# planet

## Installation

    if(!requireNamespace("BiocManager", quietly = TRUE))
      install.packages("BiocManager")
    BiocManager::install("planet")

## Cell composition

To infer cell composition on placental villi DNAm samples, we can need to use placental reference cpgs (Yuan 2021). These are provided in this package as `plCellCpGsThird` and `plCellCpGsFirst` for third trimester (term) and first trimester samples, respectively.

In this example we are using term villi DNAm data, so we first load the reference cpgs `plCellCpGsThird`. This is a data frame of 600 cpgs, with mean methylation levels for each cell type.

    # cell deconvolution packages
    library(minfi)
    library(EpiDISH)

    # data wrangling and plotting
    library(dplyr)
    library(ggplot2)
    library(tidyr)
    library(planet)

    # load example data
    data("plBetas")
    data("plCellCpGsThird")
    head(plCellCpGsThird)

    ##            Trophoblasts   Stromal  Hofbauer Endothelial      nRBC
    ## cg10590657    0.1014098 0.9345796 0.8655285   0.8963641 0.8448382
    ## cg14923398    0.1282030 0.8902107 0.9036769   0.9383641 0.9508709
    ## cg05348366    0.1305697 0.9519820 0.8803082   0.9065136 0.9278057
    ## cg17907628    0.1215249 0.9278777 0.8727841   0.8914412 0.9143601
    ## cg26799656    0.1259953 0.9482014 0.8803863   0.8791004 0.9010419
    ## cg11862144    0.1561991 0.9430855 0.9114967   0.9341671 0.9647331
    ##            Syncytiotrophoblast
    ## cg10590657          0.05460441
    ## cg14923398          0.05383193
    ## cg05348366          0.06546727
    ## cg17907628          0.05325227
    ## cg26799656          0.06823985
    ## cg11862144          0.06044207

After our reference cpg data is loaded, we can estimate cell composition by applying either the Constrained Projection approach implemented by the R packages minfi or EpiDISH, or a non-constrained approach by EpiDish. I demonstrate how to do both.

#### Minfi

    houseman_estimates <- minfi:::projectCellType(
      plBetas[rownames(plCellCpGsThird), ],
      plCellCpGsThird,
      lessThanOne = FALSE
    )

    head(houseman_estimates)

    ##            Trophoblasts    Stromal      Hofbauer Endothelial       nRBC
    ## GSM1944936    0.1091279 0.04891919  0.000000e+00  0.08983998 0.05294062
    ## GSM1944939    0.2299918 0.00000000 -1.806592e-19  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

#### EpiDISH

    # robust partial correlations
    epidish_RPC <- epidish(
      beta.m = plBetas[rownames(plCellCpGsThird), ],
      ref.m = plCellCpGsThird,
      method = "RPC"
    )

    # CIBERSORT
    epidish_CBS <- epidish(
      beta.m = plBetas[rownames(plCellCpGsThird), ],
      ref.m = plCellCpGsThird,
      method = "CBS"
    )

    ## 1

    ## 2

    ## 3

    # constrained projection (houseman 2012)
    epidish_CP <- epidish(
      beta.m = plBetas[rownames(plCellCpGsThird), ],
      ref.m = plCellCpGsThird,
      method = "CP"
    )

    ## 1

    ## 2

    ## 3

    ## 4

    ## 5

    ## 6

    ## 7

    ## 8

    ## 9

    ## 10

    ## 11

    ## 12

    ## 13

    ## 14

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    ## 16

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    ## 18

    ## 19

    ## 20

    ## 21

    ## 22

    ## 23

    ## 24

#### Compare

Below, I demonstrate how we can visually compare the different cell composition estimates.

    data("plColors")

    # bind estimate data frames and reshape for plotting
    bind_rows(
      houseman_estimates %>% as.data.frame() %>% mutate(algorithm = "CP (Houseman)"),
      epidish_RPC$estF %>% as.data.frame() %>% mutate(algorithm = "RPC"),
      epidish_CBS$estF %>% as.data.frame() %>% mutate(algorithm = "CBS"),
      epidish_CP$estF %>% as.data.frame() %>% mutate(algorithm = "CP (EpiDISH)")
    ) %>%
      mutate(sample = rep(rownames(houseman_estimates), 4)) %>%
      as_tibble() %>%
      pivot_longer(
        cols = -c(algorithm, sample),
        names_to = "component",
        values_to = "estimate"
      ) %>%

      # relevel for plot
      mutate(component = factor(component,
                                levels = c(
                                  "nRBC", "Endothelial", "Hofbauer",
                                  "Stromal", "Trophoblasts",
                                  "Syncytiotrophoblast"
                                )
      )) %>%

      # plot
      ggplot(aes(x = sample, y = estimate, fill = component)) +
      geom_bar(stat = "identity") +
      facet_wrap(~algorithm, ncol = 1) +
      scale_fill_manual(values = plColors) +
      scale_y_continuous(
        limits = c(-0.1, 1.1), breaks = c(0, 0.5, 1),
        labels = scales::percent
      ) +
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
      coord_cartesian(ylim = c(0, 1)) +
      labs(x = "", fill = "")

Some notes:

- Normalize your data with `minfi::preprocessNoob` and BMIQ
- Use all cell CpGs - if some are missing, estimates may vary
- If your samples have been processed in a particular manner, (e.g. sampling from maternal side) expect cell composition to reflect that

## Gestational age

#### Example Data

For demonstration, I use 24 samples from a placental DNAm dataset from GEO, (GSE7519), which contains samples collected in an Australian population. The DNA methylation data (in betas) can be accessed with `data(plBetas)` and corresponding sample information from `data(plPhenoData)`. Note that for demonstration purposes, the cpgs have been filtered to a random ~10,000 CpGs, plus the CpGs used in all of the functions from this package.

    # load example data
    data(plBetas)
    data(plPhenoData)

    dim(plBetas)

    ## [1] 13918    24

    #> [1] 13918    24
    head(plPhenoData)

sample_id | sex | disease | gestation_wk | ga_RPC | ga_CPC | ga_RRPC
--- | --- | --- | --- | --- | --- | ---
GSM1944936 | Male | preeclampsia | 36 | 38.46528 | 38.72867 | 38.65396
GSM1944939 | Male | preeclampsia | 32 | 33.09680 | 34.21252 | 32.62763
GSM1944942 | Female | preeclampsia | 32 | 34.32520 | 35.09565 | 33.32502
GSM1944944 | Male | preeclampsia | 35 | 35.50937 | 36.71512 | 35.51295
GSM1944946 | Female | preeclampsia | 38 | 37.63910 | 37.57931 | 36.61721
GSM1944948 | Female | preeclampsia | 36 | 36.77051 | 38.42632 | 36.72150

    #> # A tibble: 6 x 7
    #>   sample_id  sex   disease   gestation_wk ga_RPC ga_CPC ga_RRPC
    #>   <fct>      <chr> <chr>            <dbl>  <dbl>  <dbl>   <dbl>
    #> 1 GSM1944936 Male  preeclam~           36   38.5   38.7    38.7
    #> 2 GSM1944939 Male  preeclam~           32   33.1   34.2    32.6
    #> 3 GSM1944942 Fema~ preeclam~           32   34.3   35.1    33.3
    #> 4 GSM1944944 Male  preeclam~           35   35.5   36.7    35.5
    #> 5 GSM1944946 Fema~ preeclam~           38   37.6   37.6    36.6
    #> 6 GSM1944948 Fema~ preeclam~           36   36.8   38.4    36.7

There are 3 gestational age clocks for placental DNA methylation data from (Lee 2019):

1. Robust Placental Clock (RPC)
2. Control Placental Clock (CPC)
3. Refined Robust Placental Clock (RRPC)

To predict gestational, we load the example data:

- `plBetas` - DNAm data for 24 placental samples
- `plPhenoData` - Matching sample information

#### Predict Gestational Age

To select the type of clock, we can specify the `type` argument in `predictAge`.

We will apply all three clocks on this data, and add the predicted age to the sample information data.frame, `plPhenoData`.

    plPhenoData <- plPhenoData %>%
      mutate(
        ga_RPC = predictAge(plBetas, type = "RPC"),
        ga_CPC = predictAge(plBetas, type = "CPC"),
        ga_RRPC = predictAge(plBetas, type = "RRPC")
      )

    ## 558 of 558 predictors present.

    ## 546 of 546 predictors present.

    ## 395 of 395 predictors present.

Note that the number of predictors (CpGs) that were used in our data are printed. It’s important to take note if a significant number of predictive CpGs are missing in your data, as this can affect the predicted gestational age accuracy.

Next, I plot the difference between predicted and reported gestational age, for each of the 3 gestational age predictors.

    plPhenoData %>%

      # reshape, to plot
      pivot_longer(
        cols = contains("ga"),
        names_to = "clock_type",
        names_prefix = "ga_",
        values_to = "ga"
      ) %>%

      # plot code
      ggplot(aes(x = gestation_wk, y = ga, col = disease)) +
      geom_point() +
      geom_smooth(method = "lm", se = FALSE) +
      facet_wrap(~clock_type) +
      theme(legend.position = "top") +
      labs(x = "Reported GA (weeks)", y = "Inferred GA (weeks)", col = "")

    ## `geom_smooth()` using formula = 'y ~ x'

## Ethnicity

Before predicting ethnicity You can ensure that you have all features using the `ethnicityCpGs` vector:

    data(ethnicityCpGs)
    all(ethnicityCpGs %in% rownames(plBetas))

    ## [1] TRUE

    results <- predictEthnicity(plBetas)

    ## 1860 of 1860 predictors present.

    results %>%
      tail(8)

Sample_ID | Predicted_ethnicity_nothresh | Predicted_ethnicity | Prob_African | Prob_Asian | Prob_Caucasian | Highest_Prob
--- | --- | --- | --- | --- | --- | ---
GSM1944959 | Asian | Asian | 0.0123073 | 0.9523544 | 0.0353383 | 0.9523544
GSM1944960 | Caucasian | Caucasian | 0.0156961 | 0.1595213 | 0.8247827 | 0.8247827
GSM1944961 | Asian | Asian | 0.0208421 | 0.8954518 | 0.0837061 | 0.8954518
GSM1944962 | Caucasian | Caucasian | 0.0009276 | 0.0008801 | 0.9981923 | 0.9981923
GSM1944963 | Caucasian | Caucasian | 0.0022635 | 0.0028007 | 0.9949358 | 0.9949358
GSM1944964 | Caucasian | Caucasian | 0.0065973 | 0.0112013 | 0.9822014 | 0.9822014
GSM1944965 | Caucasian | Caucasian | 0.0021578 | 0.0024196 | 0.9954226 | 0.9954226
GSM1944966 | Caucasian | Caucasian | 0.0011397 | 0.0017651 | 0.9970952 | 0.9970952

`predictEthnicity` returns probabilities corresponding to each ethnicity for each sample (e.g `Prob_Caucasian`, `Prob_African`, `Prob_Asian`). This applies a glmnet model described in (Yuan 2019). A final classification is determined in two ways:

1. `Predicted_ethnicity_nothresh` - returns a classification corresponding to the highest class-specific probability.

2. `Predicted_ethnicity` - if the highest class-specific probability is below `0.75`, then the the sample is assigned an `Amibiguous` label. This threshold can be adjusted with the `threshold` argument. Samples with this label might require special attention in downstream analyses.

    results %>%
      ggplot(aes(
        x = Prob_Caucasian, y = Prob_African,
        col = Predicted_ethnicity
      )) +
      geom_point(alpha = 0.7) +
      coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
      scale_x_continuous(labels = scales::percent) +
      scale_y_continuous(labels = scales::percent) +
      labs(x = "P(Caucasian)", y = "P(African)")

    results %>%
      ggplot(aes(
        x = Prob_Caucasian, y = Prob_Asian,
        col = Predicted_ethnicity
      )) +
      geom_point(alpha = 0.7) +
      coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
      scale_x_continuous(labels = scales::percent) +
      scale_y_continuous(labels = scales::percent) +
      labs(x = "P(Caucasian)", y = "P(Asian)")

We can’t compare this to self-reported ethnicity as it is unavailable. But we know these samples were collected in Sydney, Australia, and are therefore likely mostly European with some East Asian participants.

    table(results$Predicted_ethnicity)

    ##
    ##     Asian Caucasian
    ##         2        22

A note on adjustment in differential methylation analysis

Because ‘Ambiguous’ samples might have different mixtures of ancestries, it might be inadequate to adjust for them as one group in an analysis of admixed populations (e.g. 50/50 Asian/African should not be considered the same group as 50/50 Caucasian/African). One solution would be to simply remove these samples. Another would be to adjust for the raw probabilities-in this case, use only two of the three probabilities, since the third will be redundant (probabilities sum to 1). If sample numbers are large enough in each group, stratifying downstream analyses by ethnicity might also be a valid option.

## Early-onset preeclampsia (EOPE)

To calculate the probability of EOPE of placental DNAm chorionic villi samples, we rely on 45 predictive CpGs.

In this example, we load the validation data used in Fernández-Boyano 2023] and estimate the EOPE probability of the samples. Note that the function must be run on a matrix with the full set of CpG probes in either the 450K or 850K arrays - the reason for this is that all 45 predictive CpGs must be present for prediction to be completed.

It is recommended that data is normalized using BMIQ prior to prediction.

Note that samples must be rows and CpGs must be columns. The default threshold for classification used to assign labels is 55%; if the users wishes to use other threshold, different labels can be assigned based on the output probabilities.

    library(ExperimentHub)

    ## Loading required package: AnnotationHub

    ## Loading required package: BiocFileCache

    ## Loading required package: dbplyr

    ##
    ## Attaching package: 'dbplyr'

    ## The following objects are masked from 'package:dplyr':
    ##
    ##     ident, sql

    ##
    ## Attaching package: 'AnnotationHub'

    ## The following object is masked from 'package:Biobase':
    ##
    ##     cache

    eh <- ExperimentHub()
    query(eh, "eoPredData")

    ## ExperimentHub with 3 records
    ## # snapshotDate(): 2026-04-23
    ## # $dataprovider: University of British Columbia
    ## # $species: Homo sapiens
    ## # $rdataclass: mixo_splsda, matrix, data.frame
    ## # additional mcols(): taxonomyid, genome, description,
    ## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
    ## #   rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["EH8090"]]'
    ##
    ##            title
    ##   EH8090 | eoPredModel
    ##   EH8403 | valBMIQ
    ##   EH8404 | valMeta

    # download BMIQ normalized 450k data
    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.

Inspect the results:

    head(preds)

Sample_ID | EOPE | Non-PE Preterm | PE_Status
--- | --- | --- | ---
GSM2589533 | 0.6699478 | 0.3300522 | EOPE
GSM2589535 | 0.7680531 | 0.2319469 | EOPE
GSM2589536 | 0.8068395 | 0.1931605 | EOPE
GSM2589538 | 0.7843615 | 0.2156385 | EOPE
GSM2589540 | 0.3858278 | 0.6141722 | Normotensive
GSM2589541 | 0.6488529 | 0.3511471 | EOPE

    # join with metadata
    valMeta <- eh[['EH8404']]

    ## see ?eoPredData and browseVignettes('eoPredData') for documentation

    ## loading from cache

    valMeta <- left_join(valMeta, preds, by="Sample_ID")

    # visualize results
    plot_predictions <- function(df, predicted_class_column) {
      df %>%
        ggplot() +
        geom_boxplot(
          aes(x = Class, y = EOPE),
          width = 0.3,
          alpha = 0.5,
          color = "darkgrey"
        ) +
        geom_jitter(
          aes(x = Class, y = EOPE, color = {{predicted_class_column}}),
          alpha = 0.75,
          position = position_jitter(width = .1)
        ) +
        coord_flip() +
        ylab("Class Probability of EOPE") +
        xlab("True Class") +
        ylim(0,1) +
        scale_color_manual(
          name = "Predicted Class",
          values = c("#E69F00", "skyblue", "#999999")
        ) +
        theme_minimal()
    }

    valMeta %>% plot_predictions(PE_Status)

if user wishes to use different threshold from 55% probability, as an example 70%

    valMeta %>% mutate(
      Pred_Class = case_when(
        EOPE > 0.7 ~ "EOPE",
        `Non-PE Preterm` > 0.7 ~ "Non-PE Preterm",
        .default = 'low-confidence'
      )) %>% plot_predictions(Pred_Class)

## References

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.

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.

Lee Y, Choufani S, Weksberg R, Wilson SL, Yuan V, Burt A, et al. Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels. Aging (Albany NY). 2019 Jun 24;11(12):4238–53.

Fernández-Boyano I, A.M. Inkster, V. Yuan, W.P. Robinson medRxiv 2023 May

## Session Info

    sessionInfo()

    ## R version 4.6.0 RC (2026-04-17 r89917)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
    ##
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    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] eoPredData_1.5.0            ExperimentHub_3.2.0
    ##  [3] AnnotationHub_4.2.0         BiocFileCache_3.2.0
    ##  [5] dbplyr_2.5.2                planet_1.20.0
    ##  [7] tidyr_1.3.2                 ggplot2_4.0.3
    ##  [9] dplyr_1.2.1                 EpiDISH_2.28.0
    ## [11] minfi_1.58.0                bumphunter_1.54.0
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    ##
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    ## [113] labeling_0.4.3            nor1mix_1.3-3
    ## [115] mclust_6.1.2              plyr_1.8.9
    ## [117] stringi_1.8.7             BiocParallel_1.46.0
    ## [119] Matrix_1.7-5              hms_1.1.4
    ## [121] sparseMatrixStats_1.24.0  bit64_4.8.0
    ## [123] Rhdf5lib_2.0.0            KEGGREST_1.52.0
    ## [125] statmod_1.5.1             igraph_2.3.0
    ## [127] memoise_2.0.1             bslib_0.10.0
    ## [129] bit_4.6.0