FeatureCloud Flimma app

Federated version of state of the art Limma Voom application

Flimma is a federated privacy-aware version of state-of-the art differential expression analysis method limma voom.

Publication: BMC Genome Biology (2021).

FeatureCloud Flimma app is implemented based on exbio.wzw.tum.de/flimma/

Config

flimma:
  local_dataset:
    counts: counts.tsv
    design: design.tsv
  logic:
    mode: file
    dir: .
  axis: 0
  use_smpc: false
  normalization: upper quartile
  min_count: 10
  min_total_count: 15
  group1: Lum
  group2: Basal
  confounders: diagnosis_age,stage
  result:
    table: results.csv
    volcano: volcano

Run Flimma

Prerequisite

To run the Flimma application you should install Docker and featurecloud pip package:

pip install featurecloud

Then either download the Flimma image from featurecloud docker repository:

featurecloud app download featurecloud.ai/flimma 

Or build the app locally:

featurecloud app build featurecloud.ai/fc_flimma 

You can use provided example data or you own data. And provide the desired settings in the config.yml file.

Running app

You can run Flimma as a standalone app in the FeatureCloud test-bed or FeatureCloud Workflow. You can also run the app using CLI:

featurecloud test start --app-image featurecloud.ai/flimma --client-dirs './flimma/c1,./flimma/c2,./flimma/c3' --generic-dir './flimma/generic'

@article{Zolotareva2021,
 doi = {10.1186/s13059-021-02553-2},
 url = {https://doi.org/10.1186/s13059-021-02553-2},
 year = {2021},
 month = dec,
 publisher = {Springer Science and Business Media {LLC}},
 volume = {22},
 number = {1},
 author = {Olga Zolotareva and Reza Nasirigerdeh and Julian Matschinske and Reihaneh Torkzadehmahani and Mohammad Bakhtiari and Tobias Frisch and Julian Sp\"{a}th and David B. Blumenthal and Amir Abbasinejad and Paolo Tieri and Georgios Kaissis and Daniel R\"{u}ckert and Nina K. Wenke and Markus List and Jan Baumbach},
 title = {Flimma: a federated and privacy-aware tool for differential gene expression analysis},
 journal = {Genome Biology}
}
  
@misc{nasirigerdeh2021hyfed,
       title={HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning},
       author={Reza Nasirigerdeh and Reihaneh Torkzadehmahani and Julian Matschinske and Jan Baumbach and Daniel Rueckert and Georgios Kaissis},
       year={2021},
       eprint={2105.10545},
       archivePrefix={arXiv},
       primaryClass={cs.LG}
}