mi-Mic: a novel multi-layer statistical test for microbiota-disease associations

Oshrit Shtossel, Shani Finkelstein, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review


mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Original languageEnglish
Article number113
JournalGenome Biology
Issue number1
StatePublished - 1 May 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.


  • 16S
  • Cladogram
  • Image-microbiome
  • Microbiota
  • Nested ANOVA
  • WGS


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