Short title: The benchdamic package: benchmarking of differential abundance methods on microbiome data
Workshop URL: https://mcalgaro93.github.io/benchdamicWorkshop
Workshop docker image: ghcr.io/mcalgaro93/benchdamicworkshop
Workshop port: 8787
Workshop memory request: the image in my docker-desktop app weights a little more than 6GB
Workshop description:
This workshop provides an introductory example on how to work with the analysis framework firstly proposed in the research article titled "Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data" by Calgaro et al. (2020, Genome Biol) and now available in the benchdamic Bioconductor package.
It is shown how to test commonly used methods for differential abundance (DA) analysis on a microbiome dataset and how to set up and benchmark custom methods. Performances of each method are evaluated with respect to i) suitability of distributional assumptions (GOF), ii) ability to control false positives (TIEC), iii) concordance of the findings, and iv) enrichment of DA microbial species in specific conditions.
Please supply the following information:
Short title: The
benchdamic
package: benchmarking of differential abundance methods on microbiome data Workshop URL: https://mcalgaro93.github.io/benchdamicWorkshop Workshop docker image: ghcr.io/mcalgaro93/benchdamicworkshop Workshop port: 8787 Workshop memory request: the image in my docker-desktop app weights a little more than 6GB Workshop description: This workshop provides an introductory example on how to work with the analysis framework firstly proposed in the research article titled "Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data" by Calgaro et al. (2020, Genome Biol) and now available in thebenchdamic
Bioconductor package. It is shown how to test commonly used methods for differential abundance (DA) analysis on a microbiome dataset and how to set up and benchmark custom methods. Performances of each method are evaluated with respect to i) suitability of distributional assumptions (GOF), ii) ability to control false positives (TIEC), iii) concordance of the findings, and iv) enrichment of DA microbial species in specific conditions.