” calculates basic genetic parameters like allele frequency, genotype frequency, heterozygosity, Hardy–Weinberg equilibrium, and linkage disequilibrium (LD) by mutual independence from population data, regardless of the type of markers, such as simple nucleotide polymorphisms, short tandem repeats, insertions and deletions, and any other genetic markers. A novel method of assessing the dependence of mixed genetic panels is developed in this study and functionally analyzed in the software package. By comparing the observed distribution of two common summary statistics (the number of heterozygous loci [K] and the number of share alleles [X]) with their expected distributions under the assumption of mutual independence, the overall independence is tested. Conclusion The package “mixIndependR” is compatible to all categories of genetic markers and detects the overall non-random associations. Compared to pairwise disequilibrium, the approach described herein tends to have higher power, especially when number of markers is large. With this package, more multi-functional or stronger genetic panels can be developed, like mixed panels with different kinds of markers. In population genetics, the package “mixIndependR” makes it possible to discover more about admixture of populations, natural selection, genetic drift, and population demographics, as a more powerful method of detecting LD. Moreover, this new approach can optimize variants selection in disease studies and contribute to panel combination for treatments in multimorbidity. Application of this approach in real data is expected in the future, and this might bring a leap in the field of genetic technology. Availability The R package mixIndependR, is available on the Comprehensive R Archive Network (CRAN) at: https://cran.r-project.org/web/packages/mixIndependR/index.html.

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