README.Rmd

---
title: CellDRIFT
subtitle: |
  | Revisiting the bad luck hypothesis: 
  | Cancer risk and aging are linked to replication-driven changes to the epigenome
author: |
  | **Christopher Minteer**, Kyra Thrush, Peter Niimi, Joel Rozowsky, 
  | Jason Liu, Mor Frank, Thomas McCabe, Erin Hofstatter, 
  | Mariya Rozenblit, Lajos Pusztai, Kenneth Beckman, Mark Gerstein, 
  | **Morgan E. Levine**

date: 09.13.2022
output: 
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
    df_print: kable
---

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knitr::opts_chunk$set(
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# CellDRIFT

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The goal of the *CellDRIFT* package is to provide the essential function predictor used to calculate the **CellDRIFT** as described in <[insert-paper-here]>.

## Installation

You can install the development version of CellDRIFT like so:

``` r
devtools::install_github("MorganLevineLab/CellDRIFT")
```

## Usage

This is an example of CellDRIFT run on GSE31848, which we have included in part as exampleDNAm in the package.

```{r example}
library(CellDRIFT)
CellDRIFT(exampleDNAm)
```

## Model Data Details

If you are curious regarding the CellDRIFT model, all components required for its calculation are included as data objects within the package.  

If you would like to look at the composition of the yellow module, you might want to look at the objects in `data(yellow_module_means)` and `data(PCA_yellow)` which give you:  
 - the **CpGs** in the yellow module with the command `names(yellow_module_means)`  
 - the **mean values** that are imputed for missing data as the vector `yellow_module_means`  
 - the **variance** of the model PCs in `PCA_yellow$sdev`  
 - the **rotations** of the CpG basis to the new PC basis in `PCA_yellow$rotation`  
 - the **centering** values in `PCA_yellow$center`  
 - the **original PC training** data in `PCA_yellow$x`  
 
 If you are interested in the tan module, all the same values are available if you simply swap `yellow` for `tan`.