--- title: "planet" output: rmarkdown::html_vignette: df_print: "kable" fig_caption: FALSE toc: TRUE pkgdown: as_is: true vignette: > %\VignetteIndexEntry{planet} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console --- ## Installation ```{r, message = FALSE, warning = FALSE, eval = FALSE} 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)](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07186-6). 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. ```{r, message = FALSE, warning = FALSE} # 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) ``` 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 ```{r} houseman_estimates <- minfi:::projectCellType( plBetas[rownames(plCellCpGsThird), ], plCellCpGsThird, lessThanOne = FALSE ) head(houseman_estimates) ``` #### EpiDISH ```{r} # 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" ) # constrained projection (houseman 2012) epidish_CP <- epidish( beta.m = plBetas[rownames(plCellCpGsThird), ], ref.m = plCellCpGsThird, method = "CP" ) ``` #### Compare Below, I demonstrate how we can visually compare the different cell composition estimates. ```{r, fig.width = 7, fig.height = 7} 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](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75196)), 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. ```{r} # load example data data(plBetas) data(plPhenoData) dim(plBetas) #> [1] 13918 24 head(plPhenoData) #> # A tibble: 6 x 7 #> sample_id sex disease gestation_wk ga_RPC ga_CPC ga_RRPC #> #> 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)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628997/): 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`. ```{r} plPhenoData <- plPhenoData %>% mutate( ga_RPC = predictAge(plBetas, type = "RPC"), ga_CPC = predictAge(plBetas, type = "CPC"), ga_RRPC = predictAge(plBetas, type = "RRPC") ) ``` 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. ```{r, fig.width = 7, fig.height = 5} 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 = "") ``` ## Ethnicity Before predicting ethnicity You can ensure that you have all features using the `ethnicityCpGs` vector: ```{r} data(ethnicityCpGs) all(ethnicityCpGs %in% rownames(plBetas)) results <- predictEthnicity(plBetas) results %>% tail(8) ``` `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)](https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-019-0296-3). 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. ```{r, fig.width = 7} 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. ```{r} table(results$Predicted_ethnicity) ``` **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. ](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07186-6) [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.](https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-019-0296-3) [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.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628997/) ## Session Info ```{r} sessionInfo() ```