# planet <img src="man/figures/logo.png" align="right" height = "100px" style="float:right; height:100px;"/>
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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
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("planet")
```
Or the development version of `planet`:
``` r
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
library(minfi)
library(planet)
#load example data
data(plBetas)
data(plPhenoData) # sample information
```
#### Predict Ethnicity
``` r
predictEthnicity(plBetas) |>
head()
#> 1860 of 1860 predictors present.
#> # A tibble: 6 × 7
#> Sample_ID Predicted_ethnicity_n…¹ Predicted_ethnicity Prob_African Prob_Asian
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 GSM1944936 Caucasian Caucasian 0.00331 0.0164
#> 2 GSM1944939 Caucasian Caucasian 0.000772 0.000514
#> 3 GSM1944942 Caucasian Caucasian 0.000806 0.000699
#> 4 GSM1944944 Caucasian Caucasian 0.000883 0.000792
#> 5 GSM1944946 Caucasian Caucasian 0.000885 0.00130
#> 6 GSM1944948 Caucasian Caucasian 0.000852 0.000973
#> # ℹ abbreviated name: ¹Predicted_ethnicity_nothresh
#> # ℹ 2 more variables: Prob_Caucasian <dbl>, Highest_Prob <dbl>
```
#### 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(plBetas, type = 'RPC') |>
head()
#> 558 of 558 predictors present.
#> [1] 38.46528 33.09680 34.32520 35.50937 37.63910 36.77051
```
#### 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()
#> Trophoblasts Stromal Hofbauer Endothelial nRBC
#> GSM1944936 0.1091279 0.04891919 0.000000e+00 0.08983998 0.05294062
#> GSM1944939 0.2299918 0.00000000 6.680983e-20 0.07888007 0.03374149
#> GSM1944942 0.1934287 0.03483540 0.000000e+00 0.09260353 0.02929310
#> GSM1944944 0.2239896 0.06249135 1.608645e-03 0.11040693 0.04447951
#> GSM1944946 0.1894152 0.07935955 0.000000e+00 0.10587439 0.05407587
#> GSM1944948 0.2045124 0.07657717 0.000000e+00 0.09871149 0.02269798
#> Syncytiotrophoblast
#> GSM1944936 0.6979477
#> GSM1944939 0.6377822
#> GSM1944942 0.6350506
#> GSM1944944 0.5467642
#> GSM1944946 0.6022329
#> GSM1944948 0.6085825
```
#### Predict Preeclampsia
``` r
# download the model from experimenthub
library(ExperimentHub)
#> Loading required package: AnnotationHub
#> Loading required package: BiocFileCache
#> Loading required package: dbplyr
#>
#> Attaching package: 'AnnotationHub'
#> The following object is masked from 'package:Biobase':
#>
#> cache
eh <- ExperimentHub()
# query(eh, "eoPredData") # see data
# download BMIQ normalized 450k data for prediction
x_test <- eh[['EH8403']]
#> see ?eoPredData and browseVignettes('eoPredData') for documentation
#> loading from cache
preds <- x_test |> predictPreeclampsia()
#> see ?eoPredData and browseVignettes('eoPredData') for documentation
#> loading from cache
#> 45 of 45 predictive CpGs present.
#> BMIQ normalization is recommended for best results. If choosing other method, it is recommended to compare results to predictions on BMIQ normalized data.
preds |> head()
#> # A tibble: 6 × 4
#> Sample_ID EOPE `Non-PE Preterm` PE_Status
#> <chr> <dbl> <dbl> <chr>
#> 1 GSM2589533 0.670 0.330 EOPE
#> 2 GSM2589535 0.768 0.232 EOPE
#> 3 GSM2589536 0.807 0.193 EOPE
#> 4 GSM2589538 0.784 0.216 EOPE
#> 5 GSM2589540 0.386 0.614 Normotensive
#> 6 GSM2589541 0.649 0.351 EOPE
```
### 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)
