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Differential abundance testing on single-cell data using k-nearest neighbor graphs

Nature Biotechnology volume 40, pages 245–253 (2022)Cite this article

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Abstract

Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a k-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell–cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at https://github.com/MarioniLab/miloR.

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Fig. 1: Detecting perturbed cell states as differentially abundant graph neighborhoods.
Fig. 2: Milo outperforms alternative differential abundance testing approaches and controls for false discoveries in the presence of batch effects.
Fig. 3: Milo efficiently scales to large datasets.
Fig. 4: Milo identifies the decline of a fate-biased precursor in the aging mouse thymus.
Fig. 5: Milo identifies the compositional disorder in cirrhotic liver.

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Code availability

Milo is implemented as an open-source package in R (https://github.com/MarioniLab/miloR) and is installable from Bioconductor (≥3.13; http://www.bioconductor.org/packages/release/bioc/html/miloR.html). Code used to generate figures and perform analyses can be found at https://github.com/MarioniLab/milo_analysis_2020.

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Acknowledgements

We thank S. Ghazanfar for feedback on the method; N. Kumasaka for comments on the manuscript; C. Suo, V. Kedlian, R. Elmentaite, J. P. Pett, K. Tuong and B. Stewart for feedback on the software package; and D. Burkhardt, M. Luecken and W. Lewis for discussions on benchmarking. J.C.M. acknowledges core funding from the European Molecular Biology Laboratory and core funding from Cancer Research UK (C9545/A29580), which supports M.D.M. E.D. and S.A.T. acknowledge Wellcome Sanger core funding (WT206194). N.C.H. is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science (ref. 219542/Z/19/Z), the Medical Research Council and a Chan Zuckerberg Initiative Seed Network Grant.

Author information

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Emma Dann, Sarah A. Teichmann & John C. Marioni

  2. Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

    Neil C. Henderson

  3. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

    Neil C. Henderson

  4. Theory of Condensed Matter Group, The Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

  5. European Molecular Biology Laboratory European Bioinformatics Institute, Hinxton, Cambridge, UK

    Michael D. Morgan & John C. Marioni

  6. Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK

    Michael D. Morgan & John C. Marioni

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Contributions

E.D., M.D.M. and J.C.M. conceived the method idea. E.D. and M.D.M. developed the method, wrote the code and performed analyses. E.D., M.D.M., S.A.T. and N.C.H. interpreted the results. E.D., M.D.M., S.A.T., N.C.H. and J.C.M. wrote and approved the manuscript. M.D.M. and J.C.M. oversaw the project.

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Correspondence to Michael D. Morgan or John C. Marioni.

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Competing interests

In the last three years, S.A.T. has received remuneration for consulting and Scientific Advisory Board membership from Genentech, Roche, Biogen, ForesiteLabs and Qiagen. All other authors have no competing interests to declare.

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Peer review information Nature Biotechnology thanks Dana Pe’er, Michael Love and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Benchmarking DA methods on simulated data.

DA analysis performance on KNN graphs from simulated datasets of different topologies: (a) discrete clusters (2700 cells, 3 populations); (b) 1-D linear trajectory (7500 cells, 7 populations); (c) Branching trajectory (7500 cells, 10 populations). Boxplots show the median with interquartile ranges (25–75%); whiskers extend to the largest value no further than 1.5x the interquartile range from the distance from the box, with outlier data points shown beyond this range.

Extended Data Fig. 2 Sensitivity of DA methods to low fold change in abundance.

(a) True positive rate (TPR, top) and false positive rate (FPR, bottom) of DA methods calculated on cells in different bins of P(C1) used to generate condition labels (bin size = 0.05, the number on the x-axis indicates the lower value in the bin). The results for 36 simulations on 2 representative populations (colors) are shown. The filled points indicate the mean of each P(C1) bin. (b) Variability in Milo power is explained by the fraction of true positive cells close to the DA threshold for definition of ground truth. Example distributions of P(C1) for cells detected as true positives (TP) or false negatives (FN) by Milo. Examples for simulations on 2 populations (rows) and 3 simulated fold changes (columns) are shown. (c, d) True Positive Rate (TPR) of DA detection for simulated DA regions of increasing size centred at the same centroid (Erythroid2 (c) and Caudal neuroectoderm (d)). Results for 3 condition simulations per population and fold change are shown.

Extended Data Fig. 3 Comparison of Milo and MELD for abundance fold change estimation.

(a–d) Scatter-plots of the true fold change at the neighbourhood index against the fold change estimated by Milo (A,C) and MELD (B,D), without batch effect (a, b) and with batch effect (magnitude = 0.5) (c, d), where LFC = log(pc’/(1 - pc’)). The neighbourhoods overlapping true DA cells (pc’ greater than the 75% quantile of P(C1) in the mouse gastrulation dataset) are highlighted in red. (e, f) Mean Squared Error (MSE) comparison for MELD and Milo for true negative neighbourhood (e) and true positive neighbourhoods (f), with increasing simulated log-Fold Change and magnitude of batch effect. Each boxplot summarises the results for n=27 simulations. Box plots show the median with interquartile ranges (25–75%); whiskers extend to the largest value no further than 1.5x the interquartile range from the distance from the box, with outlier data points shown beyond this range.

Extended Data Fig. 4 Controlling for batch effects in differential abundance analysis.

(a) In silico batch correction enhances the performance of DA methods in the presence of batch effects: comparison of performance of DA methods with no batch effect, with batch effects of increasing magnitude corrected with MNN, and uncorrected batch effects. Each boxplot summarises results from simulations on n=9 populations. (b) True Positive Rate (TPR, left) and False Discovery Rate (FDR, right) for recovery of cells in simulated DA regions for DA populations with increasing batch effect magnitude on the mouse gastrulation dataset. For each boxplot, results from 8 populations and 3 condition simulations per population are shown (n=24 simulations). Each panel represents a different DA method and a different simulated log-Fold Change. (c) Comparison of Milo performance with (~ batch + condition) or without (~ condition) accounting for the simulated batch in the NB-GLM. For each boxplot, results from 8 populations, simulated fold change > 1.5 and 3 condition simulations per population and fold change are shown (72 simulations per boxplot). In all panels, boxplots show the median with interquartile ranges (25–75%); whiskers extend to the largest value no further than 1.5x the interquartile range from the distance from the box, with outlier data points shown beyond this range.

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Dann, E., Henderson, N.C., Teichmann, S.A. et al. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol 40, 245–253 (2022). https://doi.org/10.1038/s41587-021-01033-z

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