planet

For inferring ancestry, gestational age, cell type proportions, and preeclampsia status from placental DNA methylation array data

https://github.com/wvictor14/planet

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For inferring ancestry, gestational age, cell type proportions, and preeclampsia status from placental DNA methylation array data

README.Rmd

---
output: 
  github_document:
editor_options: 
  chunk_output_type: console
  markdown: 
    wrap: 80
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "inst/figures/README-",
  out.width = "100%"
)
```

# planet 


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`planet` is an R package for inferring **ethnicity** [(1)](#references), **gestational age** 
[(2)](#references), **cell composition** [(3)](#references), and **preeclampsia** [(4)](#references), 
from placental DNA methylation data. 

See full documentation at [victoryuan.com/planet](https://victoryuan.com/planet)

### Installation

Latest Bioconductor release

```{r eval = FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("planet")
```

Or the development version of `planet`:

```{r eval = FALSE}
devtools::install_github('wvictor14/planet')
```

### Usage

See [vignettes](https://victor.rbind.io/planet/articles) for more detailed usage.

#### Example Data

All functions in this package take as input DNAm data the 450k and EPIC DNAm microarray. For best performance I suggest providing unfiltered data normalized with noob and BMIQ. A processed example dataset, `plBetas`, is provided to show the format that this data should be in. The output of all `planet` functions is a `data.frame`.

A quick example of each major function is illustrated with this example data:

```{r, message = F, warning = F}
library(minfi)
library(planet)

#load example data
data(plBetas)
data(plPhenoData) # sample information
```

#### Predict Ethnicity

```{r}
predictEthnicity(plBetas) |>
  head()
```

#### Predict Gestational Age

There are 3 gestational age clocks for placental DNA methylation data from Lee
Y. et al. 2019 (2). To use a specific one, we can use the `type` argument in `predictAge`:

```{r predictAge, dpi = 200}
predictAge(plBetas, type = 'RPC') |>
  head()
```

#### Predict Cell Composition

Reference data to infer cell composition on placental villi DNAm samples (3) can be used with cell deconvolution from minfi or EpiDISH. These are provided in this package as `plCellCpGsThird` and `plCellCpGsFirst` for third trimester (term) and first trimester samples, respectively.

```{r}
data('plCellCpGsThird')

minfi:::projectCellType(
  
  # subset your data to cell cpgs
  plBetas[rownames(plCellCpGsThird),], 
  
  # input the reference cpg matrix
  plCellCpGsThird,
  
  lessThanOne = FALSE) |>
  
  head()
```

#### Predict Preeclampsia

```{r}
# download the model from experimenthub
library(ExperimentHub)
eh <- ExperimentHub()
# query(eh, "eoPredData") # see data

# download BMIQ normalized 450k data for prediction
x_test <- eh[['EH8403']]
preds <- x_test |> predictPreeclampsia()
preds |>  head()
```

### References

1.  [**Yuan V**, Price EM, Del Gobbo G, Mostafavi S, Cox B, Binder AM, et al. Accurate ethnicity prediction from placental DNA methylation data. Epigenetics & Chromatin. 2019 Aug 9;12(1):51.](https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-019-0296-3)

2.  [Lee Y, Choufani S, Weksberg R, Wilson SL, **Yuan V**, et al. Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels. Aging (Albany NY). 2019;11(12):4238–4253. doi:10.18632/aging.102049](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628997/)

3. [**Yuan V**, Hui D, Yin Y, Peñaherrera MS, Beristain AG, Robinson WP. Cell-specific characterization of the placental methylome. BMC Genomics. 2021 Jan 6;22(1):6.](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07186-6)

4. [**Fernández-Boyano I**, A.M. Inkster, **V. Yuan**, W.P. Robinson medRxiv 2023 May](https://www.medrxiv.org/content/10.1101/2023.05.17.23290125v1)

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