README.Rmd

---
output: github_document
---

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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# phenopredict

The goal of phenopredict is to build predictors from expression data (RNA-Seq) that will predict necessary phenotype information across samples.The phenotypes sex, tissue, sequencing strategy, and sample source have been predicted across the ~70,000 samples currently available in [recount](https://jhubiostatistics.shinyapps.io/recount/).

## Installation

You can install phenopredict from GitHub with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("leekgroup/phenopredict")
```

## Examples using phenopredict

Details of how the predicted phenotypes previously generated for the [recount data](https://jhubiostatistics.shinyapps.io/recount/)) are [available on GitHub](https://github.com/ShanEllis/phenopredict_phenotypes).


## Adding predicted phenotypes in recount

If you're looking to find the phenotype information previously generated across the recount samples, an example of how to use `add_predictions()` within recount is provided below.

```{r example}
library('recount')

download_study('ERP001942', type='rse-gene')
load(file.path('ERP001942', 'rse_gene.Rdata'))
rse <- scale_counts(rse_gene)

rse_with_pred <- add_predictions(rse)
```