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Waste not, want not: revisiting the analysis that called into question the practice of rarefaction.

Patrick D Schloss
Published in: mSphere (2023)
Over the past 10 years, the best method for normalizing the sequencing depth of samples characterized by 16S rRNA gene sequencing has been contentious. An often cited article by McMurdie and Holmes forcefully argued that rarefying the number of sequence counts was "inadmissible" and should not be employed. However, I identified a number of problems with the design of their simulations and analysis that compromised their results. In fact, when I reproduced and expanded upon their analysis, it was clear that rarefaction was actually the most robust approach for controlling for uneven sequencing effort across samples. Rarefaction limits the rate of falsely detecting and rejecting differences between treatment groups. Far from being "inadmissible", rarefaction is a valuable tool for analyzing microbiome sequence data.
Keyphrases
  • single cell
  • healthcare
  • primary care
  • mental health
  • machine learning
  • genome wide
  • copy number
  • molecular dynamics
  • transcription factor
  • quality improvement
  • smoking cessation
  • data analysis