Batch effects removal for microbiome data via conditional quantile regression (ConQuR)

Microbial communities have been increasingly studied in recent years to investigate their role in ecological habitats. However, microbiome studies are difficult to reproduce or replicate as they may suffer from confounding factors that are unavoidable in practice and originate from biological, technical or computational sources. In this review, we define batch effects as unwanted variation introduced by confounding factors that are not related to any factors of interest. Computational and analytical methods are required to remove or account for batch effects. However, inherent microbiome data characteristics (e.g. sparse, compositional and multivariate) challenge the development and application of batch effect adjustment methods to either account or correct for batch effects. We present commonly encountered sources of batch effects that we illustrate in several case studies. We discuss the limitations of current methods, which often have assumptions that are not met due to the peculiarities of microbiome data. We provide practical guidelines for assessing the efficiency of the methods based on visual and numerical outputs and a thorough tutorial to reproduce the analyses conducted in this review.

MotivationBatch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases.ResultsIn this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction.Availability and implementationMultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747).Supplementary informationSupplementary data are available at Bioinformatics online.

Introduction: Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. It is unknown to what extent TMAs are affected by batch effects, i.e., measurement error in biomarker levels between batches (slides from TMAs), what impact batch effects have on scientific inference from TMAs, and how to correct for batch effects in the analysis phase. Methods: We leveraged 14 prostate cancer TMAs from the tumors of 1,464 men, prospectively diagnosed 1982-2009 and followed for survival through 2017. Twenty protein biomarkers were measured, using eye-scoring or computational scoring of a median of 3 cores per tumor. We quantified the maximal extent of potential batch effects (between-TMA variance) using intraclass correlation coefficients. We developed a free R package (batchtma) and implemented several methods for batch effect correction, focusing on batch means (simple means, marginal standardization, inverse probability weighting), mean and variance (ComBat), background and dynamic range (quantile regression), ranks (quantile normalization), and stratification-based approaches. Some methods attempted to retain biomarker differences from differing clinical and pathological characteristics of TMAs. We evaluated the performance of correction methods using plasmode simulation and quantified the impact of batch effect correction on common longitudinal biomarker analyses. Results: In 10 of the 20 biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1 to 47%). We observed differences in clinical and pathological characteristics between TMAs with secular changes; for example, between 11% and 32% of tumors per TMA were high-grade (Gleason 8-10). In parallel, we confirmed batch effects between multiple cores from the same tumors included on >1 TMA and between tumors with similar characteristics across TMAs. Batch effect-corrected biomarker levels were more similar between different correction methods compared to uncorrected values. For biomarkers with little between-TMA differences, associations of biomarker levels and survival outcomes were similar with and without batch effect correction. For biomarkers with higher between-TMA differences, Cox regression results after batch effect correction were substantially different from results based on uncorrected values, with up to twofold differences in hazard ratios. In plasmode simulation, all correction methods were better than no batch effect correction. Conclusions: Some extent of batch effects should be expected in TMA-based cancer biomarker studies. Batch effects should be quantified and, if necessary, corrected in any biomarker study using more than one TMA. Anticipating batch effects during study design and, for biomarkers found to have batch effects, utilizing straightforward analytical correction approaches increase the validity of TMA-based studies. Citation Format: Konrad H. Stopsack, Molin Wang, Svitlana Tyekucheva, Travis A. Gerke, J. Bailey Vaselkiv, Kathryn L. Penney, Philip W. Kantoff, Stephen P. Finn, Michelangelo Fiorentino, Massimo Loda, Giovanni Parmigiani, Lorelei A. Mucci. Batch effects in tumor biomarker studies using tissue microarrays: Extent, impact, and remediation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 893.

Microarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat) or have the potential to overcorrect, by removing true but unknown biological differences (Surrogate Variable Analysis SVA). It is well known that experimental conditions such as array or reagent batches, PCR amplification or ozone levels can affect the measured expression levels; often the direction of perturbation of the measured expression is the same in different datasets. However, there are no BE correction algorithms that attempt to estimate the individual effects of technical differences and use them to correct expression data. In this manuscript, we show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a reference set of microarray samples can predict much of the batch effect in other validation sets. We present a rationale of selecting a reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the reference set to efficiently predict and remove BE. Using two independent validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction can be precomputed without using the samples to be corrected (blind), done on each sample individually (single sample) and corrects only known technical effects without removing known or unknown biological differences (conservative). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. We also compare the performance of BESC to three other batch correction methods: SVA, Removing Unwanted Variation (RUV) and Hidden Covariates with Prior (HCP). An R Package besc implementing the algorithm is available from http://explainbio.com.

Background: Cellulitis is an infection of the skin and the tissues just under the skin. As any disease, cellulitis has various physiological and physical effects that deteriorate a patient's quality of life. Luckily, cellulitis can be treated when dealt with in a timely fashion. Nonetheless, some patients may experience more than one episode of cellulitis or a recurrence of cellulitis that was previously cured. In fact, the occurrences of cellulitis episodes are believed to follow a statistical distribution. The frequency distribution of cellulitis episodes is scrutinized herein. We aimed to investigate the risk factors that affect the number of cellulitis episodes and the pattern of association between cancer types and cellulitis episodes by using analytical and visual approaches.Methods: A statistical approach applying a two-part count regression model was used instead of the traditional one-part count model. Moreover, multiple correspondence analysis was used to support the finding of count regression models.Results: The results of analysis of the sample from the National Cheng Kung University hospital in Taiwan revealed the mean age of patients was 58.7 ± 14.31 years old. The two-part regression model is conceptually and numerically better than the one-part regression model when examining the risks factors that affect cellulitis episodes. Particularly, we found the significant factors based on the best model are cellulitis history ([Formula: see text]; P value < 0.001), clinical stage of cancer (3) ([Formula: see text]; P value < 0.001), no cancer ([Formula: see text]; P value < 0.05), cancer of female reproductive organs ([Formula: see text]; P value < 0.05), breast cancer ([Formula: see text]; P value < 0.05), and age ≥ 60 years ([Formula: see text]; P value < 0.05). Multiple correspondence analysis approach found cancer types (breast and female reproductive organ), age ≥ 60 years, and cellulitis history were more likely to link to excess zero cellulitis or one cellulitis episode.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

Decreasing costs are making it feasible to perform time series proteomics and genomics experiments with more replicates and higher resolution than ever before. With more replicates and time points, proteome and genome-wide patterns of expression are more readily discernible. These larger experiments require more batches exacerbating batch effects and increasing the number of bias trends. In the case of proteomics, where methods frequently result in missing data this increasing scale is also decreasing the number of peptides observed in all samples. The sources of batch effects and missing data are incompletely understood necessitating novel techniques. Here we show that by exploiting the structure of time series experiments, it is possible to accurately and reproducibly model and remove batch effects. We implement Learning and Imputation for Mass-spec Bias Reduction (LIMBR) software, which builds on previous block-based models of batch effects and includes features specific to time series and circadian studies. To aid in the analysis of time series proteomics experiments, which are often plagued with missing data points, we also integrate an imputation system. By building LIMBR for imputation and time series tailored bias modeling into one straightforward software package, we expect that the quality and ease of large-scale proteomics and genomics time series experiments will be significantly increased. Python code and documentation is available for download at https://github.com/aleccrowell/LIMBR and LIMBR can be downloaded and installed with dependencies using 'pip install limbr'. Supplementary data are available at Bioinformatics online.

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.

Purpose This study aims to examine how ESG disclosure (i.e. sustainability disclosure) responds to board gender diversity using a conditional panel quantile regression approach. Design/methodology/approach Data was obtained from the audited annual reports of 422 firms listed on stock exchanges across 13 countries in Africa for the period 2006–2020. Empirical results were generated using conditional panel quantile regression, but GMM was also used to confirm result robustness. Findings The findings provide evidence that board gender diversity positively affects ESG disclosure. More specifically, the results provide evidence that the economic effect of board gender diversity on ESG disclosure gets pronounced/amplified at different quantile levels, supporting the arguments of critical mass. Research limitations/implications The study used data of 422 firms from 13 African economies for the period 2006-2020 to estimate the heterogeneous effect of board gender diversity on sustainability disclosures. Further research can focus on the interplay between different dimensions of board diversity in affecting corporate and sustainability performance. Practical implications Overall, the result advances the case for firms to pursue gender inclusive strategies/policies that increase the number of women or ensure more gender balance on their boards because it comes with higher benefits as far as sustainability reporting is concerned. In terms of policy, the study reaffirms the need for relevant stakeholders in Africa to come up with a quota policy as far as women representation on boards is concerned. Originality/value Majority of studies, particularly those pertaining to board gender diversity have focused on how a percentage increase or decrease impacts organizational outcomes (where mean effect has been largely considered). This study significantly provides that lower number of women on boards may not bring out the best in them as far as the issue of ESG disclosure in Africa is concerned.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

BackgroundCell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.MethodsWe developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.ResultsHASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients’ overall survival.ConclusionHASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD.

Removal of batch effects using stratified subsampling of metabolomic data for in vitro endocrine disruptors screening

Clustering and cell type classification are a vital step of analyzing scRNA-seq data to reveal the complexity of the tissue (e.g. the number of cell types and the transcription characteristics of the respective cell type). Recently, deep learning-based single-cell clustering algorithms become popular since they integrate the dimensionality reduction with clustering. But these methods still have unstable clustering effects for the scRNA-seq datasets with high dropouts or noise. In this study, a novel single-cell RNA-seq deep embedding clustering via convolutional autoencoder embedding and soft K-means (scCAEs) is proposed by simultaneously learning the feature representation and clustering. It integrates the deep learning with convolutional autoencoder to characterize scRNA-seq data and proposes a regularized soft K-means algorithm to cluster cell populations in a learned latent space. Next, a novel constraint is introduced to the clustering objective function to iteratively optimize the clustering results, and more importantly, it is theoretically proved that this objective function optimization ensures the convergence. Moreover, it adds the reconstruction loss to the objective function combining the dimensionality reduction with clustering to find a more suitable embedding space for clustering. The proposed method is validated on a variety of datasets, in which the number of clusters in the mentioned datasets ranges from 4 to 46, and the number of cells ranges from 90 to 30302. The experimental results show that scCAEs is superior to other state-of-the-art methods on the mentioned datasets, and it also keeps the satisfying compatibility and robustness. In addition, for single-cell datasets with the batch effects, scCAEs can ensure the cell separation while removing batch effects.

With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among different datasets must be considered. Although several methods have been recently published to remove batch effects in scRNA-seq data, two problems remain to be challenging and not completely solved: (i) how to reduce the distribution differences of different batches more accurately; and (ii) how to align samples from different batches to recover the cell type clusters. We proposed a novel deep-learning approach, which is a hierarchical distribution-matching framework assisted with contrastive learning to address these two problems. Firstly, we design a hierarchical framework for distribution matching based on a deep autoencoder. This framework employs an adversarial training strategy to match the global distribution of different batches. This provides an improved foundation to further match the local distributions with a maximum mean discrepancy-based loss. For local matching, we divide cells in each batch into clusters and develop a contrastive learning mechanism to simultaneously align similar cluster pairs and keep noisy pairs apart from each other. This allows to obtain clusters with all cells of the same type (true positives), and avoid clusters with cells of different type (false positives). We demonstrate the effectiveness of our method on both simulated and real datasets. Results show that our new method significantly outperforms the state-of-the-art methods and has the ability to prevent overcorrection. The python code to generate results and figures in this article is available at https://github.com/zhanglabNKU/HDMC, the data underlying this article is also available at this github repository. Supplementary data are available at Bioinformatics online.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.