The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This analysis shows that the batch-mixing entropy is increased after background correction, indicating better mixing between samples (Fig. 4E).”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [20]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“PCA dimensionality reduction and mutual-nearest neighbours84 or Harmony34 batch correction was performed to correct for non-biological confounders between replicates and timepoints.”
From Cell diversity and plasticity during atrioventricular heart valve EMTs (2023) · cites this paper as [84]
In the citing paper
“We confirm that using an in silico batch correction maintains the sensitivity of all DA methods, comparable to the case of no batch effect, across different batch effect magnitudes (Supp Fig 9; batch effects corrected using fastMNN [19]”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [19]
In the citing paper
“We found that the SCING GRN module based dimensionality reduction carried the ability to correct for batch effect and retain biological information (Figure S6) in a similar manner to dedicated batch correction methods such as FastMNN,41 Harmony,42 and Seurat.43”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [41]
In the citing paper
“the most widely used data alignment approaches (Butler et al., 2018; Haghverdi et al., 2018; Johnson et al., 2007; Risso et al., 2014) implicitly assume that the differences between datasets arise entirely from technical variation and attempt to eliminate them, or map datasets into a completely shared latent space using dimensions of maximum correlation (Butler et al., 2018).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Haghverdi et al., 2018
In the citing paper
“Data integration methods such as Canonical Correlation Analysis (CCA; Butler et al, 2018), Mutual Nearest Neighbours (MNN; Haghverdi et al, 2018), Scanorama (preprint: Hie et al, 2018), RISC (preprint: Liu et al, 2018), scGen (preprint: Lotfollahi et al, 2018), LIGER (preprint: Welch et al, 2018), BBKNN (preprint: Park et al, 2018), and Harmony (preprint: Korsunsky et al, 2018) have been developed”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Haghverdi et al, 2018
In the citing paper
“These methods are potentially affected by the batch effect across platforms and experiments 12 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
(2008) · Journal of Machine Learning Research · Cited by 33,051
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Flagged first, then by citations
Cited by
Sorted by citations
Items 601–700 of 1,014
The mouse brain after foot shock in four dimensions: Temporal dynamics at a single-cell resolution
Bonapersona, V., Schuler, H., Damsteegt, R., Adolfs, Y., Pasterkamp, R. J., van den Heuvel, M. P., Joëls, M., & Sarabdjitsingh, R. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114002119 · Cited by 45
CellAgent: LLM-Driven Multi-Agent Framework for Natural Language-Based Single-Cell AnalysisPreprint
Xiao, Y., Liu, J., Zheng, Y., Jiao, S., Hao, J., Xie, X., Li, M., Wang, R., Ni, F., Li, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.13.593861 · Cited by 49
Differentiation reveals latent features of aging and an energy barrier in murine myogenesis
Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42
Mitchel, J., Gordon, M. G., Perez, R. K., Biederstedt, E., Bueno, R., Ye, C., & Kharchenko, P. V. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02411-z · Cited by 48
Nano, P. R., Fazzari, E., Azizad, D. J., Martija, A. A., Nguyen, C. V., Wang, S., Giang, V., Kan, R. L., Yoo, J., Wick, B. D., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01933-2 · Cited by 42
Johnson, T. S., Wang, T., Huang, Z., Yu, C. Y., Wu, Y., Han, Y., Zhang, Y., Huang, K., & Zhang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz295 · Cited by 44
Zhang, M., Liu, Y., Chen, J., Chen, L., Meng, J., Yang, C. H., Yin, S., Zhang, X., Zhang, L., Hao, Z., et al. (2020) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.16021 · Cited by 42
Clonal multi-omics reveals Bcor as a negative regulator of emergency dendritic cell development
Tian, L., Tomei, S., Schreuder, J., Weber, T., Amann‐Zalcenstein, D., Lin, D. S. Y., Tran, J., Audiger, C., Chu, M., Jarratt, A., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.012 · Cited by 39
Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data
Chu, S., Zhao, S., Shyr, Y., & Liu, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab565 · Cited by 41
Single-cell RNA-seq analysis of testicular somatic cell development in pigs
Zhang, L., Guo, M. Z., Liu, Z., Liu, R., Zheng, Y., Yu, T., Lv, Y., Lu, H., Zeng, W., Zhang, T., et al. (2022) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2022.03.014 · Cited by 43
Toseef, M., Petinrin, O. O., Wang, F., Rahaman, S., Liu, Z., Li, X., & Wong, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad254 · Cited by 44
Single-cell epigenomics and spatiotemporal transcriptomics reveal human cerebellar development
Zhong, S., Wang, M., Huang, L., Chen, Y., Ge, Y., Zhang, J., Shi, Y., Dong, H., Zhou, X., Wang, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43568-6 · Cited by 40
Liang, Y., Zhang, R., Biswas, S., Bu, Q., Xu, Z., Qiao, L., Zhou, Y., Tang, J., Zhou, J., Zhou, H., et al. (2023) · Cell Proliferation · doi:10.1111/cpr.13576 · Cited by 41
Xu, Y., Deng, C., Chen, H., Song, Y., Xu, H., Song, G., Wang, X., Luo, T., Chen, W., Ma, J., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-1307 · Cited by 41
Kim, H. J., Schneider, M., Raach, Y., Karypidis, P., Roux, J., Perdikaris, G. A., Holdermann, S., Kulsvehagen, L., Lecourt, A.-C., Narr, K., et al. (2026) · Cell · doi:10.1016/j.cell.2025.12.031 · Cited by 45
Lin, D. S. Y., Tian, L., Tomei, S., Amann‐Zalcenstein, D., Baldwin, T. M., Weber, T., Schreuder, J., Stonehouse, O. J., Rautela, J., Huntington, N. D., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00636-7 · Cited by 41
Sparse spatial transformers for few-shot learning
Chen, H., Li, H., Li, Y., & Chen, C. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3700-8 · Cited by 41
DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution
Liu, H., Zhou, J., Tian, W., Luo, C., Bartlett, A., Aldridge, A., Lucero, J., Osteen, J. K., Nery, J. R., Chen, H., et al. (2020) · doi:10.1101/2020.04.30.069377 · Cited by 290
Flynn, E., Almonte-Loya, A., & Fragiadakis, G. K. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050645 · Cited by 43
scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration
Li, Y., Zhang, D., Yang, M., Peng, D., Yu, J., Liu, Y., Lv, J., Chen, L., & Peng, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41795-5 · Cited by 39
The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research
Huang, K., Xu, Y., Feng, T., Lan, H., Ling, F., Xiang, H., & Liu, Q. (2024) · Biology · doi:10.3390/biology13060451 · Cited by 44
Luo, C., Zhang, R., Guo, R., Wu, L., Xue, T., He, Y., Jin, Y., Zhao, Y., Zhang, Z., Zhang, P., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.02.007 · Cited by 37
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment
Fei, T., & Yu, T. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa097 · Cited by 41
Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign
Chen, S., Rivaud, P., Park, J., Tsou, T., Charles, E. J., Haliburton, J. R., Pichiorri, F., & Thomson, M. W. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005990117 · Cited by 36
Therapeutic blockade of ER stress and inflammation prevents NASH and progression to HCC
Boslem, E., Reibe, S., Carlessi, R., Smeuninx, B., Tegegne, S., Egan, C. L., McLennan, E., Terry, L. V., Nobis, M., Mu, A., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0831 · Cited by 39
Derivation of human primordial germ cell-like cells in an embryonic-like culture
Esfahani, S. N., Zheng, Y., Arabpour, A., Irizarry, A. M. R., Kobayashi, N., Xue, X., Shao, Y., Zhao, C., Agranonik, N., Sparrow, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-43871-2 · Cited by 39
Hedayat, S., Cascione, L., Cunningham, D., Schirripa, M., Lampis, A., Hahne, J. C., Tunariu, N., Hong, S. P., Marchetti, S., Khan, K. H., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-2748 · Cited by 43
Four Core Genotypes mice harbour a 3.2MB X-Y translocation that perturbs Tlr7 dosage
Panten, J., Prete, S., Cleland, J., Saunders, L. M., van Riet, J., Schneider, A., Ginno, P. A., Schneider, N., Koch, M.-L., Chen, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52640-8 · Cited by 53
Yue, S. S. K., Tong, Y., Siu, H. C., Ho, S. L., Law, S. Y.-K., Tsui, W. Y., Chan, D., Huang, Y., Chan, A. S. Y., Yun, S. W., et al. (2024) · Gut · doi:10.1136/gutjnl-2024-332594 · Cited by 43
Interleukin-6 deficiency exacerbates Huntington’s disease model phenotypes
Wertz, M., Pineda, S. S., Lee, H., Kulicke, R., Kellis, M., & Heiman, M. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00379-3 · Cited by 35
Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering
Wu, Z. J., & Wu, H. (2020) · Genome biology · doi:10.1186/s13059-020-02027-x · Cited by 41
Self-organization of human dorsal-ventral forebrain structures by light induced SHH
Santis, R. D., Etoc, F., Rosado-Olivieri, E. A., & Brivanlou, A. H. (2021) · Nature Communications · doi:10.1038/s41467-021-26881-w · Cited by 38
Donnard, E. R., Shu, H., & Garber, M. G. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010221 · Cited by 39
Integrative inference of brain cell similarities and differences from single-cell genomicsPreprint
Welch, J. D., Kozareva, V., Ferreira, A. N., Vanderburg, C. R., Martin, C., & Macosko, E. Z. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/459891 · Cited by 40
Littman, R., Cheng, M., Wang, N., Peng, C., & Yang, X. (2023) · iScience · doi:10.1016/j.isci.2023.107124 · Cited by 35
A robust and scalable graph neural network for accurate single-cell classification
Zeng, Y., Wei, Z., Pan, Z., Lu, Y., & Yang, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab570 · Cited by 42
Cai, Z., Chen, L., Chen, S., Fang, R., Chen, X., & Lei, W. (2023) · Cell Communication and Signaling · doi:10.1186/s12964-023-01312-z · Cited by 41
Yu, H., Xue, W., Yu, H., Song, Y., Liu, X., Qin, L., Wang, S., Bao, H. F., Gu, H., Chen, G., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.171417 · Cited by 38
Quantitative assessment of cell population diversity in single-cell landscapes
Liu, Q., Herring, C. A., Sheng, Q., Ping, J., Simmons, A. J., Chen, B., Banerjee, A., Li, W., Gu, G., Coffey, R. J., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006687 · Cited by 40
Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor
Fischer, S., Crow, M., Harris, B. D., & Gillis, J. A. (2021) · Nature Protocols · doi:10.1038/s41596-021-00575-5 · Cited by 36
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis
Bao, S., Li, K., Yan, C., Zhang, Z., Qu, J., & Zhou, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab473 · Cited by 40
Khouri‐Farah, N., Guo, Q., Morgan, K., Shin, J., & Li, J. Y. H. (2022) · Science Advances · doi:10.1126/sciadv.abl9156 · Cited by 39
SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data
Song, Q., Zhu, X., Jin, L., Chen, M., Zhang, W., & Su, J. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac056 · Cited by 37
A universal framework for single-cell multi-omics data integration with graph convolutional networks
Gao, H., Zhang, B., Liu, L., Li, S., Gao, X., & Yu, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad081 · Cited by 38
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Sypetkowski, M., Rezanejad, M., Saberian, S., Kraus, O. Z., Urbanik, J., Taylor, J., Mabey, B., Victors, M. L., Yosinski, J., Sereshkeh, A. R., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00451 · Cited by 47
Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1
Tejwani, L., Ravindra, N. G., Lee, C., Cheng, Y., Nguyen, B., Luttik, K. P., Ni, L., Zhang, S., Morrison, L. M., Gionco, J. T., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.039 · Cited by 37
Würth, R., Donato, E., Michel, L. L., Saini, M., Becker, L., Cheytan, T., Doncevic, D., Meßmer, T., Gutjahr, E., Weber, R., et al. (2025) · Nature Cancer · doi:10.1038/s43018-024-00882-2 · Cited by 37
Sun, X., Qin, A., Wang, X., Ge, X., Liu, Z., Guo, C., Yu, X., Zhang, X., Lü, Y., Yang, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55869-z · Cited by 37
Transcriptional profiles of mouse oligodendrocyte precursor cells across the lifespan
Heo, D., Kim, A. A., Neumann, B., Doze, V. N., Xu, Y. K. T., Mironova, Y. A., Slosberg, J., Goff, L. A., Franklin, R. J. M., & Bergles, D. E. (2025) · Nature Aging · doi:10.1038/s43587-025-00840-2 · Cited by 42
Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data
Cheng, C., Easton, J. B., Rosencrance, C. D., Li, Y., Ju, B., Williams, J. S., Mulder, H. L., Pang, Y., Chen, W., & Chen, X. '. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz826 · Cited by 35
Deep feature extraction of single-cell transcriptomes by generative adversarial network
Bahrami, M., Maitra, M., Nagy, C., Turecki, G., Rabiee, H. R., & Li, Y. (2020) · Computer applications in the biosciences · doi:10.1093/bioinformatics/btaa976 · Cited by 38
scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-Cell Datasets
Song, Q., Su, J., Miller, L. D., & Zhang, W. (2020) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.09.002 · Cited by 37
Integration of millions of transcriptomes using batch-aware triplet neural networks
Simon, L. M., Wang, Y., & Zhao, Z. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00361-8 · Cited by 36
Ex vivo reconstitution of fetal oocyte development in humans and cynomolgus monkeys
Mizuta, K., Katou, Y., Nakakita, B., Kishine, A., Nosaka, Y., Saito, S., Iwatani, C., Tsuchiya, H., Kawamoto, I., Nakaya, M., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2022110815 · Cited by 39
Soucy, J. R., Todd, L. J., Kriukov, E., Phay, M., Malechka, V. V., Rivera, J. D., Reh, T. A., & Baranov, P. Y. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2302089120 · Cited by 35
Zhang, Z., Zhao, X., Bindra, M., Qiu, P., & Zhang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-45227-w · Cited by 40
Spatiotemporal transcriptome and metabolome landscapes of cotton somatic embryos
Ge, X., Yu, X., Liu, Z., Yuan, J., Qin, A., Wang, Y., Chen, Y., Qin, W., Liu, Y., Liu, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55870-6 · Cited by 36
Soraggi, S., Riera, M., Meyts, E. R., Schierup, M. H., & Almstrup, K. (2020) · Human Genetics · doi:10.1007/s00439-020-02116-8 · Cited by 36
Liu, Y., Zou, L., Tang, H., Li, J., Liu, H., Jiang, X., Jiang, B., Dong, Z., & Fu, W. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.791875 · Cited by 36
Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms
Crow, M., Suresh, H., Lee, J., & Gillis, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac276 · Cited by 34
Th17.1 cell driven sarcoidosis-like inflammation after anti-BCMA CAR T cells in multiple myeloma
Leipold, A. M., Werner, R. A., Düll, J., Jung, P., John, M., Stanojkovska, E., Zhou, X., Hornburger, H., Ruckdeschel, A. M., Dietrich, O., et al. (2023) · Leukemia · doi:10.1038/s41375-023-01824-0 · Cited by 35
MsImpute: Estimation of Missing Peptide Intensity Data in Label-Free Quantitative Mass Spectrometry
Hediyeh-zadeh, S., Webb, A. I., & Davis, M. J. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100558 · Cited by 33
Integration of gene expression and DNA methylation data across different experiments
Itai, Y., Rappoport, N., & Ron Shamir, A. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad566 · Cited by 35
Heterogeneity and transcriptional drivers of triple-negative breast cancer
Jovanović, B., Temko, D., Stevens, L. E., Seehawer, M., Fassl, A., Murphy, K. C., Anand, J., Garza, K., Gulvady, A. C., Qiu, X., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113564 · Cited by 41
Gawusu, S., Jamatutu, S. A., & Ahmed, A. (2024) · International Journal of Energy Research · doi:10.1155/2024/9411326 · Cited by 36
Knoll, R., Helbig, E. T., Dahm, K., Bolaji, O., Hamm, F., Dietrich, O., van Uelft, M., Müller, S. M., Bonaguro, L., Schulte-Schrepping, J., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.014 · Cited by 34
T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis
Jelčić, I., Naghavian, R., Fanaswala, I., Macnair, W., Esposito, C., Calini, D., Han, Y., Marti, Z., Raposo, C., del Castillo, J. S., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102027 · Cited by 42
Macrophages direct location-dependent recall of B cell memory to vaccination
Dhenni, R., Hoppé, A. C., Reynaldi, A., Kyaw, W., Handoko, N. T., Grootveld, A. K., Keith, Y. H., Bhattacharyya, N. D., Ahel, H. I., Telfser, A. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.04.005 · Cited by 38
Computational approaches for high‐throughput single‐cell data analysis
Todorov, H., & Saeys, Y. (2018) · FEBS Journal · doi:10.1111/febs.14613 · Cited by 35
Fröhlich, F., Reiser, A., Fink, L., Woschée, D., Ligon, T. S., Theis, F. J., Rädler, J. O., & Hasenauer, J. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0079-7 · Cited by 37
Cavalli, M., Diamanti, K., Pan, G., Spalinskas, R., Kumar, C., Deshmukh, A. S., Mann, M., Sahlén, P., Komorowski, J., & Wadelius, C. (2020) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0215 · Cited by 37
Yang, Y., Li, G., Qian, H., Wilhelmsen, K. C., Shen, Y., & Li, Y. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa097 · Cited by 34
Specific Neuroligin3–αNeurexin1 signaling regulates GABAergic synaptic function in mouse hippocampus
Uchigashima, M., Konno, K., Demchak, E., Cheung, A., Watanabe, T., Keener, D. G., Abe, M., Le, T., Sakimura, K., Sasaoka, T., et al. (2020) · eLife · doi:10.7554/elife.59545 · Cited by 35
Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular HierarchiesPreprint
Schoof, E. M., Furtwängler, B., Üresin, N., Rapin, N., Savickas, S., Gentil, C., Lechman, E. R., Keller, U. A. D., Dick, J. E., & Porse, B. T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/745679 · Cited by 41
Algorithmic advances in machine learning for single-cell expression analysis
Moreno, S. O., Kloiber, K., Machart, P., & Bonn, S. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.02.002 · Cited by 35
Xu, J., Gao, J., Liu, J., Huang, X., Zhang, H., Ma, A., Ye, J., Zhang, X., Li, Y., Yang, G., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110540 · Cited by 33
Fan, B., Zhou, J., Zhao, Y., Zhu, X., Zhu, M., Peng, Q., Li, J., Chang, X., Shi, D., Yin, J., et al. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2101216 · Cited by 35
Wang, Y. J., Schug, J., Lin, J., Wang, Z. P., Kossenkov, A. V., & Kaestner, K. H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/541433 · Cited by 33
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018820 · Cited by 35
Query to reference single-cell integration with transfer learningPreprint
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Avsec, Ž., Misharin, A. V., & Theis, F. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.16.205997 · Cited by 38
Kapell, H., Fazio, L., Dyckow, J., Schwarz, S., Cruz-Herranz, A., Mayer, C., Campos, J., D’Este, E., Möbius, W., Cordano, C., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci164223 · Cited by 39
Rajan, S., Ringwalt, E. M., McAloney, C. A., Vetter, T. A., Cam, M., Gross, A. C., Taslim, C., Wang, M., Cannon, M. V., Oles, A. R., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01593-3 · Cited by 44
Full spectrum flow cytometry in the clinical laboratory
Brestoff, J. R. (2023) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.14098 · Cited by 34
Smith, J. J., Taylor, S. R., Blum, J. A., Feng, W., Collings, R., Gitler, A. D., Miller, D. M., & Kratsios, P. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113857 · Cited by 35
A lymphocyte chemoaffinity axis for lung, non-intestinal mucosae and CNS
Ocón, B., Xiang, M., Bi, Y., Tan, S. Y., Brulois, K., Ayesha, A., Kunte, M., Zhou, C., LaJevic, M. D., Lazarus, N. H., et al. (2024) · Nature · doi:10.1038/s41586-024-08043-2 · Cited by 39
Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia
Wegmann, R., Bonilla, X., Casanova, R., Chevrier, S., Coelho, R., Esposito, C., Ficek, J., Götze, S., Gut, G., Jacob, F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53535-4 · Cited by 35
Embedding to reference t-SNE space addresses batch effects in single-cell classification
Poličar, P. G., Stražar, M., & Zupan, B. (2021) · Machine Learning · doi:10.1007/s10994-021-06043-1 · Cited by 38
Zerti, D., Collin, J., Queen, R. A., Cockell, S., & Lako, M. (2019) · Current Eye Research · doi:10.1080/02713683.2019.1697453 · Cited by 32
Reverse engineering human brain evolution using organoid models
Mostajo-Radji, M. A., Schmitz, M. T., Torres, S., & Pollen, A. A. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146582 · Cited by 36
Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells
Iyer, A., Gupta, K., Sharma, S., Hari, K., Lee, Y., Ramalingam, N., Yap, Y. S., West, J., Bhagat, A. A., Subramani, B. V., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041206 · Cited by 34
Hu, J., Zhong, Y., & Shang, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab400 · Cited by 33
Yang, J., Li, Y., Han, Y., Feng, Y., Zhou, M., Zong, C., He, X., Jia, R., Xu, X., & Fan, J. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-04390-4 · Cited by 34
Smajić, S., Prada‐Medina, C. A., Landoulsi, Z., Dietrich, C., Jarazo, J., Henck, J., Balachandran, S., Pachchek, S., Morris, C. M., Antony, P. M. A., et al. (2020) · medRxiv · doi:10.1101/2020.09.28.20202812 · Cited by 34
Choueiri, T. K., Atkins, M. B., Bakouny, Z., Carlo, M. I., Drake, C. G., Jonasch, E. W., Kapur, P., Lewis, B., Linehan, W. M., Mitchell, M. J., et al. (2020) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djaa064 · Cited by 33
A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization
Almet, A. A., Yuan, H., Annusver, K., Ramos, R., Liu, Y., Wiedemann, J., Sorkin, D. H., Xu, N., Sonkoly, E., Haniffa, M. A., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.03.1679 · Cited by 33
Xu, F., Chen, J., Li, Y., Ouyang, S., Yu, M., Wang, Y., Fang, X., He, K., & Yu, F. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.001 · Cited by 32
Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data
Çelik, S., Hütter, J.-C., Carlos, S. M., Lazar, N. H., Mohan, R., Tillinghast, C. A., Biancalani, T., Fay, M. M., Earnshaw, B. A., & Haque, I. S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012463 · Cited by 40
Chen, C., Chung, W., Chang, J. W., Yang, C., Hui‐Kang, D., Teng, Y.-C., Lu, C., Chen, W., Yang, H.-Y., Tsai, C.-C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54180-7 · Cited by 33
Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity
Zeng, T., & Dai, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00629 · Cited by 33
The temporal structure of the inner retina at a single glance
Zhao, Z., Klindt, D. A., Chagas, A. M., Szatko, K. P., Rogerson, L. E., Protti, D. A., Behrens, C., Dalkara, D., Schubert, T., Bethge, M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-60214-z · Cited by 32
Metadata
- Authors
- Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- University of Cambridge (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Marioni, John C
- Venue
- Nature Biotechnology
- Published
- 2018-04-02
- Volume
- 36
- Issue
- 5
- Pages
- 421-427
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC6152897
- OpenAlex
- W2794521141
- Cited by
- 2,416
- References
- 33
- Citations by year
- 2026: 266 · 2025: 355 · 2024: 387 · 2023: 364 · 2022: 271 · 2021: 321 · 2020: 240 · 2019: 144 · 2018: 63
- FWCI
- 71.66
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (108437, 105045/Z/14/Z)
- Cancer Research UK (22231, C14303 / A17197)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effects, single-cell RNA sequencing, batch effect correction, mutual nearest neighbors, data integration
- MeSH terms
- Data Analysis, Algorithms, Cluster Analysis, Sequence Analysis, RNA, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
