q2-repeat-rarefy: QIIME2 plugin for generating the average rarefied table for library size normalization using repeated rarefaction
- When handling a sparse dataset, I noticed that the rare taxa were easily ignored by the traditional one-shot rarefaction.
- To deal with this problem, I proposed the “Average Rarefied Table” method and wrote a very simple plugin (reference: https://github.com/qiime2/q2-feature-table/tree/master/q2_feature_table/_normalize.py)).
- Repeat rarefy simply runs random rarefaction N times, and computes the average count (floats are round up) of each OTU (ASV/feature) to generate the final average rarefied OTU table.
- It proves that comparing with the one-shot rarefaction, using repeat rarefy to normalize library size can keep significantly more OTUs (unpublished results).
- As the float average count of OTU is round up, the total OTU count of each sample may not be exactly the same.
- This method has the potential to be an ideal alternative to the current one-shot rarefaction, as it can keep information and avoid variation of composition.
- In addition to OTU (ASV/feature) table, the “Average Rarefied Table” method can also be extended to other profile tables (e.g., taxonomic profile table, gene profile table).
conda activate qiime2-2020.11 pip install git+https://github.com/yxia0125/q2-repeat-rarefy.git
Type “qiime repeat-rarefy” to test if the installation is successful.
pip uninstall q2-repeat-rarefy
qiime repeat-rarefy repeat-rarefy --i-table table.qza \ --p-sampling-depth 2000 \ --p-repeat-times 100 \ --o-rarefied-table average_rarefied_table.qza
The above example rarefied the ‘table.qza’, with the sampling depth of 2000 and the repeat times of 100, to ‘average_rarefied_table.qza’.
You can set the sampling depth based on your own dataset and increase repeat times to 1,000, 10,000 …
If you are interested to use this method, please include the following citation:
Yao Xia, q2-repeat-rarefy: QIIME2 plugin for generating the average rarefied table for library size normalization using repeated rarefaction, (2021), GitHub repository, https://github.com/yxia0125/q2-repeat-rarefy.