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Tools for the analysis of high-dimensional single-cell RNA sequencing data

Nature Reviews Nephrology volume 16, pages 408–421 (2020)Cite this article

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Abstract

Breakthroughs in the development of high-throughput technologies for profiling transcriptomes at the single-cell level have helped biologists to understand the heterogeneity of cell populations, disease states and developmental lineages. However, these single-cell RNA sequencing (scRNA-seq) technologies generate an extraordinary amount of data, which creates analysis and interpretation challenges. Additionally, scRNA-seq datasets often contain technical sources of noise owing to incomplete RNA capture, PCR amplification biases and/or batch effects specific to the patient or sample. If not addressed, this technical noise can bias the analysis and interpretation of the data. In response to these challenges, a suite of computational tools has been developed to process, analyse and visualize scRNA-seq datasets. Although the specific steps of any given scRNA-seq analysis might differ depending on the biological questions being asked, a core workflow is used in most analyses. Typically, raw sequencing reads are processed into a gene expression matrix that is then normalized and scaled to remove technical noise. Next, cells are grouped according to similarities in their patterns of gene expression, which can be summarized in two or three dimensions for visualization on a scatterplot. These data can then be further analysed to provide an in-depth view of the cell types or developmental trajectories in the sample of interest.

Key points

  • As single-cell RNA sequencing datasets increase in scale and complexity, faster and more efficient computational tools for processing and analysis are required.

  • New computational tools that correct technical and batch effects can unlock additional heterogeneity and enable higher-resolution clustering and trajectory inference.

  • Graph-based methods for clustering and trajectory inference allow for the scalable analysis of large single-cell RNA sequencing datasets.

  • Visualization methods can distort the structure of the data and batch correction methods can reduce cell-type resolution; both methods should therefore be used with care and might require specific parameter tuning for each dataset.

  • High-level biological interpretation, such as cell-type annotation, remains challenging and time-consuming — new automated methods, alongside the creation of single-cell reference atlases, promise to address these issues.

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Fig. 1: Overview of the single-cell RNA sequencing analysis pipeline.
Fig. 2: Pre-processing of single-cell RNA sequencing data.
Fig. 3: Integration of single-cell RNA sequencing data.
Fig. 4: Cell clustering in datasets with discrete cell types.
Fig. 5: Modelling continuous cellular states.
Fig. 6: Local and global structure in a dataset.

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Acknowledgements

The authors were supported by NIH grants U01MH098977, R01HL123755, U54HL145608, UH3DK114933 and R01HG009285.

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  1. Department of Bioengineering, University of California at San Diego, La Jolla, CA, USA

    Yan Wu & Kun Zhang

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All authors researched data for the article, wrote the manuscript, made substantial contributions to discussions of the content and reviewed or edited the manuscript before submission.

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Correspondence to Kun Zhang.

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Y.W. declares no competing interests. K.Z. is a co-founder, equity holder, scientific advisory board member and paid consultant of Singlera Genomics, which has no commercial interests related to this article.

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Related links

Broad Institute online single-cell data browser: https://portals.broadinstitute.org/single_cell

EMBL-EBI online single-cell data browser: https://www.ebi.ac.uk/gxa/sc/home

UCSC online single-cell data browser: https://cells.ucsc.edu/

Glossary

FASTQ file

A text file that stores DNA sequences and their associated quality metrics and metadata; a single sequence in a FASTQ file is called a ‘read’.

Counts matrix

An integer matrix (that is, numerical data arranged in a set of columns and rows) in which the columns typically correspond to cells, whereas the rows correspond to genes; each entry represents the number of molecules of that gene expressed in that cell.

Total counts

The total number of reads or UMIs in a given cell.

Size factor

An estimate of how much variation in sequencing depth or RNA capture efficiency affects the overall quantification of gene expression in a cell.

Over-dispersed genes

Genes that show a greater than expected variance between cells given their average expression, which suggests that they are expressed in a cell-type-specific manner.

Regression model

A model that compares the relationship between two variables. In the context of single-cell RNA sequencing, regression can assess relationships between observed gene expression, and technical and/or biological factors.

Mutual nearest neighbours

(MNNs). Cells from different batches that belong to each other’s set of k-nearest neighbours (that is, cells with the most similar gene expression patterns).

Dimensionality reduction

Summarizing a large set of variables with a smaller set of variables, while retaining as much information as possible.

Embedding

The set of variables that remains after running some form of dimensional reduction.

Dropout

The absence of a detectable gene or transcript in a cell.

Classification

A machine learning task in which an algorithm learns the relevant features that distinguish the different classes of a training dataset to predict the classes of an unknown test dataset.

Cell hashing

A technique that attaches unique molecular barcodes to multiple batches of samples for pooling and processing in one batch, which not only improves the experimental throughput but also reduces technical batch differences.

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Wu, Y., Zhang, K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol 16, 408–421 (2020). https://doi.org/10.1038/s41581-020-0262-0

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