https://github.com/randel/MixRF

A random-forest-based approach for imputing clustered incomplete data
https://github.com/randel/MixRF

gene-expression imputation mixed-models random-forest

Last synced: 3 days ago
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A random-forest-based approach for imputing clustered incomplete data

• Host: GitHub
• URL: https://github.com/randel/MixRF
• Owner: randel
• Created: 2015-06-13T04:21:18.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2017-05-06T12:56:21.000Z (over 7 years ago)
• Last Synced: 2024-10-28T17:32:47.707Z (16 days ago)
• Topics: gene-expression, imputation, mixed-models, random-forest
• Language: R
• Size: 182 KB
• Stars: 35
• Watchers: 6
• Forks: 14
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
MixRF

=====

### A random-forest-based approach for imputing clustered incomplete data

This `R` package offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

### Installation

- For the stable version from [CRAN](https://cran.r-project.org/web/packages/MixRF/index.html):

```r

install.packages('MixRF')

```

- For the development version (requiring the `devtools` package):

```r

devtools::install_github('randel/MixRF')

```

### Reference

Wang, J., Gamazon, E. R., Pierce, B. L., Stranger B. E., Im, H. K., Gibbons, R. D., Cox, N. J., Nicolae, D. L., & Chen, L. S. (2016). Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. *American Journal of Human Genetics*. http://dx.doi.org/10.1016/j.ajhg.2016.02.020