Best practices for single-cell analysis across modalities

Heumos, Lukas; Schaar, Anna C.; Lance, Christopher; Litinetskaya, Anastasia; Drost, Felix; Zappia, Luke; Luecken, Malte D.; Strobl, Daniel C.; Henao, Juan D.; Curion, Fabiola; Aliee, Hananeh; Ansari, Meshal; Badia-i-Mompel, Pau; Büttner, Maren; Dann, Emma; Dimitrov, Daniel; Dony, Leander; Frishberg, Amit; He, Dongze; Hediyeh-Zadeh, Soroor; Hetzel, Leon; Ibarra, Ignacio L.; Jones, Matthew G.; Lotfollahi, Mohammad; Martens, Laura D.; Müller, Christian L.; Nitzan, Mor; Ostner, Johannes; Palla, Giovanni; Patro, Rob; Piran, Zoe; Ramírez-Suástegui, Ciro; Sáez-Rodríguez, Julio; Sarkar, Hirak; Schubert, Benjamin; Sikkema, Lisa; Srivastava, Avi; Tanevski, Jovan; Virshup, Isaac; Weiler, Philipp; Schiller, Herbert B.; Theis, Fabian J.

doi:10.1038/s41576-023-00586-w

Cited by 207 publications

(95 citation statements)

References 260 publications

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“…Data from various sources is ingested to be combined and formatted as a MuData object. Specific QC metrics are computed for each modality, following guidelines defined in single-cell 11 best practices [8]. Scrublet is used to compute doublet scores [48].…”

Section: Qcmentioning

confidence: 99%

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Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Rich-Griffin

Curion

Thomas

et al. 2023

Preprint

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Single-cell multiomic analysis of the epigenome, transcriptome and proteome allows for comprehensive characterisation of the molecular circuitry that underpins cell identity, cell state, and cell type-specific gene regulatory networks. Technological advances, whereby multiple omics modalities can be simultaneously profiled in individual cells in a highly parallel manner, are already beginning to provide a step-change in our capacity to comprehend complex tissue biology during development and ageing, in health and disease, and upon treatment. However, the holistic interpretation of such datasets still presents a challenge due to an absence of approaches for the systematic joint analysis and evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate the analysis of multimodal single-cell datasets by incorporating widely used Python-based tools to efficiently perform: quality control, preprocessing, integration, clustering, and reference mapping at scale in the multiomic setting. Panpipes combines established and state-of-the-art methods to allow reliable and customisable analysis and evaluation of multiomic single-cell datasets, enabling users to investigate individual and integrated modalities, and to empower decision-making prior to downstream analyses and data interpretation.

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Section: Qcmentioning

confidence: 99%

“…Best practices for multimodal analysis are now emerging [8], with a wide range of packages and tutorials from which users can develop custom scripts [9,10]. For an end user, a typical analysis workflow could consist of collections of notebooks, which are run interactively and customised for each individual project.…”

Section: Introductionmentioning

confidence: 99%

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Rich-Griffin

Curion

Thomas

et al. 2023

Preprint

Self Cite

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“…Similar developments are taking place for the analyses of these data using bioinformatics tools. A plethora of open-source generic software (reviewed in [177,178]) are under continual development and maintenance, and these are routinely used by computational scientists either directly or as a part of workflow management systems (e.g. Nextflow, Snakemake).…”

Section: Single Cell Omics In Breast Cancer: the Futurementioning

confidence: 99%

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Thakur,

Haider,

Natrajan

2023

The Journal of Pathology

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Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre‐clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.

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“…Thus, our protocol demonstrates the concerted use of both tools, describes the insights they provide, and facilitates the interpretation of their outputs. We base this protocol on recent best practices for single-cell transcriptomics and CCC inference 9 . We begin by processing the key inputs of our tools.…”

Section: Development Of the Protocolmentioning

confidence: 99%

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

Baghdassarian

Dimitrov

Armingol

et al. 2023

Preprint

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In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.

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Best practices for single-cell analysis across modalities

Cited by 207 publications

References 260 publications

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

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