To cite noisyr in publications please use this paper:

Ilias Moutsopoulos, Lukas Maischak, Elze Lauzikaite, Sergio A Vasquez Urbina, Eleanor C Williams, Hajk-Georg Drost, Irina I Mohorianu, noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise, Nucleic Acids Research, 2021;, gkab433, https://doi.org/10.1093/nar/gkab433

Corresponding BibTeX entry:

  @Article{,
    title = {noisyR: enhancing biological signal in sequencing datasets
      by characterizing random technical noise},
    author = {Ilias Moutsopoulos and Lukas Maischak and Elze Lauzikaite
      and Sergio A. Vasquez-Urbina and Eleanor C. Williams and
      Hajk-Georg Drost and Irina Mohorianu},
    journal = {Nucleic Acids Research},
    year = {2021},
    month = {06},
    abstract = {High-throughput sequencing enables an unprecedented
      resolution in transcript quantification, at the cost of
      magnifying the impact of technical noise. The consistent
      reduction of random background noise to capture functionally
      meaningful biological signals is still challenging. Intrinsic
      sequencing variability introducing low-level expression
      variations can obscure patterns in downstream analyses. We
      introduce noisyR, a comprehensive noise filter to assess the
      variation in signal distribution and achieve an optimal
      information-consistency across replicates and samples; this
      selection also facilitates meaningful pattern recognition outside
      the background-noise range. noisyR is applicable to count
      matrices and sequencing data; it outputs sample-specific
      signal/noise thresholds and filtered expression matrices. We
      exemplify the effects of minimizing technical noise on several
      datasets, across various sequencing assays: coding, non-coding
      RNAs and interactions, at bulk and single-cell level. An
      immediate consequence of filtering out noise is the convergence
      of predictions (differential-expression calls, enrichment
      analyses and inference of gene regulatory networks) across
      different approaches.},
    issn = {0305-1048},
    doi = {10.1093/nar/gkab433},
    url = {https://doi.org/10.1093/nar/gkab433},
    note = {gkab433},
    eprint =
      {https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkab433/38449279/gkab433.pdf},
  }