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Best practices for single-cell analysis across modalities

Nature Reviews Genetics volume 24, pages 550–572 (2023)Cite this article

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Abstract

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

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Fig. 1: Single-cell analysis across modalities.
Fig. 2: Overview of unimodal analysis steps for scRNA-seq.
Fig. 3: Overview of scATAC-seq analysis steps.
Fig. 4: Overview of CITE-seq data processing.
Fig. 5: Overview of the adaptive immune receptor analysis.
Fig. 6: Overview of spatial transcriptomics preprocessing and downstream analysis steps.

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Acknowledgements

The authors acknowledge Y. Chen for editing the single-cell RNA-sequencing discussions, Y. Ji for editing the perturbation modelling discussions, A. McKenna for editing the lineage tracing discussions, C. N. Talavera Lopez for providing helpful suggestions for the adaptive immune receptor repertoire discussion, L. B. Kuemmerle for editing the spatial omics discussions, and all members of the Theis group for reviews and helpful discussion. This work was supported by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS18053A, by the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells), by the Wellcome Trust grant 108413/A/15/D and by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (grant number: ZT-I-PF-5-01). Main author list, individual acknowledgements: F.D. is supported by the Helmholtz Association under the joint research school Munich School for Data Science and by the Joachim Herz Stiftung. F.C. acknowledges support from a German Research Foundation (DFG) (SFB-TRR 338/1 2021-452881907), Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells) and by the Deutsche Forschungsgemeinschaft. A.C.S., F.C. and L.Z. acknowledge support from the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells). C.L. is supported by the Helmholtz Association under the joint research school Munich School for Data Science. Single-cell Best Practices Consortium, individual acknowledgements: G.P. and L.D. are supported by the Joachim Herz Stiftung. G.P. is supported by the Helmholtz Association under the joint research school Munich School for Data Science. R.P. acknowledges funding from US NIH (R01 HG009937) and US National Science Foundation (CCF-1750472, and CNS-1763680). L. Hetzel and L.D.M. are supported by the Helmholtz Association under the joint research school Munich School for Data Science. B.S. acknowledges funding from (DFG, German Research Foundation) Projektnummer 490846870-TRR355/1 TPZ02.

Author information

Author notes

  1. These authors contributed equally: Lukas Heumos, Anna C. Schaar.

Authors and Affiliations

  1. Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany

    Lukas Heumos, Anna C. Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zappia, Malte D. Lücken, Daniel C. Strobl, Juan Henao, Fabiola Curion, Hananeh Aliee, Meshal Ansari, Maren Büttner, Leander Dony, Amit Frishberg, Soroor Hediyeh-zadeh, Leon Hetzel, Ignacio L. Ibarra, Mohammad Lotfollahi, Laura D. Martens, Christian L. Müller, Johannes Ostner, Giovanni Palla, Ciro Ramírez-Suástegui, Benjamin Schubert, Lisa Sikkema, Isaac Virshup, Philipp Weiler & Fabian J. Theis

  2. Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany

    Lukas Heumos, Meshal Ansari & Herbert B. Schiller

  3. TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany

    Lukas Heumos, Felix Drost, Daniel C. Strobl, Leander Dony, Giovanni Palla, Lisa Sikkema & Fabian J. Theis

  4. Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany

    Anna C. Schaar, Anastasia Litinetskaya, Luke Zappia, Fabiola Curion, Leon Hetzel, Benjamin Schubert, Philipp Weiler & Fabian J. Theis

  5. Munich Center for Machine Learning, Technical University of Munich, Garching, Germany

    Anna C. Schaar, Leon Hetzel & Fabian J. Theis

  6. Department of Paediatrics, Dr von Hauner Children’s Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany

    Christopher Lance

  7. Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany

    Malte D. Lücken

  8. Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany

    Daniel C. Strobl

  9. TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany

    Daniel C. Strobl

  10. Institute for Computational Biomedicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

    Pau Badia-i-Mompel, Daniel Dimitrov, Julio Saez-Rodriguez & Jovan Tanevski

  11. Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany

    Maren Büttner

  12. Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany

    Maren Büttner

  13. Wellcome Sanger Institute, Hinxton, Cambridge, UK

    Emma Dann & Mohammad Lotfollahi

  14. Department of Translational Psychiatry, Max Planck Institute of Psychiatry, and International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany

    Leander Dony

  15. Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA

    Dongze He

  16. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA

    Dongze He

  17. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA

    Matthew G. Jones

  18. Department of Computer Science, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany

    Laura D. Martens

  19. Department of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany

    Christian L. Müller & Johannes Ostner

  20. Center for Computational Mathematics, Flatiron Institute, New York, NY, USA

    Christian L. Müller

  21. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mor Nitzan & Zoe Piran

  22. Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mor Nitzan

  23. Department of Computer Science, University of Maryland, College Park, MD, USA

    Rob Patro

  24. La Jolla Institute for Immunology, La Jolla, CA, USA

    Ciro Ramírez-Suástegui

  25. Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany

    Julio Saez-Rodriguez

  26. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Hirak Sarkar

  27. New York Genome Center, New York, NY, USA

    Avi Srivastava

Authors
  1. Lukas Heumos
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  2. Anna C. Schaar
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  3. Christopher Lance
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  4. Anastasia Litinetskaya
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  5. Felix Drost
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  6. Luke Zappia
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  7. Malte D. Lücken
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  8. Daniel C. Strobl
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  9. Juan Henao
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  10. Fabiola Curion
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Consortia

Single-cell Best Practices Consortium

  • Hananeh Aliee
  • , Meshal Ansari
  • , Pau Badia-i-Mompel
  • , Maren Büttner
  • , Emma Dann
  • , Daniel Dimitrov
  • , Leander Dony
  • , Amit Frishberg
  • , Dongze He
  • , Soroor Hediyeh-zadeh
  • , Leon Hetzel
  • , Ignacio L. Ibarra
  • , Matthew G. Jones
  • , Mohammad Lotfollahi
  • , Laura D. Martens
  • , Christian L. Müller
  • , Mor Nitzan
  • , Johannes Ostner
  • , Giovanni Palla
  • , Rob Patro
  • , Zoe Piran
  • , Ciro Ramírez-Suástegui
  • , Julio Saez-Rodriguez
  • , Hirak Sarkar
  • , Benjamin Schubert
  • , Lisa Sikkema
  • , Avi Srivastava
  • , Jovan Tanevski
  • , Isaac Virshup
  •  & Philipp Weiler

Contributions

Main author list: A.C.S., L. Heumos and F.J.T. conceived the project. L. Heumos and A.C.S. contributed equally and have the right to list their name first in their curriculum vitae. A.C.S., L. Heumos, C.L. and F.D. wrote the manuscript. L.Z. and M.D.L. provided expertise for the discussion on transcriptomics; C.L. on chromatin accessibility; D.C.S. on surface protein expression; F.D., J.H. and F.C. on adaptive immune receptor repertoire analysis; and A.L. and F.C. on multimodal data integration. F.J.T. and H.B.S. supervised the work. Single-cell Best Practices Consortium: A.F., H.A., I.L.I., L.D., L.S., M.B., M.L., P.W., S.H.-z., Z.P., M.G.J., A.S., H.S., D.H., E.D., J.O., I.V., D.D., R.P., C.L.M., J.S.-R., J.H., P.B.M. and M.N. provided expertise for the discussion on transcriptomics; L.D.M. and I.L.I. on chromatin accessibility; C.R.-S. on surface protein expression; B.S. on adaptive immune receptor repertoire analysis; and G.P., L. Hetzel, J.T. and J.S.-R. on single-cell data resolved in space. M.A. contributed to the figure design. All authors read, edited and approved the final manuscript.

Corresponding author

Correspondence to Fabian J. Theis.

Ethics declarations

Competing interests

Main author list: M.D.L. has received speaker’s honoraria from Pfizer and Janssen, and received consulting fees from Chan-Zuckerberg Initiative. F.J.T. consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. M.G.J. consults for and has ownership interests in Vevo Therapeutics. L. Heumos has received speaker’s honorarium from Vesalius Therapeutics. Single-Cell Best Practices Consortium: M.G.J. consults for and has ownership interests in Vevo Therapeutics. R.P. is co-founder of Ocean Genomics, Inc. The other authors declare no competing interests.

Peer review

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Nature Reviews Genetics thanks Francesca Finotello, Jong-Eun Park and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Single-Cell Best Practices online book: https://sc-best-practices.org

Glossary

Adaptive immune receptor

(AIR). Transmembrane complex of proteins expressed on T and B cells that is key for the recognition of potential hazardous antigens and pathogens invading the body.

Ambient RNA

mRNA counts that originate from other lysed cells in the input solution and do not belong to the cell captured in the droplet itself.

Antibody-derived tags

(ADTs). Antibodies (also known as soluble immunoglobulins) are Y-shaped proteins used by the immune system to identify and neutralize pathogens by recognizing antigens. ADTs are directly conjugated DNA-barcode oligonucleotides that can be used to recover expressed surface proteins.

Antigens

Substances recognized as non-self that induce an immune response and lead to the production of antibodies.

Barcodes

Unique known nucleic acid sequences of fixed length used to label individual cells to enable tracking through space and time.

Batch effects

Confounding effects that result from technical differences in data generation across different batches, such as samples obtained through different experimental set-ups or from different laboratories.

CDR3

Whereas complementarity-determining region 1 (CDR1) and CDR2 are encoded in the germline V genes, CDR3 loops are assembled from V(D)J segments, giving rise to the variability of adaptive immune receptors.

Cell fate

A cell’s final cell type that is established by corresponding, specific transcriptional programmes.

Cell–cell communication

Interactions of cells through secreted ligands and plasma membrane receptors, secreted enzymes, extracellular matrix proteins or cell–cell adhesion proteins and gap junctions.

Cell-type deconvolution

Decomposing the cell-type composition of individual barcode regions based on a reference data set to obtain abundances or proportions of individual cells within a barcode region.

Cell segmentation

Processing of microscopic image domains into segments that represent individual cells.

Chain pairing

Assignment of cells to V(D)J chain types such as orphans, single pair, extra VJ/VDJ or multichains.

Cis-regulatory elements

(CREs). Regions of non-coding DNA — such as promoters, enhancers and silencers — that control the transcription of nearby genes.

Clonotype

Collection of T or B cells that descended from an antecedent cell, have the same adaptive immune receptors and henceforth recognize the same epitopes.

Compositional data

Comprises multi-dimensional data points (for example, cell-type composition) in which each component (or part) carries only proportional or relative abundance information about some whole.

Confounding sources of variation

Technical artefacts that arise from library preparation and sequencing, and biological confounders such as cell cycle status, which cause systematic bias and may distort biological findings.

Differential gene expression

(DGE). The inference of statistically significant differences in expression between groups such as healthy and diseased.

Epitopes

The parts of antigens that are recognized by antibodies, B cells or T cells to potentially stimulate immune responses.

Gene set enrichment

Grouping genes with shared characteristics together and testing for over-representation.

Graph neural networks

A deep-learning approach to do inference on input data represented in the form of a graph. For example, in spatial transcriptomics, cells are typically represented as nodes in graphs obtained through spatial proximity.

Highly variable genes

A measure to identify genes that vary in terms of gene expression across all cells present in the data set.

K nearest-neighbours graph

(KNN graph). A computational data structure in which cells are represented as nodes in a graph. Based on distance metrics such as the Euclidean distance on a principal-component reduced expression, cells are connected to their K most similar cells. K is commonly set to be between 5 and 100 depending on the data set.

Latent semantic indexing

(LSI). A dimension reduction method that uses term frequency inverse document frequency transformation (TFIDF) followed by singular value decomposition (SVD).

Lineage tracing

Tracking physiological or pathological changes by exogenous or endogenous cell markers such as DNA mutations.

Major histocompatibility complex

(MHC). Surface proteins that display or ‘present’ small peptides (epitopes) on the cell surface for T and B cells to potentially react to. Presented endogenous self-antigens prevent the immune system from targeting its own cells, whereas presented pathogen-derived peptides alarm nearby immune cells.

Nucleosome signal

The ratio of long fragments resulting from one or multiple histones bound between the Tn5 transposition sites and short nucleosome-free fragments; the ratio is small in high-quality single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) data.

Optimal transport

Mathematical framework to estimate the optimal transport plan of mass between two (discrete) distributions.

Phase portrait

For any given gene, the phase portrait visualizes splicing kinetics as a parametric curve (with time as a parameter).

Pseudobulks

Aggregated cells within a biological replicate whereby the data from every single cell is combined via sum or mean of counts into a single pseudo-sample to resemble a bulk RNA experiment.

Pseudoreplication

Also known as subsampling. Pseudoreplication occurs when replicates are not statistically independent, but are treated as if they were, such as cell samples from a single individual.

Reference mapping

The process of leveraging and transferring information from a reference data set to a query.

RNA velocity

Ratios of spliced mRNA, unspliced mRNA and mRNA degradation. Positive ratios (velocities) indicate recent increases in unspliced transcripts followed by upregulation of spliced transcripts. Negative velocities indicate downregulation. Examining velocities across genes can provide insight into future states of individual cells.

Scaling

Normalization of gene expression levels that scales gene counts to zero mean and unit variance.

Somatic hypermutation

Mechanism of B cell receptors to allow the immune system to adapt its response to unseen threats. Somatic hypermutation is triggered when B cells engage antigens, which results in the introduction of point mutations in the variable regions of the V(D)J genes. Cells harbouring mutagenized antibodies with a high affinity for the antigen proliferate preferentially (known as affinity maturation).

Spatially variable genes

(SVGs). Genes with variable expression levels between individual locations in the spatial transcriptomics data set.

Spectratyping

Measuring the heterogeneity of complementarity-determining region 3 (CDR3) regions by their length diversity across different cell types or conditions.

Trajectory inference

Also known as pseudotime analysis. Ordering of cells along a trajectory based on gene expression similarity.

Transcription factor motif

(TF motif). DNA sequence pattern that is specifically recognized by a sequence-specific TF. It is commonly represented as a logo diagram representing the most informative DNA positions by height.

Variational autoencoders

A generative artificial neural network architecture that allows for statistical inference. Input data are sampled from a parameterized distribution (prior), and an encoder and decoder are trained jointly to minimize the reconstruction error between the updated prior probability (posterior) and its parametric approximation (variational posterior).

V(D)J recombination

Somatic recombination in developing lymphocytes whereby variable (V), diversity (D) and joining (J) segments are randomly selected and joined to form the V region of a full-length receptor.

V(D)J sequencing

Determination of protein sequence of the adaptive immune receptor (AIR) for both chains, from which the variable (V), diversity (D), joining (J) and constant (C) sequences are determined in addition to the complementarity-determining region (CDR) sequences.

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Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet 24, 550–572 (2023). https://doi.org/10.1038/s41576-023-00586-w

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