poss_dataset_ids = dataset_info
.map(d => d.dataset_id)
.filter(d => results.map(r => r.dataset_id).includes(d))
poss_method_ids = method_info
.map(d => d.method_id)
.filter(d => results.map(r => r.method_id).includes(d))
poss_metric_ids = metric_info
.map(d => d.metric_id)
.filter(d => results.map(r => Object.keys(r.scaled_scores)).flat().includes(d))Batch integration graph
Removing batch effects while preserving biological variation (graph output)
3 datasets · 40 methods · 5 control methods · 4 metrics
Task info Method info Metric info Dataset info Results
This is a sub-task of the overall batch integration task. Batch (or data) integration methods integrate datasets across batches that arise from various biological and technical sources. Methods that integrate batches typically have three different types of output: a corrected feature matrix, a joint embedding across batches, and/or an integrated cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods that can output integrated graphs, and includes methods that canonically output the other two data formats with subsequent postprocessing to generate a graph. Other sub-tasks for batch integration can be found for:
This sub-task was taken from a benchmarking study of data integration methods.
Summary
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Results
Results table of the scores per method, dataset and metric (after scaling). Use the filters to make a custom subselection of methods and datasets. The “Overall mean” dataset is the mean value across all datasets.
Dataset info
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Immune (by batch)
Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2) (Luecken et al. 2021).
Lung (Viera Braga et al.)
Human lung scRNA-seq data from 3 datasets with 32,472 cells. From Vieira Braga et al. Technologies: 10X and Drop-seq (Luecken et al. 2021).
Pancreas (by batch)
Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq) (Luecken et al. 2021).
Method info
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BBKNN (full/scaled)
BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation (Polański et al. 2019). Links: Docs.
BBKNN (full/unscaled)
BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation (Polański et al. 2019). Links: Docs.
BBKNN (hvg/scaled)
BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation (Polański et al. 2019). Links: Docs.
BBKNN (hvg/unscaled)
BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation (Polański et al. 2019). Links: Docs.
Combat (full/scaled)
ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results (Johnson, Li, and Rabinovic 2006). Links: Docs.
Combat (full/unscaled)
ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results (Johnson, Li, and Rabinovic 2006). Links: Docs.
Combat (hvg/scaled)
ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results (Johnson, Li, and Rabinovic 2006). Links: Docs.
Combat (hvg/unscaled)
ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results (Johnson, Li, and Rabinovic 2006). Links: Docs.
FastMNN embed (full/scaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN embed (full/unscaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN embed (hvg/scaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN embed (hvg/unscaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN feature (full/scaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN feature (full/unscaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN feature (hvg/scaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
FastMNN feature (hvg/unscaled)
fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step (Lun 2019). Links: Docs.
Harmony (full/scaled)
Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable (Korsunsky et al. 2019). Links: Docs.
Harmony (full/unscaled)
Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable (Korsunsky et al. 2019). Links: Docs.
Harmony (hvg/scaled)
Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable (Korsunsky et al. 2019). Links: Docs.
Harmony (hvg/unscaled)
Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable (Korsunsky et al. 2019). Links: Docs.
Liger (full/unscaled)
LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised (Welch et al. 2019). Links: Docs.
Liger (hvg/unscaled)
LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised (Welch et al. 2019). Links: Docs.
MNN (full/scaled)
MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively (Haghverdi et al. 2018). Links: Docs.
MNN (full/unscaled)
MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively (Haghverdi et al. 2018). Links: Docs.
MNN (hvg/scaled)
MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively (Haghverdi et al. 2018). Links: Docs.
MNN (hvg/unscaled)
MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively (Haghverdi et al. 2018). Links: Docs.
SCALEX (full)
SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space (Xiong et al. 2022). Links: Docs.
SCALEX (hvg)
SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space (Xiong et al. 2022). Links: Docs.
Scanorama (full/scaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama (full/unscaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama (hvg/scaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama (hvg/unscaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama gene output (full/scaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama gene output (full/unscaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama gene output (hvg/scaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
Scanorama gene output (hvg/unscaled)
Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane (Hie, Bryson, and Berger 2019). Links: Docs.
scANVI (full/unscaled)
ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation (Xu et al. 2021). Links: Docs.
scANVI (hvg/unscaled)
ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation (Xu et al. 2021). Links: Docs.
scVI (full/unscaled)
scVI combines a variational autoencoder with a hierarchical Bayesian model (Lopez et al. 2018). Links: Docs.
scVI (hvg/unscaled)
scVI combines a variational autoencoder with a hierarchical Bayesian model (Lopez et al. 2018). Links: Docs.
Control method info
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Random Integration by Batch
Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each batch label
Random Graph by Celltype
Cells are embedded as a one-hot encoding of celltype labels. A graph is then built on this embedding
Random Integration by Celltype
Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each celltype label
No Integration
Cells are embedded by PCA on the unintegrated data. A graph is built on this PCA embedding
Random Integration
Feature values, embedding coordinates, and graph connectivity are all randomly permuted
Metric info
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ARI
ARI (Adjusted Rand Index) compares the overlap of two clusterings. It considers both correct clustering overlaps while also counting correct disagreements between two clustering (Luecken et al. 2021).
Graph connectivity
The graph connectivity metric assesses whether the kNN graph representation, G, of the integrated data connects all cells with the same cell identity label (Luecken et al. 2021).
Isolated label F1
Isolated cell labels are identified as the labels present in the least number of batches in the integration task. The score evaluates how well these isolated labels separate from other cell identities based on clustering (Luecken et al. 2021).
NMI
NMI compares the overlap of two clusterings. We used NMI to compare the cell-type labels with Louvain clusters computed on the integrated dataset (Luecken et al. 2021).
Quality control results
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Normalisation visualisation
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Authors
Haghverdi, Laleh, Aaron T L Lun, Michael D Morgan, and John C Marioni. 2018. “Batch Effects in Single-Cell RNA-Sequencing Data Are Corrected by Matching Mutual Nearest Neighbors.” Nature Biotechnology 36 (5): 421–27. https://doi.org/10.1038/nbt.4091.
Hie, Brian, Bryan Bryson, and Bonnie Berger. 2019. “Efficient Integration of Heterogeneous Single-Cell Transcriptomes Using Scanorama.” Nature Biotechnology 37 (6): 685–91. https://doi.org/10.1038/s41587-019-0113-3.
Johnson, W. Evan, Cheng Li, and Ariel Rabinovic. 2006. “Adjusting Batch Effects in Microarray Expression Data Using Empirical Bayes Methods.” Biostatistics 8 (1): 118–27. https://doi.org/10.1093/biostatistics/kxj037.
Korsunsky, Ilya, Nghia Millard, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-ru Loh, and Soumya Raychaudhuri. 2019. “Fast, Sensitive and Accurate Integration of Single-Cell Data with Harmony.” Nature Methods 16 (12): 1289–96. https://doi.org/10.1038/s41592-019-0619-0.
Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, and Nir Yosef. 2018. “Deep Generative Modeling for Single-Cell Transcriptomics.” Nature Methods 15 (12): 1053–58. https://doi.org/10.1038/s41592-018-0229-2.
Luecken, Malte D., M. Büttner, K. Chaichoompu, A. Danese, M. Interlandi, M. F. Mueller, D. C. Strobl, et al. 2021. “Benchmarking Atlas-Level Data Integration in Single-Cell Genomics.” Nature Methods 19 (1): 41–50. https://doi.org/10.1038/s41592-021-01336-8.
Lun, Aaron. 2019. “A Description of the Theory Behind the fastMNN Algorithm.” https://marionilab.github.io/FurtherMNN2018/theory/description.html.
Polański, Krzysztof, Matthew D Young, Zhichao Miao, Kerstin B Meyer, Sarah A Teichmann, and Jong-Eun Park. 2019. “BBKNN: Fast Batch Alignment of Single Cell Transcriptomes.” Edited by Bonnie Berger. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz625.
Welch, Joshua D., Velina Kozareva, Ashley Ferreira, Charles Vanderburg, Carly Martin, and Evan Z. Macosko. 2019. “Single-Cell Multi-Omic Integration Compares and Contrasts Features of Brain Cell Identity.” Cell 177 (7): 1873–1887.e17. https://doi.org/10.1016/j.cell.2019.05.006.
Xiong, Lei, Kang Tian, Yuzhe Li, Weixi Ning, Xin Gao, and Qiangfeng Cliff Zhang. 2022. “Online Single-Cell Data Integration Through Projecting Heterogeneous Datasets into a Common Cell-Embedding Space.” Nature Communications 13 (1). https://doi.org/10.1038/s41467-022-33758-z.
Xu, Chenling, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I Jordan, and Nir Yosef. 2021. “Probabilistic Harmonization and Annotation of Single-Cell Transcriptomics Data with Deep Generative Models.” Molecular Systems Biology 17 (1). https://doi.org/10.15252/msb.20209620.