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Recovery of biological signals lost in single-cell batch integration with CellANOVA

Nature Biotechnology (2024)Cite this article

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Abstract

Data integration to align cells across batches has become a cornerstone of single-cell data analysis, critically affecting downstream results. Currently, there are no guidelines for when the biological differences between samples are separable from batch effects. Here we show that current paradigms for single-cell data integration remove biologically meaningful variation and introduce distortion. We present a statistical model and computationally scalable algorithm, CellANOVA (cell state space analysis of variance), that harnesses experimental design to explicitly recover biological signals that are erased during single-cell data integration. CellANOVA uses a ‘pool-of-controls’ design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest and allow the recovery of subtle biological signals. We apply CellANOVA to diverse contexts and validate the recovered biological signals by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nucleus data integration, where it recovers subtle biological signals that can be validated and replicated by external data.

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Fig. 1: Examples of control pool construction and integration results.
Fig. 2: Overview of CellANOVA.
Fig. 3: Benchmarking of CellANOVA and other integration methods on batch effect correction and data distortion.
Fig. 4: Benchmarking of CellANOVA and other integration methods on biological signal preservation.
Fig. 5: Evaluation of CellANOVA in multimodal data integration.
Fig. 6: Descriptive analysis of CellANOVA-estimated batch effects.

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Data availability

The mouse radiation therapy dataset is available from the GEO under accession number GSE280883. The JAK inhibition and PD1 immunotherapy trial dataset was obtained from Mathew et al.30 and is availabe from the Gene Expression Omnibus (GEO) under accession number GSE266219. The T1D dataset was obtained from Fasolino et al.29 and is available from the GEO under accession number GSE148073. The human kidney multiomics atlas dataset was obtained from Abedini et al.31 and is available from the GEO under accession number GSE211785. The scRNA-seq data by Lake et al.36 can be downloaded from KPMP (https://www.kpmp.org.; DK133081, DK133091, DK133092, DK133093, DK133095, DK1330971, DK114866, DK114908, DK133090, DK133113, DK133766, DK133768, DK114907, DK114920, DK114923, DK114933 and DK114886). The NSCLC T cell dataset was obtained from Salcher et al.32 and downloaded from another study59.

Code availability

All code used in this study, including the CellANOVA software and the analysis code, can be found on GitHub (https://github.com/Janezjz/cellanova).

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Acknowledgements

We would like to acknowledge support from the National Science Foundation (NSF; DMS-2345215 and DMS-2210104 to Z.M. and DMS-2245575 to Z.M., Z.Z. and N.R.Z.), National Institutes of Health (R01-HG006137, U2C-CA233285, and 5R01GM125301 to N.R.Z., 5R01DK087635 to K.S. and TL1TR001880 to T.L.L.), Mark Foundation Center for Radiobiology and Immunology (to N.R.Z. and A.M.) and Parker Institute for Cancer Immunotherapy (to D.M.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations.

Author information

Author notes

  1. These authors contributed equally: Zongming Ma, Nancy R. Zhang.

Authors and Affiliations

  1. Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Zhaojun Zhang, Kaishu Mason & Nancy R. Zhang

  2. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Divij Mathew & E. John Wherry

  3. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Divij Mathew, E. John Wherry & Andy J. Minn

  4. Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Divij Mathew, E. John Wherry & Andy J. Minn

  5. Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Tristan L. Lim, Clara Morral Martinez & Andy J. Minn

  6. Mark Foundation Center for Immunotherapy, Immune Signaling and Radiation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Clara Morral Martinez, E. John Wherry & Andy J. Minn

  7. Penn Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Sijia Huang

  8. Renal, Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Katalin Susztak

  9. Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA

    Katalin Susztak

  10. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Katalin Susztak

  11. Department of Statistics and Data Science, Yale University, New Haven, CT, USA

    Zongming Ma

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Contributions

Conceptualization, Z.Z., Z.M. and N.R.Z. Algorithm development, Z.Z., Z.M. and N.R.Z. Software implementation, Z.Z. Design of computational experiments, Z.Z., Z.M. and N.R.Z. Immunotherapy trial experiment, D.M. Mouse irradiation experiment, C.M.M. and T.L.L. Kidney disease experiment, K.S. Data analysis, Z.Z., with help from D.M., T.L.L., K.M., S.H., Z.M. and N.R.Z. Manuscript writing, Z.Z., Z.M. and N.R.Z., with feedback from T.L., D.M. and K.M. Supervision, A.J.M., E.J.W., K.S., Z.M. and N.R.Z.

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Correspondence to Zongming Ma or Nancy R. Zhang.

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Extended data

Extended Data Fig. 1 Benchmarking of Harmony and CellANOVA integrations of single-cell samples measured with multiple technologies on the NSCLC atlas dataset.

Benchmarking of Harmony and CellANOVA integrations of single-cell samples measured with multiple technologies on the NSCLC atlas dataset32. Harmony is used in the first step of CellANOVA. (a) UMAP visualizations of uncorrected data, Harmony-integrated data, and CellANOVA-integrated data. Each sample was treated as a separate batch. UMAPs were colored by technology, sample origin, and cell type in the top, the middle, and the bottom row, respectively. (b) Comparisons of batch correction (left), gene-wise distortion (middle) and global distortion (right) between Harmony and CellANOVA (scRNA-seq data: 20000 cells, 26023 genes; snRNA-seq data: 20000 cells, 30899 genes.) Left: Boxplots of iLISI scores across cells of each hold-out sample based on uncorrected data, Harmony-integrated data, and CellANOVA-integrated data. In each box plot, the box covers the middle 50% of iLISI scores and the whiskers stop at the largest iLISI score value below the upper quartile plus 1.5 * IQR and the smallest iLISI score value above the lower quartile minus 1.5 * IQR. Middle: Comparisons of p-values obtained from DE analysis pre- and post-Harmony integration and pre- and post-CellANOVA integration. Red dashed line corresponds to equal p-values pre- and post-integration. P-values for DEG analysis are calculated using a two-sided Wilcoxon signed-rank test. Right: Violin plots of Pearson correlations between pre- and post-integration gene expressions per cell with Harmony integration (top) and with CellANOVA integration (bottom). (c) Signal preservation evaluation of batch-corrected embeddings by out-of-batch nearest neighbor proportion distributions. We focused on the combined group of ‘T cell CD8’, ‘T cell CD4’, and ‘T regulatory’ cells, considering sample origin as the primary biological condition of interest. For cells with each origin, we computed out-of-batch nearest neighbor proportions across all sample origins. With successful signal preservation, cells with the same biological condition should be enriched in out-of-batch nearest neighbors.

Extended Data Fig. 2 Top ten upregulated pathways the disease condition for each specific cell type after Harmony integration without CellANOVA recovery.

Top ten upregulated pathways identified within the disease condition for each specific cell type in the Abedini et al. data and the KPMP data after Harmony integration without CellANOVA recovery. P-values for DEG analysis are calculated using a two-sided Wilcoxon signed-rank test.

Extended Data Fig. 3 Additional evaluation of CellANVOA in multimodal data integration.

Left: Scatter plots of epithelial-mesenchymal transition (EMT) pathway activity versus fibroblast ratio, with p-values obtained from a two-sided t-test of slope in linear regression. Middle: Epithelial-mesenchymal transition pathway activity. Right: Abundance of fibroblast cells from deconvolution.

Extended Data Fig. 4 Comparison of pathway enrichment analysis based on scRNA-seq versus flow cytometry of corresponding markers in NSCLC immunotherapy trial data.

(a) G2-M checkpoint pathway enrichment (scRNA-seq) versus Ki-67 frequency (flow cytometry). A positive normalized enrichment score (NES) from GSEA indicates higher pathway enrichment in the later time points. Both Ki-67 and G2-M checkpoint pathway activity measure cell proliferation. For the aPD1 group, comparing Ki-67 frequency (flow cytometry), the Chi-squared test yielded a p-value of 0.03824 between cycle 1 and cycle 2, and a p-value of 0.00043 between cycle 2 and cycle 4. (b) Interferon alpha/gamma response pathway enrichment (scRNA-seq) versus ISG15 mean fluorescence intensity (flow cytometry). For the aPD1 group, comparing between cycle 1 and cycle 2, the Chi-squared test yielded a p-value of 0.07008. (MFI: mean fluorescence intensity). (c) Cell-subtype-specific gene set enrichment analysis within each response group between cycle 2 and cycle 4 after Harmony-based CellANOVA integration. Top 5 up-regulated and down-regulated pathways in cycle 4 compared to cycle 2 are shown. P-value for DEG analysis are calculated using a two-sided Wilcoxon signed-rank test.

Extended Data Fig. 5 Benchmarking analysis on type 1 diabetes dataset.

(a) Comparison of methods from three complementary perspectives: batch correction, cell type prediction, and preservation of biological signals beyond cell type. (b) Performance of each method measured by different metrics from each perspective and overall performance percentiles of those methods. Performance scores for individual metrics are rank-based and have been normalized within a 0 to 1 range. For each metric, a higher score indicates better performance.

Extended Data Fig. 6 Benchmarking analysis on immunotherapy trial dataset.

(a) Comparison of methods from three complementary perspectives: batch correction, cell type prediction, and preservation of biological signals beyond cell type. (b) Performance of each method measured by different metrics from each perspective and overall performance percentiles of those methods. Performance scores for individual metrics are rank-based and have been normalized within a 0 to 1 range. For each metric, a higher score indicates better performance.

Extended Data Fig. 7 Benchmarking analysis on mouse radiation dataset.

(a) Comparison of methods from three complementary perspectives: batch correction, cell type prediction, and preservation of biological signals beyond cell type. (b) Performance of each method measured by different metrics from each perspective and overall performance percentiles of those methods. Performance scores for individual metrics are rank-based and have been normalized within a 0 to 1 range. For each metric, a higher score indicates better performance.

Extended Data Fig. 8 Benchmarking analysis on human kidney multi-omics dataset.

(a) Comparison of methods from three complementary perspectives: batch correction, cell type prediction, and preservation of biological signals beyond cell type. (b) Performance of each method measured by different metrics from each perspective and overall performance percentiles of those methods. Performance scores for individual metrics are rank-based and have been normalized within a 0 to 1 range. For each metric, a higher score indicates better performance.

Extended Data Fig. 9 Additional benchmarking of CellANOVA and other batch correction methods on real and simulated datasets.

(a) UMAP visualizations of the ‘hold-one-celltype-out’ experiment using immunotherapy data before and after batch correction, colored by cell type, condition, and batch. (b) Box plots showing the LISI score of cells from the control pool (left) and cells belonging to treatment-specific cell type Terminal Effector (right) in the experiment in (a) (30153 cells, 18126 genes). In each box plot, the box covers the middle 50% of iLISI scores and the whiskers stop at the largest iLISI score value below the upper quartile plus 1.5 * IQR and the smallest iLISI score value above the lower quartile minus 1.5 * IQR. (c) UMAP visualizations of simulated data before and after batch correction, colored by cell type, condition, and batch. The simulation setting is described in the Section ‘Additional benchmarks and cell type hold-out experiments’ of the main article. (d) Box plots showing the LISI score of cells from the control pool (left) and cells belonging to treatment-specific cell type CT6 (right) in the simulation in (b). Box plots constructed in the same way as in (b). (simulation data: 21000 cells, 5000 genes). (e) ROC curves obtained from differential expression analysis between control and treatment groups, using the batch-corrected expressions of CT6 cells from different integration methods.

Extended Data Fig. 10 Descriptive analysis of CellANOVA-estimated batch effects with initial integration by Seurat.

(a) UMAP visualizations of batch effects estimated by Seurat-based CellANOVA on three datasets, colored by batch and cell type. (b) Top ten batch-affected pathways of each study based on batch-susceptibility score (BSS) with Seurat-based CellANOVA. P-values are calculated using a two-sided Wilcoxon signed-rank test.

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Zhang, Z., Mathew, D., Lim, T.L. et al. Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02463-1

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