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Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender

Nature Methods volume 20, pages 1323–1335 (2023)Cite this article

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Abstract

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.

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Fig. 1: The phenomenology of ambient RNA and its deep generative modeling using CellBender remove-background.
Fig. 2: Evaluation of CellBender on a PBMC dataset, showing a standard SCANPY analysis of the publicly available 10x Genomics dataset pbmc8k with and without CellBender.
Fig. 3: Removal of background RNA from a published human heart snRNA-seq atlas, heart600k, using CellBender.
Fig. 4: Comparing four cell-calling algorithms (CellRanger version 3, dropkick, EmptyDrops and CellBender) on the rat6k snRNA-seq dataset.
Fig. 5: Benchmarking CellBender on denoising the hgmm12k human–mouse mixture dataset and a simulated dataset with differently sized cells.
Fig. 6: Performance of CellBender on denoising a CITE-seq PBMC dataset from 10x Genomics (pbmc5k).

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Data availability

The datasets used in this study are the following: pbmc8k (the publicly available pbmc8k dataset from 10x Genomics called ‘8k PBMCs from a healthy donor’, run with version 2 chemistry and analyzed with CellRanger version 2.1.0, available at https://www.10xgenomics.com/resources/datasets/8-k-pbm-cs-from-a-healthy-donor-2-standard-2-1-0); heart600k (the published dataset from the Broad–Bayer PCL called ‘Single-nuclei profiling of human dilated and hypertrophic cardiomyopathy’ (ref. 23), run with 10x Genomics 3′ capture version 3 chemistry and analyzed with CellRanger version 4.0.0, available at https://singlecell.broadinstitute.org/single_cell/study/SCP1303); hgmm12k (the publicly available hgmm12k dataset from 10x Genomics called ‘12k 1:1 Mixture of Fresh Frozen Human (HEK293T) and Mouse (NIH3T3) Cells’, run with version 2 chemistry and analyzed with CellRanger version 2.1.0, available at https://www.10xgenomics.com/resources/datasets/12-k-1-1-mixture-of-fresh-frozen-human-hek-293-t-and-mouse-nih-3-t-3-cells-2-standard-2-1-0); pbmc5k (the publicly available pbmc5k dataset with antibodies from 10x Genomics called ‘5k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor with a Panel of TotalSeq™-B Antibodies (Next GEM)’, run with version 3 Next GEM chemistry and analyzed with CellRanger version 3.1.0, available at https://www.10xgenomics.com/resources/datasets/5-k-peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-with-cell-surface-proteins-next-gem-3-1-standard-3-1-0); and rat6k (an snRNA-seq dataset from a healthy Wistar rat left atrium, comprising approximately 6,000 nuclei, processed on the 10x Genomics platform using version 2 chemistry and analyzed with CellRanger version 3.1.0. The dataset was provided by P.T.E.’s group at the Broad Institute as part of the Broad–Bayer PCL. The experiment was performed by A.A. and A.-D.A. The dataset is publicly available on Broad’s Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP2148). Datasets analyzed only in the Supplementary Information are as follows: smartseq3xpress_pbmc (a Smart-seq3xpress (well-based) scRNA-seq dataset from healthy human PBMCs called ‘Scalable full-transcript coverage single-cell RNA sequencing of PBMCs using Smart-seq3xpress’ and published by Hagemann-Jensen et al.59. This dataset was kindly provided to the authors in count matrix format by C. Ziegenhain, an author of the referenced paper. We subsetted the data to the 16 384-well plates that came from ‘donor8’ and fluentbio_pbmc (the publicly available scRNA-seq dataset of healthy human PBMCs from Fluent BioSciences called ‘Profiling 20k Immune Cells in Healthy PBMCs from a Single T20 Reaction’, generated with T20 PIPseq and analyzed with PIPseeker version 1.1.3 by Fluent Biosciences60, available at https://fbs-public.s3.us-east-2.amazonaws.com/public-datasets/pbmc/raw_matrix.tar.gz).

Code availability

CellBender can be obtained from https://github.com/broadinstitute/CellBender. Additional documentation is available at https://cellbender.readthedocs.io. CellBender modules are also available as workflows on Terra (https://app.terra.bio), a secure open platform for collaborative omic analysis, and can be run on the cloud with zero set-up. We have implemented the model and the inference method using Pyro probabilistic programming language16 and PyTorch61 and presented it as a user-friendly, production-grade and stand-alone command-line tool. We refer to the background noise-removal algorithm implemented in CellBender as remove-background.

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Acknowledgements

We thank L. D’Alessio, C. Roselli, C. Porter, E. Bingham, F. Obermeyer, J. Nemesh, B. Wang, B. Babadi, V. Popic, A. Wysoker, A. Subramanian, N. Tucker, Y. Farjoun, T. Tickle and A. Carr for insightful discussions at various stages of this project. S.J.F., M.D.C. and M.B. acknowledge financial support from the Broad–Bayer PCL. M.B. acknowledges additional support from the SPARC grant ‘Development of Production-Grade Computational Methods for Single-Cell Genomics’ from the Broad Institute. The publicly available rat6k snRNA-seq dataset was generated by the PCL, and the experiment was performed by A.A. and A.-D.A. We additionally thank C. Ziegenhain for providing a count matrix for the published Smart-seq3xpress PBMC dataset analyzed in Supplementary Section 2.4.

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Author notes

  1. Alessandro Arduini

    Present address: Bayer US, LLC, Cambridge, MA, USA

Authors and Affiliations

  1. Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Stephen J. Fleming, Eric Banks, Anthony A. Philippakis & Mehrtash Babadi

  2. Precision Cardiology Laboratory (PCL), Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Stephen J. Fleming, Mark D. Chaffin, Alessandro Arduini, Patrick T. Ellinor & Mehrtash Babadi

  3. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mark D. Chaffin & Patrick T. Ellinor

  4. Precision Cardiology Laboratory (PCL), Bayer US, LLC, Cambridge, MA, USA

    Amer-Denis Akkad

  5. Wellcome Sanger Institute, Hinxton, Cambridge, UK

    John C. Marioni

  6. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK

    John C. Marioni

  7. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Patrick T. Ellinor

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Contributions

S.J.F. and M.B. jointly developed the probabilistic model, software and study design and jointly wrote the paper. S.J.F. additionally performed statistical analyses on real and simulated data. A.A. and A.-D.A. collected the rat6k dataset under the supervision of P.T.E. M.D.C. provided critical feedback at various stages of the project and analyzed the heart600k dataset. M.B. and P.T.E. jointly supervised the project, with additional input from A.A.P., J.C.M. and E.B.

Corresponding authors

Correspondence to Stephen J. Fleming or Mehrtash Babadi.

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Competing interests

A.-D.A. is an employee of Bayer US LLC (a subsidiary of Bayer AG) and may own stock in Bayer AG. A.A.P. is employed as a venture partner at Google Ventures, and he is also supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. P.T.E. has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. The other authors declare no competing interests.

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Nature Methods thanks Eran Mukamel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editors: Lei Tang and Lin Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 The CellBender model.

(a) The CellBender generative model for noisy single-cell count data. (b) The variational posterior used by CellBender. The neural network NNenc takes the observed data as input and yields the parameters of various variational distributions assumed for the local latent variables. The global latent variables are treated in the usual mean-field approximation.

Extended Data Fig. 2 Violin plots showing the count distributions of lysozyme, LYZ, per cluster before and after CellBender denoising.

(nFPR was 0.01.) The off-target counts are effectively removed, with counts remaining in clusters 0 (CD14+ monocytes C), 10 (FCGR3A+ monocytes NC), and 12 (plasmacytoid dendritic cells).

Extended Data Fig. 3 UMAPs created from the CellBender-analyzed pbmc8k data, showing increased expression specificity of marker genes for different cell types after CellBender denoising as compared to the raw data.

a–d, UMAP plots of the expression of NKG7, CST3, AIF1 and LST1 in each cell before and after CellBender.

Extended Data Fig. 4 UMI curves from the raw data together with various CellBender outputs for the pbmc8k and rat6k datasets.

(a-d) pbmc8k, and (e-h) rat6k. (a,e) The raw UMI curves, annotated with areas of cells and empty droplets. Notably, the distinction is much more difficult in (e), the nuclei dataset extracted from heart tissue. (b,f) Cells probabilities inferred by CellBender on same UMI curves from (a,e) respectively. The region of transition from “surely-cell” to “surely-empty” is much broader in the snRNA-seq dataset. (c,g) First two principal components of the latent gene expression embedding inferred by CellBender, colored by Leiden clustering from a separate scanpy analysis. The structure very closely reflects the labels attributed by that separate analysis. (d,h) Scatter plots showing removal of each gene by CellBender (each dot is a gene, MALAT1 is off-scale). Several top denoised genes are indicated.

Extended Data Fig. 5 Presence of doublets does not impact the denoising performance of CellBender.

(a,c,e) Simulated dataset without doublets. (b,d,f) Simulated dataset where 20% of the cell-containing droplets are doublets. (a) UMAP of the gene expression profile of the three simulated cell types. (b) Same as (a), but including doublets, which are highlighted in bold color. Doublets with cells of two different types form their own clusters in UMAP space, due to their unique transcriptional profile. (c) The learned CellBender prior on gene expression, visualized via PCA, shows three clusters for the three cell types. (d) With doublets present, the prior on gene expression now additionally contains clusters for each type of doublet. From the standpoint of CellBender, a doublet is like a unique cell type. (e,f) Denoising performance has been quantified using a ROC curve, and shows that denoising metrics are nearly identical (TPR 0.750, FPR 0.041) whether doublets are present or not. The error bars shown in panels e-f correspond to the interquartile range of TPR (vertical) and FPR (horizontal) over N=2400 simulated cells.

Extended Data Fig. 6 Published human scRNA-seq PBMC dataset from the well-based Smart-seq3xpress protocol59.

This dataset is extremely clean to begin with. The UMAP shows the expected cell types, nicely clustered. The two dotplots show expression of immune cell marker genes before and after CellBender. Some genes show improvement, but many look quite similar, as expected for a clean dataset. UMAP plots on the right show cleanup of a few genes after CellBender.

Extended Data Fig. 7 Publicly available human scRNA-seq PBMC dataset from the Fluent Biosciences PIPseq platform60.

Droplets are generated by vigorous vortexing, and thus we expect more ambient RNA than a microfluidics experiment. The UMAP shows the expected cell types, in addition to some probable doublets. The two dotplots show expression of immune cell marker genes before and after CellBender. Many genes show significant cleanup. UMAP plots on the right show rather marked cleanup of a few genes after CellBender.

Extended Data Fig. 8 Systematic background noise as a source of batch variation and spurious differential expression across batches.

(a) Setup of the cohort of simulated datasets, where there are two cell types whose expression profiles are taken from real data (rat6k) for cardiomyocytes and fibroblasts. The only difference between simulations from batch A and batch B is the number of cardiomyocytes. Noise ends up being different in the two batches due to these cell number differences. The “truth” in this simulated cohort is that there are no differences between a cell type’s expression profile between batches. (b-d) Raw data. (e-g) CellBender denoised data. (b) Dotplot showing top cardiomyocyte and fibroblast marker genes. Background noise causes marker genes to show up in the off-target cell type at a low level. (e) Marked cleanup of the dataset at an aggregate level. (c,f) The cardiomyocytes show no differentially expressed genes between batch A and B, before or after CellBender. (d) In the raw data, many genes show up as being significantly differentially-expressed due to background noise. (g) After CellBender, these spurious results have disappeared (a few of which are labeled). Benjamini-Hochberg-corrected FDR value for significance (red dotted line) is 0.01 in all volcano plots.

Extended Data Fig. 9 Comparison of output summarization methods for constructing an integer count matrix.

Methods are discussed in Supplementary Sections 5.5 (legend label MCKP), 5.6 (legend label Posteior CDF), and 5.7 (legend labels PR-μ and PR-q). The four panels show four different ways to compute TPR and FPR to display a ROC curve. “Macro-averaged per cell” computes TPR as (∑gTPng)/(∑gTPng + FNng), while “micro-averaged per cell" computes TPR as ∑g[TPng/(TPng + FNng)]. For the “per gene” cases, the sum over genes is replaced by a sum over cells. We exclude genes whose raw data counts are less than 10 summed over all cells. The dots shown represent the mean over all cells or genes as appropriate.

Extended Data Fig. 10 Comparison of per-gene performance of different noise estimation methods.

Methods are discussed in Supplementary Sections 4.5 (MCKP), 4.6 (Posterior CDF), and 4.7 (PR-μ and PR-q). Each plot shows the over-removal of each gene (fraction removed - fraction that should have been removed according to truth) for the given method with the hyperparameter setting specified in the title. Each dot is a gene. Positive values indicate that too many counts of the gene were removed at the level of the entire experiment. Row 1 column 1 shows the posterior mode, row 2 column 1 shows the posterior mean, and row 3 column 1 shows a single sample from the unregularized posterior (α = 0).

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Supplementary Information

Supplementary Figs. 1–10, Discussion and Tables 1–6.

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Fleming, S.J., Chaffin, M.D., Arduini, A. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat Methods 20, 1323–1335 (2023). https://doi.org/10.1038/s41592-023-01943-7

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