How to cite the Bioconductor package consensus

Peters, T. J., French, H. J., Bradford, S. T., Pidsley, R., Stirzaker, C., Varinli, H., Nair, S., Qu, W., Song, J., Giles, K. A., Statham, A. L., Speirs, H., Speed, T. P., & Clark, S. J. (2019). Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements. Bioinformatics (Oxford, England), 35(4), 560–570.

1.

Peters TJ, French HJ, Bradford ST, Pidsley R, Stirzaker C, Varinli H, et al. Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements. Bioinformatics. 2019 Feb 15;35(4):560–70.

@ARTICLE{Peters2019-hk,
  title     = "Evaluation of cross-platform and interlaboratory concordance via
               consensus modelling of genomic measurements",
  author    = "Peters, Timothy J and French, Hugh J and Bradford, Stephen T and
               Pidsley, Ruth and Stirzaker, Clare and Varinli, Hilal and Nair,
               Shalima and Qu, Wenjia and Song, Jenny and Giles, Katherine A
               and Statham, Aaron L and Speirs, Helen and Speed, Terence P and
               Clark, Susan J",
  abstract  = "MOTIVATION: A synoptic view of the human genome benefits chiefly
               from the application of nucleic acid sequencing and microarray
               technologies. These platforms allow interrogation of patterns
               such as gene expression and DNA methylation at the vast majority
               of canonical loci, allowing granular insights and opportunities
               for validation of original findings. However, problems arise
               when validating against a ``gold standard'' measurement, since
               this immediately biases all subsequent measurements towards that
               particular technology or protocol. Since all genomic
               measurements are estimates, in the absence of a ``gold
               standard'' we instead empirically assess the measurement
               precision and sensitivity of a large suite of genomic
               technologies via a consensus modelling method called the
               row-linear model. This method is an application of the American
               Society for Testing and Materials Standard E691 for assessing
               interlaboratory precision and sources of variability across
               multiple testing sites. Both cross-platform and cross-locus
               comparisons can be made across all common loci, allowing
               identification of technology- and locus-specific tendencies.
               RESULTS: We assess technologies including the Infinium
               MethylationEPIC BeadChip, whole genome bisulfite sequencing
               (WGBS), two different RNA-Seq protocols (PolyA+ and Ribo-Zero)
               and five different gene expression array platforms. Each
               technology thus is characterised herein, relative to the
               consensus. We showcase a number of applications of the
               row-linear model, including correlation with known interfering
               traits. We demonstrate a clear effect of cross-hybridisation on
               the sensitivity of Infinium methylation arrays. Additionally, we
               perform a true interlaboratory test on a set of samples
               interrogated on the same platform across twenty-one separate
               testing laboratories. AVAILABILITY AND IMPLEMENTATION: A full
               implementation of the row-linear model, plus extra functions for
               visualisation, are found in the R package consensus at
               https://github.com/timpeters82/consensus. SUPPLEMENTARY
               INFORMATION: Supplementary data are available at Bioinformatics
               online.",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  35,
  number    =  4,
  pages     = "560--570",
  month     =  feb,
  year      =  2019,
  url       = "https://academic.oup.com/bioinformatics/article/35/4/560/5063406",
  copyright = "http://creativecommons.org/licenses/by-nc/4.0/",
  language  = "en",
  issn      = "1367-4803, 1367-4811",
  pmid      = "30084929",
  doi       = "10.1093/bioinformatics/bty675",
  pmc       = "PMC6378945"
}

TY  - JOUR
AU  - Peters, Timothy J
AU  - French, Hugh J
AU  - Bradford, Stephen T
AU  - Pidsley, Ruth
AU  - Stirzaker, Clare
AU  - Varinli, Hilal
AU  - Nair, Shalima
AU  - Qu, Wenjia
AU  - Song, Jenny
AU  - Giles, Katherine A
AU  - Statham, Aaron L
AU  - Speirs, Helen
AU  - Speed, Terence P
AU  - Clark, Susan J
AD  - Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan
      Institute of Medical Research, Darlinghurst, NSW, Australia.; Epigenetics
      Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical
      Research, Darlinghurst, NSW, Australia.; South Western Sydney Clinical
      School, Faculty of Medicine, University of New South Wales, Liverpool,
      NSW, Australia.; Epigenetics Laboratory, Genomics and Epigenetics
      Division, Garvan Institute of Medical Research, Darlinghurst, NSW,
      Australia.; CSIRO Health and Biosecurity, North Ryde, NSW, Australia.;
      Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan
      Institute of Medical Research, Darlinghurst, NSW, Australia.; St Vincent's
      Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW, Australia.;
      Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan
      Institute of Medical Research, Darlinghurst, NSW, Australia.; CSIRO Health
      and Biosecurity, North Ryde, NSW, Australia.; Department of Biological
      Sciences, Macquarie University, North Ryde, NSW, Australia.; NSW Ministry
      of Health, LMB 961, North Sydney, NSW, Australia.; Ramaciotti Centre for
      Genomics, University of New South Wales, Randwick, NSW, Australia.;
      Bioinformatics Division, The Walter and Eliza Hall Institute of Medical
      Research, Parkville, VIC, Australia.; Department of Mathematics &
      Statistics, University of Melbourne, Melbourne, VIC, Australia.
TI  - Evaluation of cross-platform and interlaboratory concordance via consensus
      modelling of genomic measurements
T2  - Bioinformatics
VL  - 35
IS  - 4
SP  - 560-570
PY  - 2019
DA  - 2019/2/15
Y2  - 2021/3/4
PB  - Oxford University Press (OUP)
AB  - MOTIVATION: A synoptic view of the human genome benefits chiefly from the
      application of nucleic acid sequencing and microarray technologies. These
      platforms allow interrogation of patterns such as gene expression and DNA
      methylation at the vast majority of canonical loci, allowing granular
      insights and opportunities for validation of original findings. However,
      problems arise when validating against a "gold standard" measurement,
      since this immediately biases all subsequent measurements towards that
      particular technology or protocol. Since all genomic measurements are
      estimates, in the absence of a "gold standard" we instead empirically
      assess the measurement precision and sensitivity of a large suite of
      genomic technologies via a consensus modelling method called the
      row-linear model. This method is an application of the American Society
      for Testing and Materials Standard E691 for assessing interlaboratory
      precision and sources of variability across multiple testing sites. Both
      cross-platform and cross-locus comparisons can be made across all common
      loci, allowing identification of technology- and locus-specific
      tendencies. RESULTS: We assess technologies including the Infinium
      MethylationEPIC BeadChip, whole genome bisulfite sequencing (WGBS), two
      different RNA-Seq protocols (PolyA+ and Ribo-Zero) and five different gene
      expression array platforms. Each technology thus is characterised herein,
      relative to the consensus. We showcase a number of applications of the
      row-linear model, including correlation with known interfering traits. We
      demonstrate a clear effect of cross-hybridisation on the sensitivity of
      Infinium methylation arrays. Additionally, we perform a true
      interlaboratory test on a set of samples interrogated on the same platform
      across twenty-one separate testing laboratories. AVAILABILITY AND
      IMPLEMENTATION: A full implementation of the row-linear model, plus extra
      functions for visualisation, are found in the R package consensus at
      https://github.com/timpeters82/consensus. SUPPLEMENTARY INFORMATION:
      Supplementary data are available at Bioinformatics online.
SN  - 1367-4803
DO  - 10.1093/bioinformatics/bty675
C2  - PMC6378945
UR  - https://academic.oup.com/bioinformatics/article/35/4/560/5063406
UR  - http://dx.doi.org/10.1093/bioinformatics/bty675
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30084929
UR  - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378945
ER  -