--- title: "Chronological and gestational DNAm age estimation using different methylation-based clocks" subtitle: "Dolors Pelegri and Juan R Gonzalez" author: | Institute for Global Health (ISGlobal), Barcelona, Spain Bioinformatics Research Group in Epidemiolgy (BRGE) http://brge.isglobal.org date: "`r Sys.Date()`" package: "`r pkg_ver('methylclock')`" output: BiocStyle::html_document: number_sections: true toc: yes fig_caption: yes vignette: > %\VignetteIndexEntry{DNAm age using diffrent methylation clocks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: methylclock.bib --- ```{r setup_knitr, include=FALSE} knitr::opts_chunk$set(message = FALSE, warning = FALSE, cache=FALSE, comment = " ") options(timeout = 300) ``` # Description of implemented clocks This manual describes how to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. The package includes the following estimators: ## Chronological DNAm age (in years) - **Horvath's clock**: It uses 353 CpGs described in @horvath2013dna. It was trained using 27K and 450K arrays in samples from different tissues. Other three different age-related biomarkers are also computed: - **AgeAcDiff** (DNAmAge acceleration difference): Difference between DNAmAge and chronological age. - **IEAA** Residuals obtained after regressing DNAmAge and chronological age adjusted by cell counts. - **EEAA** Residuals obtained after regressing DNAmAge and chronological age. This measure was also known as DNAmAge acceleration residual in the first Horvath's paper. - **Hannum's clock**: It uses 71 CpGs described in @hannum2013genome. It was trained using 450K array in blood samples. Another are-related biomarer is also computed: - **AMAR** (Apparent Methylomic Aging Rate): Measure proposed in @hannum2013genome computed as the ratio between DNAm age and the chronological age. - **BNN**: It uses Horvath's CpGs to train a Bayesian Neural Network (BNN) to predict DNAm age as described in @alfonso2018. - **Horvath's skin+blood clock (skinHorvath)**: Epigenetic clock for skin and blood cells. It uses 391 CpGs described in @horvath2018epigenetic. It was trained using 450K EPIC arrays in skin and blood sampels. - **PedBE clock**: Epigenetic clock from buccal epithelial swabs. It's intended purpose is buccal samples from individuals aged 0-20 years old. It uses 84 CpGs described in @mcewen2019pedbe. The authors gathered 1,721 genome-wide DNAm profiles from 11 different cohorts with individuals aged 0 to 20 years old. - **Wu's clock**: It uses 111 CpGs described in @wu2019dna. It is designed to predict age in children. It was trained using 27K and 450K. - **BLUP clock**: It uses 319607 CpGs described in @zhang2019improved. It was trained using 450K and EPIC arrays in blood (13402 samples) and saliva (259 samples). Age predictors based on training sets with various sample sizes using Best Linear Unbiased Prediction (BLUP) - **EN clock**: It uses 514 CpGs described in @zhang2019improved. It was trained using 450K and EPIC arrays in blood (13402 samples) and saliva (259 samples). Age predictors based on training sets with various sample sizes using Elastic Net (EN) ## Gestational DNAm age (in weeks) - **Knight's clock**: It uses 148 CpGs described in @knight2016epigenetic. It was trained using 27K and 450K arrays in cord blood samples. - **Bohlin's clock**: It uses 96 CpGs described in @bohlin2016prediction. It was trained using 450K array in cord blood samples. - **Mayne's clock**: It uses 62 CpGs described in @mayne2017accelerated. It was trained using 27K and 450K. - **EPIC clock**: EPIC-based predictor of gestational age. It uses 176 CpGs described in @haftorn2021epic. It was trained using EPIC arrays in cord blood samples. - **Lee's clocks**: Three different biological clocks described in @lee2019placental are implemented. It was trained for 450K and EPIC arrays in placenta samples. - **RPC clock**: Robust placental clock (RPC). It uses 558 CpG sites. - **CPC clock**: Control placental clock (CPC). It usses 546 CpG sites. - **Refined RPC clock**: Useful for uncomplicated term pregnancies (e.g. gestational age >36 weeks). It uses 396 CpG sites. The biological DNAm clocks implemented in our package are: - **Levine's clock** (also know as PhenoAge): It uses 513 CpGs described in @levine2018epigenetic. It was trained using 27K, 450K and EPIC arrays in blood samples. - **Telomere Length's clock** (TL): It uses 140 CpGs described in @lu2019dna It was trained using 450K and EPIC arrays in blood samples. The main aim of this package is to facilitate the interconnection with R and Bioconductor's infrastructure and, hence, avoiding submitting data to online calculators. Additionally, `methylclock` also provides an unified way of computing DNAm age to help downstream analyses. # Getting started The package depends on some R packages that can be previously installed into your computer by: ```{r install_req_packages, eval=FALSE} install.packages(c("tidyverse", "impute", "Rcpp")) ``` Then `methylclock` package is installed into your computer by executing: ```{r install_packages, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("methylclock") ``` The package is loaded into R as usual: ```{r load_package} library(methylclockData) library(methylclock) ``` These libraries are required to reproduce this document: ```{r load_others, eval=TRUE} library(Biobase) library(tibble) library(impute) library(ggplot2) library(ggpmisc) library(GEOquery) ``` # DNA Methylation clocks The main function to estimate chronological and biological mDNA age is called `DNAmAge` while the gestational DNAm age is estimated using `DNAmGA` function. Both functions have similar input arguments. Next subsections detail some of the important issues to be consider before computind DNAm clocks. ## Data format The methylation data is given in the argument `x`. They can be either beta or M values. The argument `toBetas` should be set to TRUE when M values are provided. The `x` object can be: - A **matrix** with CpGs in rows and individuals in columns having the name of the CpGs in the rownames. - A **data frame** or a **tibble** with CpGs in rows and individuals in columns having the name of the CpGs in the first column (e.g. cg00000292, cg00002426, cg00003994, ...) as required in the Horvath's DNA Methylation Age Calculator website (https://dnamage.genetics.ucla.edu/home). - A **GenomicRatioSet** object, the default method to encapsulate methylation data in `minfi` Bioconductor package. - An **ExpressionSet** object as obtained, for instance, when downloading methylation data from GEO (https://www.ncbi.nlm.nih.gov/geo/). ## Data nomalization In principle, data can be normalized by using any of the existing standard methods such as QN, ASMN, PBC, SWAN, SQN, BMIQ (see a revision of those methods in @wang2015systematic). `DNAmAge` function includes the BMIQ method proposed by @teschendorff2012beta using Horvath's robust implementation that basically consists of an optimal R code implementation and optimization procedures. This normalization is recommended by Horvath since it improves the predictions for his clock. This normalization procedure is very time-consuming. In order to overcome these difficulties, we have parallelize this process using `BiocParallel` library. This step is not mandatory, so that, you can use your normalized data and set the argument `normalize` equal to FALSE (default). ## Missing individual's data All the implemented methods require complete cases. `DNAmAge` function has an imputation method based on KNN implemented in the function `knn.impute` from `impute` Bioconductor package. This is performed when missing data is present in the CpGs used in any of the computed clocks. There is also another option based on a fast imputation method that imputes missing values by the median of required CpGs as recommended in @bohlin2016prediction. This is recommended when analyzing 450K arrays since `knn.impute` for large datasets may be very time consuming. Fast imputation can be performed by setting `fastImp=TRUE` which is not the default value. ## Missing CpGs of DNAm clocks {#section-missingCpGs} By default the package computes the different clocks when there are more than 80% of the required CpGs of each method. Nothing is required when having missing CpGs since the main functions will return NA for those estimators when this criteria is not meet. Let us use a test dataset (`TestDataset`) which is available within the package to illustrate the type of information we are obtaining: ```{r check} # Get TestDataset data TestDataset <- get_TestDataset() cpgs.missing <- checkClocks(TestDataset) ``` ```{r checkGA} cpgs.missing.GA <- checkClocksGA(TestDataset) ``` The objects `cpgs.missing` and `cpgs.missing.GA` are lists having the missing CpGs of each clock ```{r showMissing} names(cpgs.missing) ``` We can see which are those CpGs for a given clock (for example Hannum) with the function `commonClockCpgs` : ```{r showMissNames} commonClockCpgs(cpgs.missing, "Hannum" ) commonClockCpgs(cpgs.missing.GA, "Bohlin" ) ``` In Section \@ref(section-example) we describe how to change this 80% threshold. ## Cell counts The EEAA method requires to estimate cell counts. We use the package `meffil` (@min2018meffil) that provides some functions to estimate cell counts using predefined datasets. This is performed by setting `cell.count=TRUE` (default value). The reference panel is passed through the argument `cell.count.reference`. So far, the following options are available: - **"blood gse35069 complete"**: methylation profiles from @reinius2012differential for purified blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK, Neu and Eos. - **"blood gse35069"**: methylation profiles from @reinius2012differential for purified blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK and Gran. - **"blood gse35069 chen"**: methylation profiles from @chen2017epigenome blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK, Neu and Eos. - **"andrews and bakulski cord blood"**. Cord blood reference from @bakulski2016dna. It includes Bcell, CD4T, CD8T, Gran, Mono, NK and nRBC. - **"cord blood gse68456"** Cord blood methylation profiles from @de2015nucleated. It includes CD4T, CD8T, Mono, Bcell, NK, Neu, Eos and RBC. - **"gervin and lyle cord blood"** Cord blood reference generated by Kristina Gervin and Robert Lyle, available at `miffil` package. It includes CD14, Bcell, CD4T, CD8T, NK, Gran. - **"saliva gse48472"**: Reference generated from the multi-tissue pannel from @slieker2013identification. It includes Buccal, CD4T, CD8T, Mono, Bcell, NK, Gran. - **"guintivano dlpfc"**: Reference generated from @guintivano2013cell. It includes dorsolateral prefrontal cortex, NeuN_neg and NeuN_pos. - **"combined cord blood"**: References generated based in samples assayed by Bakulski et al, Gervin et al., de Goede et al., and Lin et al. It includes umbilical cord blood, Bcell, CD4T, CD8T, Gran, Mono, NK and nRBC # Chronological and biological DNAm age estimation Next we illustrate how to estimate the chronological DNAm age using several datasets which aim to cover different data input formats. **IMPORTANT NOTE**: On some systems we can find an error in the `DNAmAge()` function when parameter `cell.count = TRUE`. This error is related to `preprocessCore` package and can be fixed by disabling multi-threading when installing the preprocessCore package using the command ``` BiocManager::install("preprocessCore", configure.args = "--disable-threading", force = TRUE) ``` ## Data in Horvath's format (e.g. `csv` with CpGs in rows) {#section-example} Let us start by reproducing the results proposed in @horvath2013dna. It uses the format available in the file 'MethylationDataExample55.csv" from his tutorial (available [here](https://dnamage.genetics.ucla.edu/home)). These data are available at `methylclock` package. Although these data can be loaded into R by using standard functions such as `read.csv` we hihgly recommend to use functions from `tidiverse`, in particular `read_csv` from `readr` package. The main reason is that currently researchers are analyzing Illumina 450K or EPIC arrays that contains a huge number of CpGs that can take a long time to be loaded when using basic importing R function. These functions import `csv` data as tibble which is one of the possible formats of `DNAmAge` function ```{r load_horvath_example} library(tidyverse) MethylationData <- get_MethylationDataExample() MethylationData ``` *IMPORTANT NOTE*: Be sure that the first column contains the CpG names. Sometimes, your imported data look like this one (it can happen, for instance, if the `csv` file was created in R without indicating `row.names=FALSE`) ``` > mydata # A tibble: 473,999 x 6 X1 Row.names BIB_15586_1X BIB_33043_1X EDP_5245_1X KAN_584_1X 1 1 cg000000~ 0.635 0.575 0.614 0.631 2 2 cg000001~ 0.954 0.948 0.933 0.950 3 3 cg000001~ 0.889 0.899 0.901 0.892 4 4 cg000001~ 0.115 0.124 0.107 0.123 5 5 cg000002~ 0.850 0.753 0.806 0.815 6 6 cg000002~ 0.676 0.771 0.729 0.665 7 7 cg000002~ 0.871 0.850 0.852 0.863 8 8 cg000003~ 0.238 0.174 0.316 0.206 ``` If so, the first column must be removed before being used as the input object in `DNAmAge` funcion. It can be done using `dplyr` function ``` > mydata2 <- select(mydata, -1) # A tibble: 473,999 x 5 Row.names BIB_15586_1X BIB_33043_1X EDP_5245_1X KAN_584_1X 1 cg000000~ 0.635 0.575 0.614 0.631 2 cg000001~ 0.954 0.948 0.933 0.950 3 cg000001~ 0.889 0.899 0.901 0.892 4 cg000001~ 0.115 0.124 0.107 0.123 5 cg000002~ 0.850 0.753 0.806 0.815 6 cg000002~ 0.676 0.771 0.729 0.665 7 cg000002~ 0.871 0.850 0.852 0.863 8 cg000003~ 0.238 0.174 0.316 0.206 ``` In any case, if you use the object `mydata` that contains the CpGs in the second column, you will see this error message: ``` > DNAmAge(mydata) Error in DNAmAge(mydata) : First column should contain CpG names ``` Once data is in the proper format, DNAmAge can be estimated by simply: ```{r DNAmAge_horvath, warning=TRUE} age.example55 <- DNAmAge(MethylationData) age.example55 ``` As mention in Section \@ref(section-missingCpGs) some clocks returns NA when there are more than 80% of the required CpGs are missing as we can see when typing ```{r show_cpg_miss} missCpGs <- checkClocks(MethylationData) ``` Here we can observe that 72.1% of the required CpGs for SkinHorvath clock are missing. We could estimate DNAm age using this clock just changing the argument `min.perc` in `DNAmAge`. For example, we can indicate that the minimum amount of required CpGs for computing a given clock should be 25%. ```{r DNAmAgemp_horvath, warning=TRUE} age.example55.2 <- DNAmAge(MethylationData, min.perc = 0.25) age.example55.2 ``` In that case, we see as SkinHorvath clock is estimated (though it can be observed that the estimation is not very accurate - this is why we considered at least having 80% of the required CpGs). By default all available clocks (Hovarth, Hannum, Levine, BNN, skinHorvath,...) are estimated. One may select a set of clocks by using the argument `clocks` as follows: ```{r DNAmAge_horvath_sel, warning=TRUE} age.example55.sel <- DNAmAge(MethylationData, clocks=c("Horvath", "BNN")) age.example55.sel ``` ## Age acceleration However, in epidemiological studies one is interested in assessing whether age acceleration is associated with a given trait or condition. Three different measures can be computed: - **ageAcc**: Difference between DNAmAge and chronological age. - **ageAcc2**: Residuals obtained after regressing chronological age and DNAmAge (similar to IEAA). - **ageAcc3**: Residuals obtained after regressing chronological age and DNAmAge adjusted for cell counts (similar to EEAA). All this estimates can be obtained for each clock when providing chronological age through `age` argument. This information is normally provided in a different file including different covariates (metadata or sample annotation data). In this example data are available at 'SampleAnnotationExample55.csv' file that is also available at `methylclock` package: ```{r covariates_horvath_example} library(tidyverse) path <- system.file("extdata", package = "methylclock") covariates <- read_csv(file.path(path, "SampleAnnotationExample55.csv")) covariates ``` In this case, chronological age is available at `Age` column: ```{r age_horvath_example} age <- covariates$Age head(age) ``` The different methylation clocks along with their age accelerated estimates can be simply computed by: ```{r DNAmAge_horvath_cell, warning=TRUE} age.example55 <- DNAmAge(MethylationData, age=age, cell.count=TRUE) age.example55 ``` By default, the argument `cell.count` is set equal to TRUE and, hence, can be omitted. This implies that `ageAcc3` will be computed for all clocks. In some occassions this can be very time consuming. In such cases one can simply estimate DNAmAge, accAge and accAge2 by setting `cell.count=FALSE`. NOTE: see section 3.5 to see the reference panels available to estimate cell counts. Then, we can investigate, for instance, whether the accelerated age is associated with Autism. In that example we will use a non-parametric test (NOTE: use t-test or linear regression for large sample sizes) ```{r compare_autistic} autism <- covariates$diseaseStatus kruskal.test(age.example55$ageAcc.Horvath ~ autism) kruskal.test(age.example55$ageAcc2.Horvath ~ autism) kruskal.test(age.example55$ageAcc3.Horvath ~ autism) ``` ## Chronological age prediction using `ExpressionSet` data One may be interested in assessing association between chronologial age and DNA methylation age or evaluating how well chronological age is predicted by DNAmAge. In order to illustrate this analysis we downloaded data from GEO corresponding to a set of healthy individuals (GEO accession number GSE58045). Data can be retrieved into R by using `GEOquery` package as an `ExpressionSet` object that can be the input of our main function. ```{r get_gse58045, echo=FALSE} # ff <- "c:/juan/CREAL/BayesianPrediction/Bayesian_clock/paper" # load(file.path(ff, "data/GSE58045.Rdata")) ``` ```{r get_geo_gse58045, eval=TRUE} # To avoid connection buffer size Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 * 10) # Download data dd <- GEOquery::getGEO("GSE58045") gse58045 <- dd[[1]] # Restore connection buffer size Sys.setenv("VROOM_CONNECTION_SIZE" = 131072) ``` ```{r show_gse58045} gse58045 ``` The chronological age is obtained by using `pData` function from `Biobase` package that is able to deal with `ExpressionSet` objects: ```{r age_gse58045} pheno <- pData(gse58045) age <- as.numeric(pheno$`age:ch1`) ``` And the different DNA methylation age estimates are obtained by using `DNAmAge` function (NOTE: as there are missing values, the program automatically runs `impute.knn` function to get complete cases): ```{r DNAmAge_gse58045, warning=TRUE} age.gse58045 <- DNAmAge(gse58045, age=age) age.gse58045 ``` Figure \ref{fig:horvath_age} shows the correlation between DNAmAge obtained from Horvath's method and the chronological age, while Figure \ref{fig:bnn_age} depicts the correlation of a new method based on fitting a Bayesian Neural Network to predict DNAmAge based on Horvath's CpGs. ```{r horvat_age} plotDNAmAge(age.gse58045$Horvath, age) ``` ```{r bnn_age} plotDNAmAge(age.gse58045$BNN, age, tit="Bayesian Neural Network") ``` ## Use of DNAmAge in association studies Let us illustrate how to use DNAmAge information in association studies (e.g case/control, smokers/non-smokers, responders/non-responders, ...). GEO number GSE19711 contains transcriptomic and epigenomic data of a study in lung cancer. Data can be retrieved into R by ```{r get_gse19711, echo=FALSE} # ff <- "c:/juan/CREAL/BayesianPrediction/Bayesian_clock/paper" # load(file.path(ff, "data/GSE19711.Rdata")) ``` ```{r get_geo_gse19711, eval=TRUE} # To avoid connection buffer size Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 * 10) # Download data dd <- GEOquery::getGEO("GSE19711") gse19711 <- dd[[1]] # Restore connection buffer size Sys.setenv("VROOM_CONNECTION_SIZE" = 131072) ``` The object `gse19711`is an `ExpressionSet` that can contains CpGs and phenotypic (e.g clinical) information ```{r show_gse19711} gse19711 ``` Let us imagine we are interested in comparing the accelerated age between cases and controls. Age and case/control status information can be obtained by: ```{r get_case_control} pheno <- pData(gse19711) age <- as.numeric(pheno$`ageatrecruitment:ch1`) disease <- pheno$`sample type:ch1` table(disease) disease[grep("Control", disease)] <- "Control" disease[grep("Case", disease)] <- "Case" disease <- factor(disease, levels=c("Control", "Case")) table(disease) ``` The DNAmAge estimates of different methods is computed by ```{r DNAmAge_gse19711, warning=TRUE} age.gse19711 <- DNAmAge(gse19711, age=age) ``` We can observe there are missing data. The funcion automatically impute those using `impute.knn` function from `impute` package since complete cases are required to compute the different methylation clocks. The estimates are: ```{r show_age.gse19711} age.gse19711 ``` The association between disease status and DNAmAge estimated using Horvath's method can be computed by ```{r assoc_hpv} mod.horvath1 <- glm(disease ~ ageAcc.Horvath , data=age.gse19711, family="binomial") summary(mod.horvath1) mod.skinHorvath <- glm(disease ~ ageAcc2.Horvath , data=age.gse19711, family="binomial") summary(mod.skinHorvath) mod.horvath3 <- glm(disease ~ ageAcc3.Horvath , data=age.gse19711, family="binomial") summary(mod.horvath3) ``` We do not observe statistical significant association between age acceleration estimated using Horvath method and the risk of developing lung cancer. It is worth to notice that Horvath's clock was created to predict chronological age and the impact of age acceleration of this clock on disease may be limited. On the other hand, Levine's clock aimed to distinguish risk between same-aged individuals. Let us evaluate whether this age acceleration usin Levine's clock is associated with lung cancer ```{r mod_levine} mod.levine1 <- glm(disease ~ ageAcc.Levine , data=age.gse19711, family="binomial") summary(mod.levine1) mod.levine2 <- glm(disease ~ ageAcc2.Levine , data=age.gse19711, family="binomial") summary(mod.levine2) mod.levine3 <- glm(disease ~ ageAcc3.Levine , data=age.gse19711, family="binomial") summary(mod.levine3) ``` Here we observe as the risk of developing lung cancer increases `r round((exp(coef(mod.levine1)[2]) - 1)*100,2)` percent per each unit in the age accelerated variable (`ageAcc`). Similar conclusion is obtained when using `ageAcc2` and `ageAcc3` variables. In some occasions cell composition should be used to assess association. This information is calculated in `DNAmAge` function and it can be incorporated in the model by: ```{r assoc_cell} cell <- attr(age.gse19711, "cell_proportion") mod.cell <- glm(disease ~ ageAcc.Levine + cell, data=age.gse19711, family="binomial") summary(mod.cell) ``` Here we observe as the positive association disapears after adjusting for cell counts. ## Use of DNAm age in children ```{r get_gse109446, echo=FALSE} # ff <- "c:/juan/CREAL/BayesianPrediction/Bayesian_clock/paper" # load(file.path(ff, "data/GSE109446.Rdata")) ``` ```{r get_geo_109446, eval=TRUE} dd <- GEOquery::getGEO("GSE109446") gse109446 <- dd[[1]] ``` ```{r age_gse109446, warning=TRUE} controls <- pData(gse109446)$`diagnosis:ch1`=="control" gse <- gse109446[,controls] age <- as.numeric(pData(gse)$`age:ch1`) age.gse <- DNAmAge(gse, age=age) ``` ```{r plotClocks} plotCorClocks(age.gse) ``` # Gestational DNAm age estimation ## Model predicion Let us start by reproducing the example provided in @knight2016epigenetic as a test data set (file 'TestDataset.csv'). It consists on 3 individuals whose methylation data are available as supplementary data of their paper. The data is also available at `methylclock` package as a data frame. ```{r load_3_inds} TestDataset[1:5,] ``` The Gestational Age (in months) is simply computed by ```{r age_test, warning=TRUE} ga.test <- DNAmGA(TestDataset) ga.test ``` like in DNAmAge we can use the parameter `min.perc` to set the minimum missing percentage. The results are the same as those described in the additional file 7 of @knight2016epigenetic (link [here] (https://static-content.springer.com/esm/art%3A10.1186%2Fs13059-016-1068-z/MediaObjects/13059_2016_1068_MOESM7_ESM.docx)) Let us continue by illustrating how to compute GA of real examples. The PROGRESS cohort data is available in the additional file 8 of @knight2016epigenetic. It is available at `methylclock` as a `tibble`: ```{r get_progress} data(progress_data) ``` This file also contains different variables that are available in this `tibble`. ```{r progressClin} data(progress_vars) ``` The Clinical Variables including clinical assesment of gestational age (EGA) are available at this `tibble`. The Gestational Age (in months) is simply computed by ```{r age_progress, warning=TRUE} ga.progress <- DNAmGA(progress_data) ga.progress ``` We can compare these results with the clinical GA available in the variable EGA ```{r plot_progress} plotDNAmAge(ga.progress$Knight, progress_vars$EGA, tit="GA Knight's method", clock="GA") ``` Figure 3b (only for PROGRESS dataset) in @knight2016epigenetic representing the correlation between GA acceleration and birthweight can be reproduced by ```{r plotAcc} library(ggplot2) progress_vars$acc <- ga.progress$Knight - progress_vars$EGA p <- ggplot(data=progress_vars, aes(x = acc, y = birthweight)) + geom_point() + geom_smooth(method = "lm", se=FALSE, color="black") + xlab("GA acceleration") + ylab("Birthweight (kgs.)") p ``` Finally, we can also estimate the "accelerated gestational age" using two of the the three different estimates previously described (`accAge`, `accAge2`) by provinding information of gestational age through `age` argument. Notice that in that case `accAge3` cannot be estimates since we do not have all the CpGs required by the default reference panel to estimate cell counts for gestational age which is "andrews and bakulski cord blood". ```{r acccelerated_ga, warning=TRUE} accga.progress <- DNAmGA(progress_data, age = progress_vars$EGA, cell.count=FALSE) accga.progress ``` One can also check which clocks can be estimated given the CpGs available in the methylation data by ```{r check_clocks_2} checkClocksGA(progress_data) ``` # Correlation among DNAm clocks We can compute the correlation among biological clocks using the function `plotCorClocks` that requires the package `ggplot2` and `ggpubr` to be installed in your computer. We can obtain, for instance, the correlation among the clocks estimated for the healthy individuals study previosuly analyze (GEO accession number GSE58045) by simply executing: ```{r plotCorClockHealth} plotCorClocks(age.gse58045) ``` # References ```{sessioninfo} utils::sessionInfo() ```