PseudotimeDE: inference of differential gene expression along cell pseudotime with
      well-calibrated 
      <i>p</i>-values from single-cell RNA sequencing data

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Abstract

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-021-02341-y).

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Author and article information

Contributors

Jingyi Jessica Li: jli@stat.ucla.edu

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 29 April 2021

Publication date PMC-release: 29 April 2021

Publication date Collection: 2021

Volume: 22

Electronic Location Identifier: 124

Affiliations

[1 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Bioinformatics Interdepartmental Ph.D. Program, , University of California, ; Los Angeles, CA, 90095-7246 USA

[2 ]GRID grid.266097.c, ISNI 0000 0001 2222 1582, Department of Statistics, , University of California, ; Los Angeles, CA, 90095-1554 USA

[3 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Human Genetics, , University of California, ; Los Angeles, CA, 90095-7088 USA

[4 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Computational Medicine, , University of California, ; Los Angeles, CA, 90095-1766 USA

[5 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Biostatistics, , University of California, ; Los Angeles, 90095-1772 CA USA

Article

Publisher ID: 2341

DOI: 10.1186/s13059-021-02341-y

PMC ID: 8082818

PubMed ID: 33926517

SO-VID: bdc89240-c896-47c4-b848-3d87e75dedc1

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 28 October 2020

Date accepted : 8 April 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000076, Directorate for Biological Sciences;

Award ID: 1846216

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: R01 GM120507

Funded by: FundRef http://dx.doi.org/10.13039/100004331, Johnson and Johnson;

Award ID: WiSTEM2D Award

Funded by: FundRef http://dx.doi.org/10.13039/100000879, Alfred P. Sloan Foundation;

Award ID: Sloan Research Fellowship

Funded by: FundRef http://dx.doi.org/10.13039/100000888, W. M. Keck Foundation;

Award ID: UCLA David Geffen School of Medicine W.M. Keck Foundation Junior Faculty Award

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ScienceOpen disciplines: Genetics

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PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

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RNA drug delivery

Most cited references 58

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

clusterProfiler: an R package for comparing biological themes among gene clusters.

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