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"
)

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

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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):

Robust Placental Clock (RPC)
Control Placental Clock (CPC)
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:

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

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.

Session Info

sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] planet_1.2.0                tidyr_1.1.4                
##  [3] ggplot2_3.3.5               dplyr_1.0.7                
##  [5] EpiDISH_2.10.0              minfi_1.40.0               
##  [7] bumphunter_1.36.0           locfit_1.5-9.4             
##  [9] iterators_1.0.13            foreach_1.5.1              
## [11] Biostrings_2.62.0           XVector_0.34.0             
## [13] SummarizedExperiment_1.24.0 Biobase_2.54.0             
## [15] MatrixGenerics_1.6.0        matrixStats_0.61.0         
## [17] GenomicRanges_1.46.0        GenomeInfoDb_1.30.0        
## [19] IRanges_2.28.0              S4Vectors_0.32.0           
## [21] BiocGenerics_0.40.0        
## 
## loaded via a namespace (and not attached):
##   [1] BiocFileCache_2.2.0       plyr_1.8.6               
##   [3] splines_4.1.1             BiocParallel_1.28.0      
##   [5] digest_0.6.28             htmltools_0.5.2          
##   [7] fansi_0.5.0               magrittr_2.0.1           
##   [9] memoise_2.0.0             tzdb_0.1.2               
##  [11] limma_3.50.0              readr_2.0.2              
##  [13] annotate_1.72.0           askpass_1.1              
##  [15] siggenes_1.68.0           prettyunits_1.1.1        
##  [17] colorspace_2.0-2          blob_1.2.2               
##  [19] rappdirs_0.3.3            xfun_0.27                
##  [21] crayon_1.4.1              RCurl_1.98-1.5           
##  [23] jsonlite_1.7.2            genefilter_1.76.0        
##  [25] GEOquery_2.62.0           survival_3.2-13          
##  [27] glue_1.4.2                gtable_0.3.0             
##  [29] zlibbioc_1.40.0           DelayedArray_0.20.0      
##  [31] Rhdf5lib_1.16.0           HDF5Array_1.22.0         
##  [33] scales_1.1.1              DBI_1.1.1                
##  [35] rngtools_1.5.2            Rcpp_1.0.7               
##  [37] xtable_1.8-4              progress_1.2.2           
##  [39] bit_4.0.4                 proxy_0.4-26             
##  [41] mclust_5.4.7              preprocessCore_1.56.0    
##  [43] httr_1.4.2                RColorBrewer_1.1-2       
##  [45] ellipsis_0.3.2            farver_2.1.0             
##  [47] pkgconfig_2.0.3           reshape_0.8.8            
##  [49] XML_3.99-0.8              sass_0.4.0               
##  [51] dbplyr_2.1.1              utf8_1.2.2               
##  [53] labeling_0.4.2            tidyselect_1.1.1         
##  [55] rlang_0.4.12              AnnotationDbi_1.56.0     
##  [57] munsell_0.5.0             tools_4.1.1              
##  [59] cachem_1.0.6              generics_0.1.1           
##  [61] RSQLite_2.2.8             evaluate_0.14            
##  [63] stringr_1.4.0             fastmap_1.1.0            
##  [65] yaml_2.2.1                knitr_1.36               
##  [67] bit64_4.0.5               beanplot_1.2             
##  [69] scrime_1.3.5              purrr_0.3.4              
##  [71] KEGGREST_1.34.0           nlme_3.1-153             
##  [73] doRNG_1.8.2               sparseMatrixStats_1.6.0  
##  [75] nor1mix_1.3-0             xml2_1.3.2               
##  [77] biomaRt_2.50.0            compiler_4.1.1           
##  [79] filelock_1.0.2            curl_4.3.2               
##  [81] png_0.1-7                 e1071_1.7-9              
##  [83] tibble_3.1.5              bslib_0.3.1              
##  [85] stringi_1.7.5             highr_0.9                
##  [87] GenomicFeatures_1.46.0    lattice_0.20-45          
##  [89] Matrix_1.3-4              multtest_2.50.0          
##  [91] vctrs_0.3.8               pillar_1.6.4             
##  [93] lifecycle_1.0.1           rhdf5filters_1.6.0       
##  [95] jquerylib_0.1.4           data.table_1.14.2        
##  [97] bitops_1.0-7              rtracklayer_1.54.0       
##  [99] R6_2.5.1                  BiocIO_1.4.0             
## [101] codetools_0.2-18          MASS_7.3-54              
## [103] assertthat_0.2.1          rhdf5_2.38.0             
## [105] openssl_1.4.5             rjson_0.2.20             
## [107] withr_2.4.2               GenomicAlignments_1.30.0 
## [109] Rsamtools_2.10.0          GenomeInfoDbData_1.2.7   
## [111] locfdr_1.1-8              mgcv_1.8-38              
## [113] hms_1.1.1                 quadprog_1.5-8           
## [115] grid_4.1.1                base64_2.0               
## [117] class_7.3-19              rmarkdown_2.11           
## [119] DelayedMatrixStats_1.16.0 illuminaio_0.36.0        
## [121] restfulr_0.0.13