SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.
SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.
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DOI：
10.1093/bioinformatics/btac819
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发表时间：
2023-01-01
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多个单细胞RNA测序数据集的综合分析可以更全面地表征细胞类型，但数据集之间的系统性技术差异（称为“批次效应”）需要在整合之前消除，以避免对数据的误导性解释。虽然已经开发了许多批量效应去除方法，但仍有很大的改进空间：大多数现有方法仅给出降维数据而不是单个基因的表达数据，基于计算要求高的模型，并且是黑箱模型，因此难以解释或调整。在这里，我们提出了一种新的批量效应去除方法，称为SCIBER（单细胞积分器和批量效应去除器），并研究其性能在真实的数据集。SCIBER根据细胞差异表达基因的重叠，在不同批次中匹配细胞簇。作为一种简单的算法，具有更好的可扩展性，数据与大量的细胞，是易于调整，SCIBER显示可比的，有时更好的准确性，在消除批量效应的真实的数据集相比，最先进的方法，这是更复杂。此外，SCIBER输出原始空间中的表达数据，即单个基因的表达，可直接用于下游分析。此外，SCIBER是一种基于参考的方法，它将其中一个批次指定为参考批次，并在过程中保持不变，使其特别适合将用户生成的数据集与标准参考数据（如人类细胞图谱）集成。SCIBER作为一个R包在CRAN上公开提供：https://cran.r-project.org/web/packages/SCIBER/。CRAN R软件包中包含一个小插图。 补充数据可在Bioinformatics在线获得。
Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as ‘batch effects’, need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune. Here, we present a new batch-effect-removal method called SCIBER (Single-Cell Integrator and Batch Effect Remover) and study its performance on real datasets. SCIBER matches cell clusters across batches according to the overlap of their differentially expressed genes. As a simple algorithm that has better scalability to data with a large number of cells and is easy to tune, SCIBER shows comparable and sometimes better accuracy in removing batch effects on real datasets compared to the state-of-the-art methods, which are much more complicated. Moreover, SCIBER outputs expression data in the original space, that is, the expression of individual genes, which can be used directly for downstream analyses. Additionally, SCIBER is a reference-based method, which assigns one of the batches as the reference batch and keeps it untouched during the process, making it especially suitable for integrating user-generated datasets with standard reference data such as the Human Cell Atlas. SCIBER is publicly available as an R package on CRAN: https://cran.r-project.org/web/packages/SCIBER/. A vignette is included in the CRAN R package. Supplementary data are available at Bioinformatics online.
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影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
48
作者：
McGinnistm, Christopher S.;Patterson, David M.;Gartner, Zev J.
通讯作者：
Gartner, Zev J.
影响因子：
48
作者：
Barkas, Nikolas;Petukhov, Viktor;Kharchenko, Peter V.
通讯作者：
Kharchenko, Peter V.
影响因子：
9.3
作者：
Muraro MJ;Dharmadhikari G;Grün D;Groen N;Dielen T;Jansen E;van Gurp L;Engelse MA;Carlotti F;de Koning EJ;van Oudenaarden A
通讯作者：
van Oudenaarden A