A novel approach to remove the batch effect of single-cell data.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang F
PROVIDER: S-EPMC6796914 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
A novel approach to remove the batch effect of single-cell data.
Zhang Feng F Wu Yu Y Tian Weidong W
Cell discovery 20190924
Similar Datasets
Project description:With the development of single-cell RNA sequencing (scRNA-seq) technology, analysts need to integrate hundreds of thousands of cells with multiple experimental batches. It is becoming increasingly difficult for users to select the best integration methods to remove batch effects. Here, we compared the advantages and limitations of four commonly used Scanpy-based batch-correction methods using two representative and large-scale scRNA-seq datasets. We quantitatively evaluated batch-correction performance and efficiency. Furthermore, we discussed the performance differences among the evaluated methods at the algorithm level.
| S-EPMC7338326 | biostudies-literature
Project description:BACKGROUND:Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. RESULTS:We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. CONCLUSION:Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
| S-EPMC6964114 | biostudies-literature
Project description:BackgroundBatch effects refer to data variations that arise from non-biological factors such as experimental conditions, equipment, and external factors. These effects are considered significant issues in the analysis of biological data since they can compromise data consistency and distort actual biological differences, which can severely skew the results of downstream analyses.MethodIn this study, we introduce a new approach that comprehensively addresses two types of batch effects: "systematic batch effects" which are consistent across all samples in a batch, and "nonsystematic batch effects" which vary depending on the variability of operational taxonomic units (OTUs) within each sample in the same batch. To address systematic batch effects, we apply a negative binomial regression model and correct for consistent batch influences by excluding fixed batch effects. Additionally, to handle nonsystematic batch effects, we employ composite quantile regression. By adjusting the distribution of OTUs to be similar based on a reference batch selected using the Kruskal-Walis test method, we consider the variability at the OTU level.ResultsThe performance of the model is evaluated and compared with existing methods using PERMANOVA R-squared values, Principal Coordinates Analysis (PCoA) plots and Average Silhouette Coefficient calculated with diverse distance-based metrics. The model is applied to three real microbiome datasets: Metagenomic urine control data, Human Immunodeficiency Virus Re-analysis Consortium data, and Men and Women Offering Understanding of Throat HPV study data. The results demonstrate that the model effectively corrects for batch effects across all datasets.
| S-EPMC11893821 | biostudies-literature
Project description:Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.
| S-EPMC8324985 | biostudies-literature
Project description:Batch effect correction is an essential step in the integrative analysis of multiple single-cell RNA-sequencing (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both types of methods only detect MNNs across batches of uncorrected data, where the large batch effects may affect the MNN search. To address this issue, we presented a batch effect correction approach via iterative supervised MNN (iSMNN) refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to popular alternative methods, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of differentially expressed genes (DEGs) that are relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.
| S-EPMC8579191 | biostudies-literature
Project description:The power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging owing to technical factors such as sparsity, low number of cells, and batch effect. To address these challenges, we developed scID (Single Cell IDentification), which uses the Fisher's Linear Discriminant Analysis-like framework to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.
| S-EPMC7063229 | biostudies-literature
Project description:Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.
| S-EPMC11304254 | biostudies-literature
Project description:scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.
| S-EPMC9944958 | biostudies-literature
Project description: Not available
| S-EPMC10781938 | biostudies-literature
Project description:Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.
| S-EPMC7864438 | biostudies-literature