Splatter: simulation of single-cell RNA sequencing data

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Abstract

As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.

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The online version of this article (doi:10.1186/s13059-017-1305-0) contains supplementary material, which is available to authorized users.

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featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

, , (2013)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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Counting absolute numbers of molecules using unique molecular identifiers.

Teemu Kivioja, Anna Vähärautio, Kasper Karlsson … (2011)

Counting individual RNA or DNA molecules is difficult because they are hard to copy quantitatively for detection. To overcome this limitation, we applied unique molecular identifiers (UMIs), which make each molecule in a population distinct, to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster. Use of this method can improve accuracy of almost any next-generation sequencing method, including chromatin immunoprecipitation-sequencing, genome assembly, diagnostics and manufacturing-process control and monitoring.

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Human cerebral organoids recapitulate gene expression programs of fetal neocortex development.

J Gray Camp, Farhath Badsha, Marta Florio … (2015)

Cerebral organoids-3D cultures of human cerebral tissue derived from pluripotent stem cells-have emerged as models of human cortical development. However, the extent to which in vitro organoid systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo remains unclear. Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex. Covariation network analysis using the fetal neocortex data reveals known and previously unidentified interactions among genes central to neural progenitor proliferation and neuronal differentiation. In the organoid, we detect diverse progenitors and differentiated cell types of neuronal and mesenchymal lineages and identify cells that derived from regions resembling the fetal neocortex. We find that these organoid cortical cells use gene expression programs remarkably similar to those of the fetal tissue to organize into cerebral cortex-like regions. Our comparison of in vivo and in vitro cortical single-cell transcriptomes illuminates the genetic features underlying human cortical development that can be studied in organoid cultures.

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Author and article information

Contributors

Luke Zappia:

ORCID: http://orcid.org/0000-0001-7744-8565

luke.zappia@mcri.edu.au

Belinda Phipson:

ORCID: http://orcid.org/0000-0002-1711-7454

belinda.phipson@mcri.edu.au

Alicia Oshlack:

ORCID: http://orcid.org/0000-0001-9788-5690

alicia.oshlack@mcri.edu.au

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): 12 September 2017

Publication date PMC-release: 12 September 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 174

Affiliations

[1 ]Murdoch Childrens Research Institute, Royal Children’s Hospital, 50 Flemington Rd, Parkville, VIC 3052 Australia

[2 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, School of Biosciences, The University of Melbourne, ; Parkville, VIC 3010 Australia

Author information

Luke Zappia http://orcid.org/0000-0001-7744-8565

Belinda Phipson http://orcid.org/0000-0002-1711-7454

Alicia Oshlack http://orcid.org/0000-0001-9788-5690

Article

Publisher ID: 1305

DOI: 10.1186/s13059-017-1305-0

PMC ID: 5596896

PubMed ID: 28899397

SO-VID: a065c5e5-e5ce-40fb-87ac-848fd91b2e08

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

History

Date received : 3 May 2017

Date accepted : 22 August 2017

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ScienceOpen disciplines: Genetics

Keywords: single-cell,rna-seq,simulation,software

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ScienceOpen disciplines: Genetics

Keywords: single-cell, rna-seq, simulation, software

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Splatter: simulation of single-cell RNA sequencing data

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Evolutionary Cell Biology

Most cited references 19

featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

Counting absolute numbers of molecules using unique molecular identifiers.

Human cerebral organoids recapitulate gene expression programs of fetal neocortex development.

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Cited by 276

Most referenced authors 2,873