https://github.com/ConesaLab/MultiPower

Statistical power studies for multi-omics experiments.
https://github.com/ConesaLab/MultiPower

Last synced: about 8 hours ago
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Statistical power studies for multi-omics experiments.

• Host: GitHub
• URL: https://github.com/ConesaLab/MultiPower
• Owner: ConesaLab
• Created: 2020-01-31T11:43:05.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-04-12T17:12:25.000Z (over 1 year ago)
• Last Synced: 2024-08-02T20:43:40.801Z (3 months ago)
• Language: R
• Homepage:
• Size: 2.04 MB
• Stars: 29
• Watchers: 8
• Forks: 10
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-multi-omics - MultiPower - Tarazona - Sample size in multi-omic experiments - [paper](https://doi.org/10.1038/s41467-020-16937-8) (Software packages and methods / Multi-omics correlation or factor analysis)

README

        
# MultiPower and MultiML: statistical power studies for multi-omics experiments

## MultiPower

The MultiPower R method performs statistical power studies for multi-omics experiments, 

and is designed to assist users in experimental design as well as in the evaluation of already-generated multi-omics datasets. 

More details on the method can be found in our manuscript [[1]](#1) and in the 

[MultiPower User’s Guide](https://github.com/ConesaLab/MultiPower/blob/master/MultiPowerUsersGuide_v2.pdf).

MultiPower is available as an R package, and can be installed as follows:

```

install.packages(“devtools”)

devtools::install_github(“ConesaLab/MultiPower”)

```

### Installing MultiPower dependencies

Some dependencies are required before running MultiPower that can be installed from R via 

`install.packages()` and loaded with `library()`:

- FDRsampsize

- lpSolve

## MultiML

The MultiML method is included as a complementary tool to MultiPower, 

and is designed to help users determine the optimal sample size required to control for 

classification error rates when using one or more omics datasets. 

Details on the MultiML algorithm and its applications can be found in our manuscript [[1]](#1). 

If you are interested in using MultiML for your research, please see this folder(link) 

for scripts and instructions. For detailed information on how to run the tool, please read 

[MultiML's  User Guide](https://github.com/ConesaLab/MultiPower/blob/master/MultiPower_UsersGuide.pdf)

## Citation

If you are using MultiPower or MultiML in your research, please cite the following publication:

[1]

Tarazona, S., Balzano-Nogueira, L., Gómez-Cabrero, D. et al. 

Harmonization of quality metrics and power calculation in multi-omic studies. 

Nat Commun 11, 3092 (2020). https://doi.org/10.1038/s41467-020-16937-8