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+# 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:
+
+<a id="1">[1]</a>
+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 
+