In the PCA space, Harmony iteratively removes batch effects present. Another recently proposed method, Harmony, first employs PCA for dimensionality reduction. The MNNs are then computed in the CCA subspace and serve as “anchors” to correct the data. A newer version, Seurat Integration (Seurat 3), first uses CCA to project the data into a subspace to identify correlations across datasets. It employs canonical correlation analysis (CCA) to reduce data dimensionality and capture the most correlated data features to align the data batches. The Seurat MultiCCA method from the popular Seurat package was developed in 2017 by the Satija lab. Two other methods, Scanorama and BBKNN, also search for MNNs in dimensionally reduced spaces and use them in a similarity weighted manner to guide batch integration. As such, the developers introduced fastMNN, which applies the MNN scheme in the subspace computed using principal component analysis (PCA), resulting in significant improvements in both runtime and accuracy. However, this approach is computationally demanding in terms of CPU time and memory, due to the need to compute the list of neighbors in a high dimension gene expression space. The advantage of this approach is that a normalized gene expression matrix is obtained, which can be employed in downstream analysis. The resulting list of paired cells (or MNNs) is used to compute the translation vector to align the datasets into a shared space. The algorithm first identifies mutual nearest neighbors (MNNs) to establish connections between two datasets. , identifies cell mappings between datasets and then reconstructs the data in a shared space. Ī popular and successful approach, pioneered by Haghverdi et al. This has prompted efforts to develop workflows to handle data with such characteristics.
However, single-cell experiments suffer from “drop out” events due to the stochasticity of gene expression, or failure in RNA capture or amplification during sequencing. To address these challenges, tools developed for microarray data batch correction such as ComBat and limma have been employed on single-cell RNA-seq (scRNA-seq) data. Batch effects can be highly nonlinear, making it difficult to correctly align different datasets while preserving key biological variations. As such, effective batch-effect removal is essential. These differences lead to large variations or batch effects in the data, and can confound biological variations of interest during data integration. Single-cell data is often compiled from multiple experiments with differences in capturing times, handling personnel, reagent lots, equipments, and even technology platforms. Technological advances in the recent years have increased our ability to generate high-throughput single-cell gene expression data. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives. Conclusionīased on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. We also investigate the use of batch-corrected data to study differential gene expression. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. 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. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. 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.
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