How to cite the Bioconductor package tximport

tximport is a popular Bioconductor package that is available at https://bioconductor.org/packages/tximport. By citing R packages in your paper you lay the grounds for others to be able to reproduce your analysis and secondly you are acknowledging the time and work people have spent creating the package.

APA citation

Formatted according to the APA Publication Manual 7^th edition. Simply copy it to the References page as is.

APA

The minimal requirement is to cite the R package in text along with the version number. Additionally, you can include the reference list entry the authors of the tximport package have suggested.

Example of an in-text citation

Analysis of the data was done using the tximport package (v1.18.0; Soneson et al., 2015).

Reference list entry

Soneson, C., Love, M. I., & Robinson, M. D. (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research, 4, 1521.

Vancouver citation

Formatted according to Vancouver style. Simply copy it to the references section as is.

Vancouver

Example of an in-text citation

Analysis of the data was done using the tximport package v1.18.0 (1).

Reference list entry

1.
Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015 Dec 30;4:1521.

BibTeX

Reference entry in BibTeX format. Simply copy it to your favorite citation manager.

BibTeX

@ARTICLE{Soneson2015-jb,
  title     = "Differential analyses for {RNA-seq}: transcript-level estimates
               improve gene-level inferences",
  author    = "Soneson, Charlotte and Love, Michael I and Robinson, Mark D",
  abstract  = "High-throughput sequencing of cDNA (RNA-seq) is used extensively
               to characterize the transcriptome of cells. Many transcriptomic
               studies aim at comparing either abundance levels or the
               transcriptome composition between given conditions, and as a
               first step, the sequencing reads must be used as the basis for
               abundance quantification of transcriptomic features of interest,
               such as genes or transcripts. Several different quantification
               approaches have been proposed, ranging from simple counting of
               reads that overlap given genomic regions to more complex
               estimation of underlying transcript abundances. In this paper,
               we show that gene-level abundance estimates and statistical
               inference offer advantages over transcript-level analyses, in
               terms of performance and interpretability. We also illustrate
               that while the presence of differential isoform usage can lead
               to inflated false discovery rates in differential expression
               analyses on simple count matrices and transcript-level abundance
               estimates improve the performance in simulated data, the
               difference is relatively minor in several real data sets.
               Finally, we provide an R package (tximport) to help users
               integrate transcript-level abundance estimates from common
               quantification pipelines into count-based statistical inference
               engines.",
  journal   = "F1000Res.",
  publisher = "F1000 Research Ltd",
  volume    =  4,
  pages     = "1521",
  month     =  dec,
  year      =  2015,
  url       = "http://dx.doi.org/10.12688/f1000research.7563.1",
  language  = "en",
  issn      = "2046-1402",
  doi       = "10.12688/f1000research.7563.1"
}

RIS

Reference entry in RIS format. Simply copy it to your favorite citation manager.

RIS

TY  - JOUR
AU  - Soneson, Charlotte
AU  - Love, Michael I
AU  - Robinson, Mark D
TI  - Differential analyses for RNA-seq: transcript-level estimates improve
      gene-level inferences
T2  - F1000Res.
VL  - 4
SP  - 1521
PY  - 2015
DA  - 2015/12/30
PB  - F1000 Research Ltd
AB  - High-throughput sequencing of cDNA (RNA-seq) is used extensively to
      characterize the transcriptome of cells. Many transcriptomic studies aim
      at comparing either abundance levels or the transcriptome composition
      between given conditions, and as a first step, the sequencing reads must
      be used as the basis for abundance quantification of transcriptomic
      features of interest, such as genes or transcripts. Several different
      quantification approaches have been proposed, ranging from simple counting
      of reads that overlap given genomic regions to more complex estimation of
      underlying transcript abundances. In this paper, we show that gene-level
      abundance estimates and statistical inference offer advantages over
      transcript-level analyses, in terms of performance and interpretability.
      We also illustrate that while the presence of differential isoform usage
      can lead to inflated false discovery rates in differential expression
      analyses on simple count matrices and transcript-level abundance estimates
      improve the performance in simulated data, the difference is relatively
      minor in several real data sets. Finally, we provide an R package
      (tximport) to help users integrate transcript-level abundance estimates
      from common quantification pipelines into count-based statistical
      inference engines.
SN  - 2046-1402
DO  - 10.12688/f1000research.7563.1
UR  - http://dx.doi.org/10.12688/f1000research.7563.1
ER  -

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