isglobal-brge / methylclock Goto Github PK

Congratulations on having created a much-needed R package. I'd considered doing creating a clock package several times for studies that could not be uploaded to Steve Horvath's clock server. Although I've seen a few clocks implemented individually in a various R packages, your package is by far the most complete.

I and colleagues In Leiden have identified a few additional clocks we'd like to calculate:
DunedinPoAm38
epiTOC
Brenner https://doi.org/10.1038/ncomms14617
MiAge https://pubmed.ncbi.nlm.nih.gov/29160179/
Zhang https://github.com/qzhang314/DNAm-based-age-predictor

We would be happy to create a fork of your package and add these clocks ourselves. If you were happy with the result, you could merge our changes with your repository and make whatever changes you thought necessary.

Hi,

I was wondering if it's possible to add a new clock to this package (by you or by myself):
https://github.com/qzhang314/DNAm-based-age-predictor
Corresponding paper:
https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-019-0667-1

I'm specifically interested in the Elastic Net clock. Already had a look and did some tests to see if it would fit in your structure, and I believe it does. It's also ok to implement it myself and then create a pull request if that's fine with you. Maybe you guys can have a look at it and say what you think?

I'm also checking with the author to see if it's ok to have the clock implemented.

Best, Tom

Dear Professors of ISGlobal,

We greatly appreciate your creation of "methyclock" R package, which enables the calculation of individual DNAm PhenoAge using multiple DNAm Age clock methods simultaneously. This has solved the challenging problem I and colleagues were facing.

However, when calculating DNAm PhenoAge using Levine's clock _513 CpGs, some individuals' DNAm PhenoAge values turned out to be negative (i.e., DNAm PhenoAge < 0). It is worth noting that this issue also occurred when using your provided TestDataset on the website (https://www.bioconductor.org/packages/devel/bioc/vignettes/methylclock/inst/doc/methylclock.html#chronological-age-prediction-using-expressionset-data) (as shown in the attached figure).

This problem only appears when using Levine's clock, and I would like to inquire about how to solve it and whether these negative values would have any impact on subsequent analysis.

Thank you very much for your time and consideration. Your response would be greatly appreciated as this is crucial for our work.
We looking forward to hearing from you at your earliest time.

Best regards,
Carrie Chen

Hello,

When I add a vector of numeric value to the age argument, the DNAmAge function fail with the error message next:

Error in methylclock::DNAmAge(myBvals, age = AgeAtV1) : 
  cell counts cannot be estimated since
                meffilEstimateCellCountsFromBetas function is giving an error.
                Probably your data do not have any of the required CpGs for that for that
                reference panel.

What is weird is that if I remove the age argument, the function work even when I don't specify a specific clock.

Any idea of what can cause that? I'm using the latest version (on bioconductor) with R 4.3.3.

Hi,

If I understand correctly, the intrinsic changes in the methylome do not take into account the cell types, and it is what you've written in the function ageAcc2

Best regards, Lijiao

Run :
age.example1008 <- DNAmAge(MethylationData)

"MethylationData" is a data with 853103ods. od 1009 variables.

Error :
Error in preprocessCore::normalize.quantiles.use.target(X, target = coefsgs$MeansGS) :
ERROR; return code from pthread_create() is 22

I have an error like this. And I have no idea why this error occurs. PLZ help me!!!!!!!

The methylclock paper and the bioc package page both point to this repo, but the version of the package on Bioc is completely different to the version hosted here.

Is there somewhere I can browse and possibly contribute to the current Bioc version?

Hi there, saludos desde California!

Thank you so much for publishing this work, it's truly excellent.

I was excited to see your recent addition of the Telomere Length clock. May I ask, what unit it is reporting?

When I run it clock on methylation data from an Illumina EPIC, I'm getting a number between 155-167 which seems orders of magnitude away from the measurements quoted in the research paper (between 6 and 8).

June.

Hello,

First of all thank you very much for developing this package.

I was wondering if it's possible to add these two clocks to the package.

• GrimAge clock: https://www.aging-us.com/article/101684/text.
• DeepMAge: http://www.aginganddisease.org/EN/10.14336/AD.2020.1202.

Thank you!

I appears that there is an error in calculating the Wu clock. The Wu clock was trained on age transformed as Horvath did but using toddler age = 48 months (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6914436/) instead of adult age = 20 years. However, the reverse transformation function called by DNAmAge() for the Wu clock identical to that described by Horvath:

anti.trafo <- function(x, adult.age = 20) { ifelse(x < 0, (1 + adult.age) * exp(x) - 1, (1 + adult.age) * x + adult.age) }
This results in substantial overestimation of age. For the Wu clock, I believe that adult.age = 48 should be used.

I tried to run the function DNAmAge to estimate methylation Age. This used to work well with beta matrix - an object with variables (CpGs) in the rows and the observations (patients) in the collums. I named this object "myNorm". But now this returns a error message: Run: Age <-DNAmAge(myNorm, clocks="Hannum") Return: "Error in data.frame(id =rownames(cpgs, imp), Hannum = hannum): arguments imply differng number of rows: 0,40. I though this error ocours on a internal object made by the funcition, so i searched these code in github but don't have sucess. Someone can help me?

https://drive.google.com/file/d/1_YL_T7F-ttxlEmP0yMZYVSWqIHxFIceE/view?usp=sharing
example dataframe

First of all, thanks for creating this useful package!

I'd like to flag an issue with the BNN clock. I've been unable to get any data from it. The other clocks run fine, however.

Whenever I run:
my_data <- DNAmAge(my_betas)

I get the warning message:
In DNAmAge(my_betas) : Bayesian method cannot be estimated

This only happens when I try to run my own dataset. When I use the dataset included in the tutorial, the BNN clock runs fine. Do you know what could be causing the problem?

I've also noticed that the BLUP and EN clocks mentioned in the vignette don't seem to be included in the R package?

Hello,

First of all thank you for developing such a very helpful and useful package!

I am trying to run the this command using my data to estimate the age acceleration:

age.example55 <- DNAmAge(MethylationData, age=age,
cell.count=TRUE)

My command:

age_estim = DNAmAge(beta_lung, age = age, cell.count = TRUE)

where:

• beta_lung = the beta values.
• age = chronological age of my patients

The error I am getting:

Error in model.frame.default(formula = y ~ age, data = df, na.action = "na.exclude", :
invalid type (list) for variable 'age'

Could you please help me.

Thank you in advance.

I am new to Methylclock and I have a Bisulfite sequencing dataset with Methylation calls.

I am interested in performing the methylclock analysis and checking it looks that it used the models derived from 450K Methyl chip dataset using M-values and beta values.

I want to know how can I convert the Bisulfite sequencing dataset which consists of the coverage, num Cs and num Ts dataset to M-values or beta values.

Thank you.

The predAge() function uses coefs$CpGmarker to index the methylation matrix. The indexing isn't working as expected because for some clocks coefs$CpGmarker is a factor, not a character.

Hi,

I'm using methylclock to calculate various clocks, including Hannum. Besides methylclock I'm also using GrimAge which also returns a Hannum age. What caught my eye is that the Hannum ages are very different (~5 year difference). I then checked another R tool (cgageR) & the web tool from Horvath to verify both Hannum results, and these returned the exact same ages as GrimAge does. So now I'm very curious to know why your calculation of Hannum is so different.

A reproducible example:
I did a test using the "dat0BloodIllumina450K" dataset from Horvaths website (https://horvath.genetics.ucla.edu/html/dnamage/). I used this set in methylclock & their own web tool (http://dnamage.genetics.ucla.edu/home) just as reference (or cgageR for that matter). Below are the output from each analysis:

You can see that Horvath, Levine & Horvath2/skin are all the same, but Hannum is clearly not. Any ideas??

PS. I'm using methylclock v0.7.5 (the latest master)

Best, Tom

Hello,

I am using DNAmAge to calculate IEAA and EEAA using beta values calculated by Sesame, where it produces a matrix of CpGs in rows (with CpG names as rownames) and columns representing samples (and colnames = sample names).

I am able to produce age estimates for ageAcc and ageAcc2 for Horvath, Hannum, and Levine, but I ageAcc3.Horvath and ageAcc3.Hannum are reporting 0's for their estimates. Furthermore, I am receiving a warning which states

Warning message:
In DNAmAge(betas_unmasked, age = age, normalize = TRUE, cell.count = TRUE,  :
  Bayesian method cannot be estimated

Is the 0 value that is reported an error due to the Bayesian method not being estimate? Do you have any suggestions as to the cause for this warning, and a possible solution to fix this? The rest of the code is attached to the bottom including session info.

Thanks,
Varun

> library(sesame)
> library(sesameData)
> betas_unmasked = openSesame(patientID,BPPARAM=BiocParallel::SnowParam(10), mask = FALSE)
> head(betas_unmasked)
> head(betas_unmasked)
                                R01C01                 R02C01               R03C01               R04C01               R05C01               R06C01
cg00000029           0.5739221           0.6108738           0.5837727           0.7546659           0.6303706           0.7441521
cg00000103           0.9381232           0.9461803           0.9391074           0.9445436           0.9424710           0.9465154
cg00000109           0.9377471           0.9262207           0.9374167           0.9437616           0.9415424           0.9497956
cg00000155           0.9539705           0.9583607           0.9562199           0.9640863           0.9614547           0.9582190
cg00000158           0.9610545           0.9678355           0.9641447           0.9642052           0.9624602           0.9632444
cg00000165           0.1964285           0.1734710           0.2841674           0.2603667           0.1592896           0.1933614
                                 R07C01                R08C01
cg00000029           0.7096819           0.7721658
cg00000103           0.9463656           0.9557872
cg00000109           0.9457374           0.9434120
cg00000155           0.9620859           0.9641823
cg00000158           0.9622377           0.9639014
cg00000165           0.1770486           0.1801381

> age.example1 <-DNAmAge(betas_unmasked, age=age,normalize = TRUE, 
+                        cell.count = TRUE)
Warning message:
In DNAmAge(betas_unmasked, age = age, normalize = TRUE, cell.count = TRUE) :
  Bayesian method cannot be estimated

> age.example1
# A tibble: 8 x 31
  id          Horvath ageAcc.Horvath ageAcc2.Horvath ageAcc3.Horvath Hannum ageAcc.Hannum ageAcc2.Hannum ageAcc3.Hannum Levine ageAcc.Levine
  <chr>         <dbl>          <dbl>           <dbl>           <dbl>  <dbl>         <dbl>          <dbl>          <dbl>  <dbl>         <dbl>
1 2043169701~    56.6          1.57           1.23                 0   46.2        -8.85          -2.22               0   34.7         -20.3
2 2043169701~    66.4         -2.57           0.0300               0   58.9       -10.1            1.92               0   49.3         -19.7
3 2043169701~    55.0          2.95           1.98                 0   51.1        -0.942          4.53               0   36.9         -15.1
4 2043169701~    60.5         -1.54          -0.408                0   49.7       -12.3           -3.00               0   41.0         -21.0
5 2043169701~    49.5          3.48           1.24                 0   46.4         0.425          3.59               0   27.9         -18.1
6 2043169701~    51.5         -0.536         -1.51                 0   45.8        -6.16          -0.689              0   33.0         -19.0
7 2043169701~    43.4          5.43           1.50                 0   38.4         0.402          0.498              0   24.4         -13.6
8 2043169701~    43.4         -1.60          -4.05                 0   37.6        -7.43          -4.64               0   17.6         -27.4
# ... with 20 more variables: ageAcc2.Levine <dbl>, ageAcc3.Levine <dbl>, BNN <lgl>, skinHorvath <dbl>, ageAcc.skinHorvath <dbl>,
#   ageAcc2.skinHorvath <dbl>, ageAcc3.skinHorvath <dbl>, PedBE <dbl>, ageAcc.PedBE <dbl>, ageAcc2.PedBE <dbl>, ageAcc3.PedBE <dbl>,
#   Wu <dbl>, ageAcc.Wu <dbl>, ageAcc2.Wu <dbl>, ageAcc3.Wu <dbl>, TL <dbl>, ageAcc.TL <dbl>, ageAcc2.TL <dbl>, ageAcc3.TL <dbl>, age <int>

Session Info is:

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] meffil_1.1.2                                       SmartSVA_0.1.3                                    
 [3] RSpectra_0.16-0                                    isva_1.9                                          
 [5] JADE_2.0-3                                         qvalue_2.24.0                                     
 [7] gdsfmt_1.28.0                                      statmod_1.4.36                                    
 [9] DNAcopy_1.66.0                                     fastICA_1.2-2                                     
[11] lme4_1.1-27.1                                      Matrix_1.3-4                                      
[13] multcomp_1.4-17                                    TH.data_1.0-10                                    
[15] survival_3.2-11                                    mvtnorm_1.1-2                                     
[17] markdown_1.1                                       gridExtra_2.3                                     
[19] Cairo_1.5-12.2                                     knitr_1.33                                        
[21] plyr_1.8.6                                         sva_3.40.0                                        
[23] BiocParallel_1.26.1                                genefilter_1.74.0                                 
[25] mgcv_1.8-35                                        nlme_3.1-152                                      
[27] MASS_7.3-54                                        impute_1.66.0                                     
[29] methylclock_0.7.5                                  quadprog_1.5-8                                    
[31] devtools_2.4.2                                     usethis_2.0.1                                     
[33] preprocessCore_1.54.0                              DunedinPoAm45_0.1.0                               
[35] IlluminaHumanMethylationEPICmanifest_0.3.0         wateRmelon_1.36.0                                 
[37] illuminaio_0.34.0                                  IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[39] ROC_1.68.0                                         lumi_2.44.0                                       
[41] methylumi_2.38.0                                   minfi_1.38.0                                      
[43] bumphunter_1.34.0                                  locfit_1.5-9.4                                    
[45] iterators_1.0.13                                   foreach_1.5.1                                     
[47] Biostrings_2.60.1                                  XVector_0.32.0                                    
[49] SummarizedExperiment_1.22.0                        MatrixGenerics_1.4.0                              
[51] FDb.InfiniumMethylation.hg19_2.2.0                 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2           
[53] GenomicFeatures_1.44.0                             GenomicRanges_1.44.0                              
[55] GenomeInfoDb_1.28.1                                ggplot2_3.3.5                                     
[57] reshape2_1.4.4                                     scales_1.1.1                                      
[59] matrixStats_0.59.0                                 limma_3.48.1                                      
[61] org.Hs.eg.db_3.13.0                                AnnotationDbi_1.54.1                              
[63] IRanges_2.26.0                                     S4Vectors_0.30.0                                  
[65] Biobase_2.52.0                                     sesame_1.10.4                                     
[67] sesameData_1.10.0                                  rmarkdown_2.9                                     
[69] ExperimentHub_2.0.0                                AnnotationHub_3.0.1                               
[71] BiocFileCache_2.0.0                                dbplyr_2.1.1                                      
[73] BiocGenerics_0.38.0                               

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3                SparseM_1.81                  rtracklayer_1.52.0            tidyr_1.1.3                  
  [5] ggpmisc_0.4.0                 bit64_4.0.5                   DelayedArray_0.18.0           data.table_1.14.0            
  [9] KEGGREST_1.32.0               RCurl_1.98-1.3                GEOquery_2.60.0               generics_0.1.0               
 [13] snow_0.4-3                    callr_3.7.0                   RSQLite_2.2.7                 bit_4.0.4                    
 [17] tzdb_0.1.2                    xml2_1.3.2                    lubridate_1.7.10              httpuv_1.6.1                 
 [21] ggpp_0.4.1                    assertthat_0.2.1              tidyverse_1.3.1               xfun_0.24                    
 [25] hms_1.1.0                     evaluate_0.14                 promises_1.2.0.1              fansi_0.5.0                  
 [29] restfulr_0.0.13               scrime_1.3.5                  progress_1.2.2                readxl_1.3.1                 
 [33] DBI_1.1.1                     reshape_0.8.8                 purrr_0.3.4                   ellipsis_0.3.2               
 [37] dplyr_1.0.7                   ggpubr_0.4.0                  backports_1.2.1               annotate_1.70.0              
 [41] biomaRt_2.48.2                sparseMatrixStats_1.4.0       vctrs_0.3.8                   remotes_2.4.0                
 [45] quantreg_5.86                 abind_1.4-5                   cachem_1.0.5                  withr_2.4.2                  
 [49] GenomicAlignments_1.28.0      xts_0.12.1                    prettyunits_1.1.1             mclust_5.4.7                 
 [53] cluster_2.1.2                 crayon_1.4.1                  edgeR_3.34.0                  pkgconfig_2.0.3              
 [57] pkgload_1.2.1                 rlang_0.4.11                  lifecycle_1.0.0               nleqslv_3.3.2                
 [61] sandwich_3.0-1                MatrixModels_0.5-0            filelock_1.0.2                affyio_1.62.0                
 [65] modelr_0.1.8                  cellranger_1.1.0              randomForest_4.6-14           rprojroot_2.0.2              
 [69] rngtools_1.5                  base64_2.0                    carData_3.0-4                 boot_1.3-28                  
 [73] Rhdf5lib_1.14.2               zoo_1.8-9                     reprex_2.0.0                  processx_3.5.2               
 [77] png_0.1-7                     rjson_0.2.20                  bitops_1.0-7                  KernSmooth_2.23-20           
 [81] rhdf5filters_1.4.0            blob_1.2.2                    DelayedMatrixStats_1.14.0     doRNG_1.8.2                  
 [85] stringr_1.4.0                 nor1mix_1.3-0                 readr_2.0.0                   rstatix_0.7.0                
 [89] ggsignif_0.6.2                memoise_2.0.0                 magrittr_2.0.1                zlibbioc_1.38.0              
 [93] compiler_4.1.0                BiocIO_1.2.0                  RColorBrewer_1.1-2            clue_0.3-59                  
 [97] Rsamtools_2.8.0               cli_3.0.1                     affy_1.70.0                   ps_1.6.0                     
[101] PerformanceAnalytics_2.0.4    tidyselect_1.1.1              stringi_1.7.3                 forcats_0.5.1                
[105] yaml_2.2.1                    askpass_1.1                   grid_4.1.0                    tools_4.1.0                  
[109] rio_0.5.27                    rstudioapi_0.13               foreign_0.8-81                digest_0.6.27                
[113] BiocManager_1.30.16           shiny_1.6.0                   wheatmap_0.1.0                Rcpp_1.0.7                   
[117] car_3.0-11                    siggenes_1.66.0               broom_0.7.8                   BiocVersion_3.13.1           
[121] later_1.2.0                   httr_1.4.2                    colorspace_2.0-2              rvest_1.0.0                  
[125] XML_3.99-0.6                  fs_1.5.0                      splines_4.1.0                 conquer_1.0.2                
[129] multtest_2.48.0               sessioninfo_1.1.1             xtable_1.8-4                  nloptr_1.2.2.2               
[133] jsonlite_1.7.2                testthat_3.0.4                R6_2.5.0                      pillar_1.6.1                 
[137] htmltools_0.5.1.1             mime_0.11                     minqa_1.2.4                   glue_1.4.2                   
[141] fastmap_1.1.0                 interactiveDisplayBase_1.30.0 beanplot_1.2                  codetools_0.2-18             
[145] pkgbuild_1.2.0                utf8_1.2.1                    lattice_0.20-44               tibble_3.1.2                 
[149] curl_4.3.2                    zip_2.2.0                     openxlsx_4.2.4                openssl_1.4.4                
[153] desc_1.3.0                    munsell_0.5.0                 rhdf5_2.36.0                  GenomeInfoDbData_1.2.6       
[157] HDF5Array_1.20.0              haven_2.4.1                   gtable_0.3.0

Hi there,

I very much appreciate this library and have been using it across the last month or two.

A couple of things of note,

In file DNAmage.R, L253, please correct a typo:

change     skinHorvath <- ageAcc2(skinHorvath, df, lab = "Hovarth2")
to         skinHorvath <- ageAcc2(skinHorvath, df, lab = "Horvath2")

Also, consider consistent naming for this clock, either skinHorvath or Hovarth2. It's easier to build loops on returned data with consistent naming.

Hi,

I'm getting an error when using:

methylclock <- DNAmAge(
  x = beta1,
  age = age2,
  clocks = "Hannum",
  fastImp = TRUE
)

Which produces this error:

Error in UseMethod("full_join") : 
  no applicable method for 'full_join' applied to an object of class "NULL"

Everything is ok when doing:

methylclock <- DNAmAge(
  x = beta1,
  age = age2,
  clocks = c("Horvath","Hannum"),
  fastImp = TRUE
)

When Horvath isn't used, the "out" object will still be NULL when it's joined by another output:

out <- NULL
  if (1 %in% method) {
    out <- Horvath
  }
  if (2 %in% method) {
    out <- out %>% full_join(Hannum, by = "id")
  }

Best regards, Tom

Hi,
I got unexpected results for skinHorvath and Hannum clocks. Every patient in my data has estimated DNAm age that is lower than their chronological age (Though correlation between DNAmAge and chronAge is high ( R>0.85)). For other clocks such as Horvath and Levin, some patients have lower and some higher DNAm age compared to their chronological age (as one would expect). Do you know if there is something unique about the models used for skinHorvath and Hannum that may explain these underestimates? I used "methylclock version 1.6.0". Thank you!

Hello,

I have encountered an error while attempting to use DNAmAge() to obtain DNAmAge, AgeAcDiff, IEAA, and EEAA from a methylation dataset.

I'm running the code below:

DNAmAge(Beta,
                         clocks=c("Horvath"),
                         toBetas=FALSE,
                         cell.count=TRUE,
                         cell.count.reference = "blood gse167998",
                         age=60,
                         min.perc=0.25)

The error I receive is:

Error in DNAmAge(Beta, clocks = c("Horvath"), toBetas = FALSE, cell.count = TRUE,  : 
  cell counts cannot be estimated since
                meffilEstimateCellCountsFromBetas function is giving an error.
                Probably your data do not have any of the required CpGs for that
                reference panel.
In addition: Warning message:
In knnimp(x, k, maxmiss = rowmax, maxp = maxp) :
  40589 rows with more than 50 % entries missing;
 mean imputation used for these rows

The error appears to indicate that I do not have the appropriate CpGs for "blood gse167998."

However, I know that my beta matrix contains the CpGs in Salas et al. 2022 because I used the FlowSorted.BloodExtended.EPIC dataset to perform leukocyte deconvolution on this dataset.

This also happens when I try to use other reference sets, e.g., Salas' "blood idoloptimized epic."

I took a look at the meffilEstimateCellCountsFromBetas function within DNAmAge() using trace("DNAmAge", edit=T). I thought maybe this error was thrown because meffilEstimateCellCountsFromBetas() is now meffil.estimate.cell.counts.from.betas().

I updated the name of the meffil function in DNAmAge(). Unfortunately, the same error occurred.

Will you please assist me in figuring out why this is happening?

Also, am I able to provide my own cell type estimates?

Thank you very much!

Courtney

Hello,

I am currently trying to to make cell estimates with EPIC data.

I can't seem to find any cell.count.reference argument. There are other arguments in the meffil package for this but not cell.count.reference.

Is it possible to detail the use of this argument in the supplementary material as well?

Thanks.

Thank you for creating this very useful package. I can load and use methylcock (version 1.0.1) in R 4-1.3 (Bioconductor version 3.14). However, when I upgrade to R-4.2 (with Bioconductor version 3.15) and install methylcock (version 1.2.0) , It cannot be loaded. The error massage say:
Error: package ‘methylclockData’ required by ‘methylclock’ could not be found

and in the installation process, there were warning massages:
Warning messages:
1: In install.packages(...) :
installation of package ‘openssl’ had non-zero exit status
2: In install.packages(...) :
installation of package ‘GenomeInfoDbData’ had non-zero exit status
3: In install.packages(...) :
installation of package ‘roxygen2’ had non-zero exit status
4: In install.packages(...) :
installation of package ‘methylclockData’ had non-zero exit status

Is there a way to solve this issue?

I have EPIC methylation with 679491 CpGs and 1073 samples, it ran DNAmGA successfully. But it failed to run DNAmAge, I used fastImp=TRUE . I set clocks =“ Hannum” or “Horvath”. It showed Error in apply(cpgs [, cpgs.in],2, median,na.rm=TRUE): object ‘cpgs.in’ not found
Calls: DNAmAge ->cpgs_imputation—>apply
Execution halted.
If I use fastImp=FALSE, job killed after reaching LSF memory limit . I ran the job on cluster .

Do you have suggestion?

thank you’

It seems that the package expects that the age is variable which is included in the parent environment.

When you call the method DNAmAge from within your own R environment and you specify age as a environment variable the methods works.

However if you specify age in the method call like here DNAmAge(age = age, ....) it fails.

This error occurs: Error in eval(predvars, data, env) : object 'age' not found

Hi,

I'm getting a stack overflow error when using a big dataframe as input:

> dim(beta)
[1] 442644    951

And running this with:

methylclock1 <- DNAmAge(
  x = beta,
  age = age1,
  fastImp = TRUE
)

It gives me back this error (traceback() doesn't give any extra details):

Error: segfault from C stack overflow

Maybe a recursion problem in one of your C scripts? Using only Horvath didn't produce an error for this dataset, but when using all clocks it returned this stack overflow message. I couldn't try other clocks independently due to my other issue (#8). I've checked my memory usage, but that wasn't the problem. Any idea what could be the problem?
Best regards, Tom

I believe the descriptions of IEAA and EEAA on the Bioconductor vignette are incorrect (https://www.bioconductor.org/packages/release/bioc/vignettes/methylclock/inst/doc/methylclock.html#chronological-and-biological-dnam-age-estimation).

The current descriptions are:
IEAA (Intrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age.
EEAA (Extrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age adjusted by cell counts. This measure was also known as DNAmAge acceleration residual in the first Horvath’s paper.

I believe the correct descriptions should be:
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.

Hello,

Can this package be used with the latest EPIC Array (v2).

Thank you in advance for your answer.

Hello,

I have Bed9+ files for WGBS and I wanted to estimate their ages. In the Bed9+ files, I have the beta values (in percentages) and the genome position rather than the CpG ID. Is there a way I could use this Bed9+ file directly, or use a tool/R Function to transform it to an accepted input format?

Thank you in advance!

Thank you for developing this useful package! I am having trouble implementing the DNAmAge function with normalization = TRUE. Running DNAmAge with or without normalization has similar run time and produces identical estimates, so it doesn't appear that the data are being normalized. When looking at the code for DNAmAge, I don't see when the normalization argument is used in DNAmAge or predAge.

Thanks for your help!

Hi,

The function checkClocksGA() returns a table containing the information on the number of CpGs present in the individual clocks and the number of missing CpGs from your datasets. For the Bohlin clock, the number of missing CpGs is displayed column Cpgs_in_clock, where it should state 96 (actual number of CpGs in clock) therefore the missing percentage is always at 100.
Is it possible to fix this that it is correctly displayed?

Thank you!