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@article{Buttner2019,
abstract = {Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but 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 low-dimensional embeddings, which are inherently imprecise. Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; 
                  https://github.com/theislab/kBET
                  
                ) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas.},
author = {B{\"{u}}ttner, Maren and Miao, Zhichao and Wolf, F. Alexander and Teichmann, Sarah A. and Theis, Fabian J.},
doi = {10.1038/s41592-018-0254-1},
issn = {1548-7091},
journal = {Nat. Methods},
keywords = {Data integration,Gene expression,Software},
month = {jan},
number = {1},
pages = {43--49},
publisher = {Nature Publishing Group},
title = {{A test metric for assessing single-cell RNA-seq batch correction}},
url = {http://www.nature.com/articles/s41592-018-0254-1},
volume = {16},
year = {2019}
}
@article{Haghverdi2018,
abstract = {Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.},
author = {Haghverdi, Laleh and Lun, Aaron T L and Morgan, Michael D and Marioni, John C},
doi = {10.1038/nbt.4091},
issn = {1087-0156},
journal = {Nat. Biotechnol.},
month = {apr},
number = {5},
pages = {421--427},
pmid = {29608177},
title = {{Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/29608177 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6152897 http://www.nature.com/doifinder/10.1038/nbt.4091},
volume = {36},
year = {2018}
}
@article{Scholz1987,
author = {Scholz, F. W. and Stephens, M. A.},
doi = {10.2307/2288805},
issn = {01621459},
journal = {J. Am. Stat. Assoc.},
month = {sep},
number = {399},
pages = {918},
title = {{K-Sample Anderson-Darling Tests}},
url = {https://www.jstor.org/stable/2288805?origin=crossref},
volume = {82},
year = {1987}
}
@article{Stuart2018,
abstract = {Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat},
author = {Stuart, Tim and Butler, Andrew and Hoffman, Paul and Hafemeister, Christoph and Papalexi, Efthymia and Mauck, William M and Stoeckius, Marlon and Smibert, Peter and Satija, Rahul},
doi = {10.1101/460147},
file = {:home/almut/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Stuart et al. - 2018 - Comprehensive integration of single cell data.pdf:pdf},
journal = {bioRxiv},
month = {nov},
pages = {460147},
publisher = {Cold Spring Harbor Laboratory},
title = {{Comprehensive integration of single cell data}},
url = {https://www.biorxiv.org/content/early/2018/11/02/460147},
year = {2018}
}
@article{Buttner2017,
abstract = {Single-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.},
author = {Buttner, Maren and Miao, Zhichao and Wolf, Alexander and Teichmann, Sarah A and Theis, Fabian J},
doi = {10.1101/200345},
file = {:home/almut/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Buttner et al. - 2017 - Assessment of batch-correction methods for scRNA-seq data with a new test metric.pdf:pdf},
journal = {bioRxiv},
month = {oct},
pages = {200345},
publisher = {Cold Spring Harbor Laboratory},
title = {{Assessment of batch-correction methods for scRNA-seq data with a new test metric}},
url = {https://www.biorxiv.org/content/early/2017/10/27/200345},
year = {2017}
}
@incollection{Latecki2007,
address = {Berlin, Heidelberg},
author = {Latecki, Longin Jan and Lazarevic, Aleksandar and Pokrajac, Dragoljub},
booktitle = {Mach. Learn. Data Min. Pattern Recognit.},
doi = {10.1007/978-3-540-73499-4_6},
pages = {61--75},
publisher = {Springer Berlin Heidelberg},
title = {{Outlier Detection with Kernel Density Functions}},
url = {http://link.springer.com/10.1007/978-3-540-73499-4{\_}6},
year = {2007}
}
@article{Korsunsky2018,
abstract = {The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of {\~{}}106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.},
author = {Korsunsky, Ilya and Fan, Jean and Slowikowski, Kamil and Zhang, Fan and Wei, Kevin and Baglaenko, Yuriy and Brenner, Michael and Loh, Po-Ru and Raychaudhuri, Soumya},
doi = {10.1101/461954},
file = {:home/almut/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Korsunsky et al. - 2018 - Fast, sensitive, and flexible integration of single cell data with Harmony.pdf:pdf},
journal = {bioRxiv},
month = {nov},
pages = {461954},
publisher = {Cold Spring Harbor Laboratory},
title = {{Fast, sensitive, and flexible integration of single cell data with Harmony}},
url = {https://www.biorxiv.org/content/early/2018/11/04/461954},
year = {2018}
}
