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Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial

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Ctenophores

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2757))

Abstract

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

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Notes

  1. 1.

    https://github.com/tanaylab/MetaCell/

  2. 2.

    https://anndata.readthedocs.io/en/stable/anndata.AnnData.html

  3. 3.

    https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html

  4. 4.

    https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Lists

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Acknowledgments

This work was supported in part by the Human Frontiers Science Program (RGP0060/2017) and National Science Foundation (IOS-1557923) grants to LLM. Research reported in this publication was also supported in part by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS114491 (to LLM). D.R. was supported by the Russian Science Foundation grant (23-14-00050). The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. Yanjun Li and Chaoyue Sun contributed equally with all other contributors.

Authors and Affiliations

  1. Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL, USA

    Yanjun Li

  2. Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL, USA

    Yanjun Li

  3. Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

    Chaoyue Sun & Ruogu Fang

  4. Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia

    Daria Y. Romanova

  5. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Dapeng O. Wu

  6. J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

    Ruogu Fang

  7. Center for Cognitive Aging and Memory, McKnight Brain Institute and NSF Center for Big Learning, University of Florida, Gainesville, FL, USA

    Ruogu Fang

  8. Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA

    Leonid L. Moroz

  9. Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, USA

    Leonid L. Moroz

Authors
  1. Yanjun Li
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  2. Chaoyue Sun
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  3. Daria Y. Romanova
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  4. Dapeng O. Wu
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  5. Ruogu Fang
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  6. Leonid L. Moroz
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Corresponding authors

Correspondence to Yanjun Li , Chaoyue Sun or Leonid L. Moroz .

Editor information

Editors and Affiliations

  1. Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, USA

    Leonid L. Moroz

Appendix

Appendix

1.1 Recommended Books

  • Pattern Recognition and Machine Learning [87]

  • Data Clustering: Algorithms and Applications [88]

  • Introduction to Machine Learning [40]

  • Introduction to Probability Models [89]

  • Python Programming: An Introduction to Computer Science [90]

  • Software for Data Analysis: Programming with R [91]

1.2 PAGA Graph

PAGA algorithm enables the similarity analysis between different partitions (or clusters) generated by the community-detection-based clustering methods. The edge weight or the connectivity in the page graph carries the essential information regarding the similarity. This section briefly introduces the edge weights computation of the PAGA graph. A partitioned directed graph is denotated as G containing e edges and n nodes, and each node corresponds to one cell. For the group i, there are overall ei outgoing edges linked with ni nodes in it. The target coarse-grained PAGA graph is represented as G∗ = (V∗, E∗), where \( {V}^{\ast }=\left\{{v}_1^{\ast },\dots, {v}_M^{\ast}\right\} \) is a set of the M cell groups and e∗ ∈ E∗ is a PAGA edge estimated by the PAGA algorithm. A random variable ϵij is used to describe the number of edges connected from cell group i to cell group j in random connecting situation. p(ϵij) is calculated as:

$$ {\displaystyle \begin{array}{l}p\left({e}_{ij}^{\ast }|{e}_i,{n}_i,{n}_j\right)=\frac{\Omega_{\upvarepsilon_{\mathrm{i}\mathrm{j}\mid {\mathrm{e}}_{\mathrm{i}},{\mathrm{n}}_{\mathrm{j}},\mathrm{n}}}}{\Omega_{\mathrm{total}\mid {\mathrm{e}}_{\mathrm{i}},n}}=\frac{\left(\genfrac{}{}{0pt}{}{e_i}{\epsilon_{ij}}\right){n}_j^{\epsilon_{ij}}{\left(n-{n}_j-1\right)}^{e_i-{\epsilon}_{ij}}}{{\left(n-1\right)}^{e_i}}\\ {}=\left(\genfrac{}{}{0pt}{}{e_i}{\epsilon_{ij}}\right){\left(\frac{n_j}{n-1}\right)}^{\epsilon_{ij}}{\left(1-\frac{n_j}{n-1}\right)}^{e_i-{\epsilon}_{ij}}\kern0.5em \end{array}} $$

The expression of p(ϵij| ei, ni, nj) is a binomial distribution with the expectation \( \frac{e_i{n}_j}{n-1} \) and variance \( \frac{e_i{n}_j\left(n-{n}_j-1\right)}{{\left(n-1\right)}^2} \). A new variable ϵ = ϵij + ϵji is introduced to provide a symmetric metrics for the similarity of two clusters. Suppose the cluster size is large enough so that the binomial distributions can be approximated by Gaussian, then the distribution of ϵ can be approximated as:

$$ \kern1em p\left(\epsilon |{e}_i,{e}_j,{n}_i,{n}_j,n\right)\sim N\left(\epsilon |\frac{e_i{n}_j+{e}_j{n}_i}{n-1},\frac{e_i{n}_j\left(n-{n}_j-1\right)+{e}_j{n}_i\left(n-{n}_i-1\right)}{{\left(n-1\right)}^2}\right) $$

Suppose the actual number of edges between cluster i and cluster j is \( {\epsilon}_{ij}^{\mathrm{sym}} \) and the expected number of edges is \( {\hat{\epsilon}}_{ij}=\frac{e_i{n}_j+{e}_j{n}_i}{n-1} \); the edge weights wij is defined as:

$$ {w}_{ij}=f(x)=\left\{\begin{array}{c}\frac{\epsilon_{ij}^{\mathrm{sym}}}{{\hat{\epsilon}}_{ij}},\kern0.5em \mathrm{if}\ {\epsilon}_{ij}^{\mathrm{sym}}<{\hat{\epsilon}}_{ij}\\ {}1,\kern0.5em \mathrm{otherwise}\end{array}\right. $$

If the number of actual edges is larger than the expected value, the connectivity will be set as 1, the upper bounder of the connectivity value. If the given partitioned graph is nondirected, one can convert it to a bi-directed graph by replacing a single edge with two independent edges pointing to the two linked nodes, respectively. Then, the same PAGA calculation strategy can be applied.

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Li, Y., Sun, C., Romanova, D.Y., Wu, D.O., Fang, R., Moroz, L.L. (2024). Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial. In: Moroz, L.L. (eds) Ctenophores. Methods in Molecular Biology, vol 2757. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3642-8_17

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  • DOI: https://doi.org/10.1007/978-1-0716-3642-8_17

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