How to cite the Bioconductor package deco

Campos-Laborie, F. J., Risueño, A., Ortiz-Estévez, M., Rosón-Burgo, B., Droste, C., Fontanillo, C., Loos, R., Sánchez-Santos, J. M., Trotter, M. W., & De Las Rivas, J. (2019). DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling. Bioinformatics, 35(19), 3651–3662.

1.

Campos-Laborie FJ, Risueño A, Ortiz-Estévez M, Rosón-Burgo B, Droste C, Fontanillo C, et al. DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling. Bioinformatics. 2019 Oct 1;35(19):3651–62.

@ARTICLE{Campos-Laborie2019-tf,
  title     = "{DECO}: decompose heterogeneous population cohorts for patient
               stratification and discovery of sample biomarkers using omic
               data profiling",
  author    = "Campos-Laborie, F J and Risue{\~n}o, A and Ortiz-Est{\'e}vez, M
               and Ros{\'o}n-Burgo, B and Droste, C and Fontanillo, C and Loos,
               R and S{\'a}nchez-Santos, J M and Trotter, M W and De Las Rivas,
               J",
  abstract  = "Abstract Motivation Patient and sample diversity is one of the
               main challenges when dealing with clinical cohorts in biomedical
               genomics studies. During last decade, several methods have been
               developed to identify biomarkers assigned to specific
               individuals or subtypes of samples. However, current methods
               still fail to discover markers in complex scenarios where
               heterogeneity or hidden phenotypical factors are present. Here,
               we propose a method to analyze and understand heterogeneous data
               avoiding classical normalization approaches of reducing or
               removing variation. Results DEcomposing heterogeneous Cohorts
               using Omic data profiling (DECO) is a method to find significant
               association among biological features (biomarkers) and samples
               (individuals) analyzing large-scale omic data. The method
               identifies and categorizes biomarkers of specific phenotypic
               conditions based on a recurrent differential analysis integrated
               with a non-symmetrical correspondence analysis. DECO integrates
               both omic data dispersion and predictor--response relationship
               from non-symmetrical correspondence analysis in a unique
               statistic (called h-statistic), allowing the identification of
               closely related sample categories within complex cohorts. The
               performance is demonstrated using simulated data and five
               experimental transcriptomic datasets, and comparing to seven
               other methods. We show DECO greatly enhances the discovery and
               subtle identification of biomarkers, making it especially suited
               for deep and accurate patient stratification. Availability and
               implementation DECO is freely available as an R package
               (including a practical vignette) at Bioconductor repository
               (http://bioconductor.org/packages/deco/). Supplementary
               information Supplementary data are available at Bioinformatics
               online.",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  35,
  number    =  19,
  pages     = "3651--3662",
  month     =  oct,
  year      =  2019,
  url       = "http://dx.doi.org/10.1093/bioinformatics/btz148",
  copyright = "http://creativecommons.org/licenses/by-nc/4.0/",
  language  = "en",
  issn      = "1367-4803, 1460-2059",
  doi       = "10.1093/bioinformatics/btz148"
}

TY  - JOUR
AU  - Campos-Laborie, F J
AU  - Risueño, A
AU  - Ortiz-Estévez, M
AU  - Rosón-Burgo, B
AU  - Droste, C
AU  - Fontanillo, C
AU  - Loos, R
AU  - Sánchez-Santos, J M
AU  - Trotter, M W
AU  - De Las Rivas, J
AD  - Bioinformatics and Functional Genomics Group, Cancer Research Center
      (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones
      Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de
      Unamuno s/n, Salamanca, Spain; Celgene Institute for Translational
      Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93,
      Sevilla, Spain
TI  - DECO: decompose heterogeneous population cohorts for patient
      stratification and discovery of sample biomarkers using omic data
      profiling
T2  - Bioinformatics
VL  - 35
IS  - 19
SP  - 3651-3662
PY  - 2019
DA  - 2019/10/1
PB  - Oxford University Press (OUP)
AB  - Abstract Motivation Patient and sample diversity is one of the main
      challenges when dealing with clinical cohorts in biomedical genomics
      studies. During last decade, several methods have been developed to
      identify biomarkers assigned to specific individuals or subtypes of
      samples. However, current methods still fail to discover markers in
      complex scenarios where heterogeneity or hidden phenotypical factors are
      present. Here, we propose a method to analyze and understand heterogeneous
      data avoiding classical normalization approaches of reducing or removing
      variation. Results DEcomposing heterogeneous Cohorts using Omic data
      profiling (DECO) is a method to find significant association among
      biological features (biomarkers) and samples (individuals) analyzing
      large-scale omic data. The method identifies and categorizes biomarkers of
      specific phenotypic conditions based on a recurrent differential analysis
      integrated with a non-symmetrical correspondence analysis. DECO integrates
      both omic data dispersion and predictor–response relationship from
      non-symmetrical correspondence analysis in a unique statistic (called
      h-statistic), allowing the identification of closely related sample
      categories within complex cohorts. The performance is demonstrated using
      simulated data and five experimental transcriptomic datasets, and
      comparing to seven other methods. We show DECO greatly enhances the
      discovery and subtle identification of biomarkers, making it especially
      suited for deep and accurate patient stratification. Availability and
      implementation DECO is freely available as an R package (including a
      practical vignette) at Bioconductor repository
      (http://bioconductor.org/packages/deco/). Supplementary information
      Supplementary data are available at Bioinformatics online.
SN  - 1367-4803
DO  - 10.1093/bioinformatics/btz148
UR  - http://dx.doi.org/10.1093/bioinformatics/btz148
ER  -