netDx is for biomedical researchers who want to integrate multi-modal patient data to predict outcome or patient subtype. netDx builds interpretable machine-learning patient classifiers. Unlike standard machine-learning tools, netDx allows modeling of user-defined biological groups as input features; examples include pathways and co-regulated elements. In addition to patient classification, top-scoring features provide mechanistic insight, helping drive hypothesis generation for downstream experiments. netDx currently provides native support for pathway-level features but can be generalized to any user-defined data type and grouping.
These steps were tested on a 2020 Mac Book Pro with 32 Gb RAM.
- Install Docker, and under Resources for Docker Desktop's settings set Memory to 5 GB.
- In terminal, pull the netDx image from Docker:
docker pull realpailab/netdx - To run the image as a container, save the following code as a bash script named
startDocker.sh:
#!/bin/bash
docker run -d --rm \
-p 8787:8787 \
-e PASSWORD=netdx \
-v /path/to/software/dir:/software \
--name [containerName] \
realpailab/netdx
- Modify the bash script so that it's executable and run it:
chmod u+x startDocker.sh
./startDocker.sh
- Access RStudio by going to your web browser and typing in
localhost:8787, and sign in with the usernamerstudioand passwordnetdx. - You can then run the vignettes in R:
setwd("/home/rstudio/vignettes")
rmarkdown::render("ThreeWayClassifier.Rmd")
netDx is a general-purpose algorithm for building patient classifiers by using patient similarity networks as features. It excels at interpretability and handling missing data. It also allows custom grouping rules for features, notably grouping genes into pathways. It integrates with RCy3 for network visualization of predictive pathways.
As of February 2020, netDx is available via the BioConductor repository. Visit http://bioconductor.org/packages/release/bioc/html/netDx.html to install the package and see worked examples.
Contact Shraddha Pai at [email protected] in case of questions.
References:
- Pai S, Hui S, Isserlin R, Shah MA, Kaka H and GD Bader (2019). netDx: Interpretable patient classification using patient similarity networks. Mol Sys Biol. 15: e8497. Read the paper here.
- Pai S, Weber P, Isserlin R, Kaka H, Hui S, Shah MA, Giudice L, Giugno R, Nøhr AK, Baumbach J, GD Bader (2021). netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks. F1000 Research. 9:1239.