Gene length and detection bias in single cell RNA sequencing protocols

single-cell
rna-seq
gene length
Authors

Belinda Phipson

Luke Zappia

Alicia Oshlack

Date

April 1, 2017

Links
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Abstract

Background

Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates.

Methods

Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species.

Results

We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer.

Conclusions

We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

Citation

BibTeX citation:
@article{phipson2017,
  author = {Phipson, Belinda and Zappia, Luke and Oshlack, Alicia},
  title = {Gene Length and Detection Bias in Single Cell {RNA}
    Sequencing Protocols},
  journal = {F1000Research},
  volume = {6},
  date = {2017-04-01},
  url = {https://lazappi.id.au/publications/2017-phipson-gene-length},
  doi = {10.12688/f1000research.11290.1},
  langid = {en},
  abstract = {**Background** Single cell RNA sequencing (scRNA-seq) has
    rapidly gained popularity for profiling transcriptomes of hundreds
    to thousands of single cells. This technology has led to the
    discovery of novel cell types and revealed insights into the
    development of complex tissues. However, many technical challenges
    need to be overcome during data generation. Due to minute amounts of
    starting material, samples undergo extensive amplification,
    increasing technical variability. A solution for mitigating
    amplification biases is to include unique molecular identifiers
    (UMIs), which tag individual molecules. Transcript abundances are
    then estimated from the number of unique UMIs aligning to a specific
    gene, with PCR duplicates resulting in copies of the UMI not
    included in expression estimates. **Methods** Here we investigate
    the effect of gene length bias in scRNA-Seq across a variety of
    datasets that differ in terms of capture technology, library
    preparation, cell types and species. **Results** We find that
    scRNA-seq datasets that have been sequenced using a full-length
    transcript protocol exhibit gene length bias akin to bulk RNA-seq
    data. Specifically, shorter genes tend to have lower counts and a
    higher rate of dropout. In contrast, protocols that include UMIs do
    not exhibit gene length bias, with a mostly uniform rate of dropout
    across genes of varying length. Across four different scRNA-Seq
    datasets profiling mouse embryonic stem cells (mESCs), we found the
    subset of genes that are only detected in the UMI datasets tended to
    be shorter, while the subset of genes detected only in the
    full-length datasets tended to be longer. **Conclusions** We find
    that the choice of scRNA-seq protocol influences the detection rate
    of genes, and that full-length datasets exhibit gene-length bias. In
    addition, despite clear differences between UMI and full-length
    transcript data, we illustrate that full-length and UMI data can be
    combined to reveal the underlying biology influencing expression of
    mESCs.}
}
For attribution, please cite this work as:
Phipson, Belinda, Luke Zappia, and Alicia Oshlack. 2017. “Gene Length and Detection Bias in Single Cell RNA Sequencing Protocols.” F1000Research 6 (April). https://doi.org/10.12688/f1000research.11290.1.