---
title: "references"
date: "`r doc_date()`"
vignette: >
  % \VignetteIndexEntry{References for metilclock using Bioconductor's ExperimentHub}
  % \VignetteEngine{knitr::rmarkdown}
  % \VignetteEncoding{UTF-8}
output: 
  BiocStyle::html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Overview

The methylclockData package is a repository of a few public datasets that
needs the *methylclock* package to estimate chronological and gestational DNA 
methylation (DNAm) age as well as biological age using different methylation clocks.


## Chronological DNAm age (in years)

- **Horvath's clock**: It uses 353 CpGs described in @horvath2013dna. It was trained using 27K and 450K arrays in samples from different tissues. Other three different age-related biomarkers are also computed:
     - **AgeAcDiff** (DNAmAge acceleration difference): Difference between DNAmAge and chronological age.
     - **IEAA** (Intrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age adjusted by cell counts.  
     - **EEAA** (Extrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age. This measure was also known as DNAmAge acceleration residual in the first Horvath's paper.
- **Hannum's clock**: It uses 71 CpGs described in @hannum2013genome. It was trained using 450K array in blood samples. Another are-related biomarer is also computed:
     - **AMAR** (Apparent Methylomic Aging Rate): Measure proposed in @hannum2013genome computed as the ratio between DNAm age and the chronological age.
- **BNN**: It uses Horvath's CpGs to train a Bayesian Neural Network (BNN) to predict DNAm age as described in @alfonso2018.
- **Horvath's skin+blood clock (Horvath2)**: Epigenetic clock for skin and blood cells. It uses 391 CpGs described in @horvath2018epigenetic. It was trained using 450K EPIC arrays in skin and blood sampels.
- **PedBE clock**: Epigenetic clock from buccal epithelial swabs. It's intended purpose is buccal samples from individuals aged 0-20 years old. It uses 84 CpGs described in @mcewen2019pedbe. The authors gathered 1,721 genome-wide DNAm profiles from 11 different cohorts with individuals aged 0 to 20 years old. 
- **Wu's clock**: It uses 111 CpGs described in @wu2019dna. It is designed to predict age in children. It was trained using 27K and 450K.

## Gestational DNAm age (in weeks)

- **Knight's clock**: It uses 148 CpGs described in @knight2016epigenetic. It was trained using 27K and 450K arrays in cord blood samples.
- **Bohlin's clock**: It uses 96 CpGs described in @bohlin2016prediction. It was trained using 450K array in cord blood samples.
- **Mayne's clock**: It uses 62 CpGs described in @mayne2017accelerated. It was trained using 27K and 450K.
- **Lee's clocks**: Three different biological clocks described in @lee2019placental are implemented. It was trained for 450K and EPIC arrays in placenta samples.
     - **RPC clock**: Robust placental clock (RPC). It uses 558 CpG sites.
     - **CPC clock**: Control placental clock (CPC). It usses 546 CpG sites.
     - **Refined RPC clock**: Useful for uncomplicated term pregnancies (e.g. gestational age >36 weeks). It uses 396 CpG sites.


The biological DNAm clocks implemented in our package are:

- **Levine's clock** (also know as PhenoAge): It uses 513 CpGs described in @levine2018epigenetic. It was trained using 27K, 450K and EPIC arrays in blood samples.
- **Telomere Length's clock** (TL): It uses 140 CpGs described in @lu2019dna It was trained using 450K and EPIC arrays in blood samples.


# How to load data

In the below example, we show how one can download this dataset from 
ExperimentHub.

```{r get_experimenthub, warning = FALSE, message=FALSE}
library(ExperimentHub)
library(methylclockData)

# Get experimentHub records
eh <- ExperimentHub()

# Get data about methylclockData experimentHub
pData <- query(eh , "methylclockData")

# Get information rows about methylclockData
df <- mcols(pData)
df

# Retrieve data with Hobarth's clock coefficients
pData["EH6071"]


```

We also implemented some functions to easy access to the different datasets , for example, we can access to Hovarths CpGs to train a Bayesian Neural Network with function `get_cpgs_bn` or to `get_coefHannum` for Hannum's clock coefficients

```{r get_Clocks, warning = FALSE, message=FALSE}

#  Hovarths CpGs to train a Bayesian Neural Network
cpgs.bn <- get_cpgs_bn()
head(cpgs.bn)

# Hannum's clock coefficients
coefHannum <- get_coefHannum()
head(coefHannum)

# Hobarth's clock coefficients
coefHorvath <- get_coefHorvath()
head(coefHorvath)

# Knight's clock coefficients
coefKnightGA <- get_coefKnightGA()
head(coefKnightGA)

# Lee's Gestational Age clock coefficients
coefLeeGA <- get_coefLeeGA()
head(coefLeeGA)

# Levine's clock coefficients
coefLevine <- get_coefLevine()
head(coefLevine)

# Mayne's clock coefficients
coefMayneGA <- get_coefMayneGA()
head(coefMayneGA)

# PedBE's clock coefficients
coefPedBE <- get_coefPedBE()
head(coefPedBE)

#  Horvath's skin+blood clock coefficients
coefSkin <- get_coefSkin()
head(coefSkin)

# Telomere Length clock coefficients
coefTL <- get_coefTL()
head(coefTL)

#  Wu's clock coefficients
Wu <- get_coefWu()
head(Wu)

# # references
references <- get_references()
load(references)

```


For more information in how loading and use of the data, please, refer to [`MethylClock` vignette](https://github.com/isglobal-brge/methylclock) 


# sessionInfo()

```{r sessionInfo}
sessionInfo()
```