MethylSurroGetR

Tools to Generate Predicted Values of DNAm Surrogates in R

Joshua A. Goode

Trey Smith

June 19, 2025

Some Quick Notes

https://bit.ly/jagoode

Stop Me
R Resources

🚧 Under Development 🚧

This package is still in development and not yet ready for general use.

Proceed with caution!

What is MethylSurroGetR?

Simple set of user-friendly functions for generating predicted values from existing DNA methylation surrogates

Data Management
Handling Missing Data
Generating Estimates

What is MethylSurroGetR?

Does not develop surrogates
Existing packages already address well-known clocks
- methylCIPHER
Fills in gap for recently published and/or less well-known surrogates
Allows construction of MRSs/PEGs from EWAS results

Install & Load

MethylSurroGetR is available on GitHub
Can be installed with remotes::install_github()

# install.packages("remotes")
remotes::install_github("jagoode27/MethylSurroGetR")

library(MethylSurroGetR)

Load Example Methylation Data

data("beta_matrix_miss", package = "MethylSurroGetR")
print(beta_matrix_miss)

         samp1      samp2      samp3     samp4      samp5
cg01 0.1028646 0.32037324 0.48290240        NA 0.36948887
cg02 0.2875775         NA         NA 0.8830174         NA
cg04 0.4348927 0.18769112 0.89035022 0.1422943 0.98421920
cg05 0.9849570 0.78229430 0.91443819 0.5492847 0.15420230
cg07 0.8998250 0.24608773         NA 0.3279207 0.95450365
cg08 0.8895393 0.69280341 0.64050681 0.9942698 0.65570580
cg09        NA 0.09359499 0.60873498 0.9540912         NA
cg10 0.8864691         NA         NA 0.5854834 0.14190691
cg12 0.1750527         NA 0.14709469        NA 0.69000710
cg13 0.9630242 0.90229905 0.69070528 0.7954674 0.02461368
cg14 0.1306957         NA 0.93529980 0.6478935         NA
cg16 0.6531019 0.33282354 0.30122890 0.3198206 0.89139412
cg17 0.1428000 0.41454634 0.41372433 0.3688455 0.15244475
cg19 0.3435165 0.48861303 0.06072057 0.3077200         NA
cg20 0.6567581 0.95447383 0.94772694 0.2197676         NA

Load Example Surrogate Weights

data("wts_df", package = "MethylSurroGetR")
print(wts_df)

                wt_lin      wt_prb       wt_cnt
cg02      -0.009083377  0.16511519  0.050895032
cg03      -0.001155999 -0.40515934  0.025844226
cg06       0.005978497 -0.11603036  0.042036480
cg07      -0.007562015 -0.22561636 -0.099875045
cg08       0.001218960  0.31464004 -0.004936685
cg11      -0.005869372 -0.05148366 -0.055976223
cg13      -0.007449367  0.31006435 -0.024036692
cg15       0.005066157  0.31238951  0.022554201
cg17       0.007900907  0.29434232 -0.029640418
cg18      -0.002510744 -0.06016831 -0.077772915
Intercept  1.211000000  0.01900000  0.937000000

wts_vec_lin <- setNames(wts_df$wt_lin, rownames(wts_df))

Create `mehtyl_surro` Object

surro_set(methyl, weights, intercept = NULL)

methyl: Numeric matrix of methylation data
- CpG sites as row names
- sample IDs as column names
weights: Named numeric vector of surrogate weights.
intercept: Optional chacracter string to identify the name of the intercept in the weights object

Create `mehtyl_surro` Object

lin_surrogate <- surro_set(methyl = beta_matrix_miss,
                           weights = wts_vec_lin,
                           intercept = "Intercept")
print(lin_surrogate)

$methyl
         samp1     samp2     samp3     samp4      samp5
cg02 0.2875775        NA        NA 0.8830174         NA
cg07 0.8998250 0.2460877        NA 0.3279207 0.95450365
cg08 0.8895393 0.6928034 0.6405068 0.9942698 0.65570580
cg13 0.9630242 0.9022990 0.6907053 0.7954674 0.02461368
cg17 0.1428000 0.4145463 0.4137243 0.3688455 0.15244475
cg03        NA        NA        NA        NA         NA
cg06        NA        NA        NA        NA         NA
cg11        NA        NA        NA        NA         NA
cg15        NA        NA        NA        NA         NA
cg18        NA        NA        NA        NA         NA

$weights
        cg02         cg03         cg06         cg07         cg08         cg11 
-0.009083377 -0.001155999  0.005978497 -0.007562015  0.001218960 -0.005869372 
        cg13         cg15         cg17         cg18 
-0.007449367  0.005066157  0.007900907 -0.002510744 

$intercept
Intercept 
    1.211 

attr(,"class")
[1] "methyl_surro"

Two Types of Missing Values

Missing Observatons
- probes present in target data
- missing for some samples
Missing Probes
- probes not present in target data
- missing for all samples
  - removed during QC
  - not on chip

Two Types of Missing Values

	samp1	samp2	samp3	samp4	samp5
cg02	0.288	NA	NA	0.883	NA
cg07	0.900	0.246	NA	0.328	0.955
cg08	0.890	0.693	0.641	0.994	0.656
cg13	0.963	0.902	0.691	0.795	0.025
cg17	0.143	0.415	0.414	0.369	0.152
cg03	NA	NA	NA	NA	NA
cg06	NA	NA	NA	NA	NA
cg11	NA	NA	NA	NA	NA
cg15	NA	NA	NA	NA	NA
cg18	NA	NA	NA	NA	NA

Check for Missing Values

methyl_miss(methyl_surro)

methyl_surro: methyl_surro object created with surro_set()

Check for Missing Values

missing <- methyl_miss(methyl_surro = lin_surrogate)
print(missing)

Missing Data Summary for methyl_surro Object
============================================

Total probes: 10
Total samples: 5
Complete probes: 3 (30.0%)
Probes with missing observations: 2 (20.0%)
Completely missing probes: 5 (50.0%)
Overall missing rate: 58.0%

Probes with partial missing data:
cg02 cg07 
 0.6  0.2 

Completely missing probes:
cg03, cg06, cg11, cg15, cg18

Impute Missing Observations

impute_obs(methyl_surro,
           method = c("mean", "median"),
           min_nonmiss_prop = 0)

methyl_surro: methyl_surro object
method: Character string indicating the imputation method
- Current options are “mean” or “median”
- Currently developing KNN and weighted KNN options
min_nonmiss_prop: Optional minimum proportion of non-missing data required in a probe for imputation to proceed

Impute Missing Observations

lin_surrogate <- impute_obs(methyl_surro = lin_surrogate,
                            method = "mean",
                            min_nonmiss_prop = 0)
print(lin_surrogate)

$methyl
         samp1     samp2     samp3     samp4      samp5
cg02 0.2875775 0.5852975 0.5852975 0.8830174 0.58529746
cg07 0.8998250 0.2460877 0.6070843 0.3279207 0.95450365
cg08 0.8895393 0.6928034 0.6405068 0.9942698 0.65570580
cg13 0.9630242 0.9022990 0.6907053 0.7954674 0.02461368
cg17 0.1428000 0.4145463 0.4137243 0.3688455 0.15244475
cg03        NA        NA        NA        NA         NA
cg06        NA        NA        NA        NA         NA
cg11        NA        NA        NA        NA         NA
cg15        NA        NA        NA        NA         NA
cg18        NA        NA        NA        NA         NA

$weights
        cg02         cg03         cg06         cg07         cg08         cg11 
-0.009083377 -0.001155999  0.005978497 -0.007562015  0.001218960 -0.005869372 
        cg13         cg15         cg17         cg18 
-0.007449367  0.005066157  0.007900907 -0.002510744 

$intercept
Intercept 
    1.211 

attr(,"class")
[1] "methyl_surro"

Fill Missing Probes

reference_fill(
  methyl_surro,
  reference,
  type = c("probes", "obs", "all")
)

methyl_surro: methyl_surro object
reference: Named numeric vector of methylation reference values
type: Character string to identify which probes to fill

Fill Missing Probes

data("ref_df", package = "MethylSurroGetR")
print(ref_df)

          mean    median
cg01 0.3992451 0.3694889
cg02 0.6616689 0.7883051
cg03 0.4948262 0.5281055
cg04 0.5278895 0.4348927
cg05 0.6770353 0.7822943
cg06 0.5526592 0.5726334
cg07 0.4940793 0.3279207
cg08 0.7745650 0.6928034
cg09 0.5281033 0.6087350
cg10 0.4982656 0.4667790
cg11 0.4566024 0.5440660
cg12 0.3856340 0.4045103
cg13 0.6752219 0.7954674
cg14 0.5866269 0.6192565
cg15 0.4004940 0.3181810
cg16 0.4996738 0.3328235
cg17 0.2984722 0.3688455
cg18 0.3923206 0.2659726
cg19 0.3747138 0.3435165
cg20 0.7031609 0.7370777

ref_vec_mean <- setNames(ref_df$mean, rownames(ref_df))

Fill Missing Probes

lin_surrogate <- reference_fill(methyl_surro = lin_surrogate,
                                reference = ref_vec_mean,
                                type = "probes")
print(lin_surrogate)

$methyl
         samp1     samp2     samp3     samp4      samp5
cg02 0.2875775 0.5852975 0.5852975 0.8830174 0.58529746
cg07 0.8998250 0.2460877 0.6070843 0.3279207 0.95450365
cg08 0.8895393 0.6928034 0.6405068 0.9942698 0.65570580
cg13 0.9630242 0.9022990 0.6907053 0.7954674 0.02461368
cg17 0.1428000 0.4145463 0.4137243 0.3688455 0.15244475
cg03 0.4948262 0.4948262 0.4948262 0.4948262 0.49482616
cg06 0.5526592 0.5526592 0.5526592 0.5526592 0.55265924
cg11 0.4566024 0.4566024 0.4566024 0.4566024 0.45660238
cg15 0.4004940 0.4004940 0.4004940 0.4004940 0.40049405
cg18 0.3923206 0.3923206 0.3923206 0.3923206 0.39232059

$weights
        cg02         cg03         cg06         cg07         cg08         cg11 
-0.009083377 -0.001155999  0.005978497 -0.007562015  0.001218960 -0.005869372 
        cg13         cg15         cg17         cg18 
-0.007449367  0.005066157  0.007900907 -0.002510744 

$intercept
Intercept 
    1.211 

attr(,"class")
[1] "methyl_surro"

Estimate Surrogate Values

surro_calc(methyl_surro,
           transform = c("linear", "count", "probability"))

methyl_surro: methyl_surro object
transform: Character string specifying the transformation to apply
- "linear": For surrogates estimated with Gaussian models
- "count": For surrogates estimated with Poisson models
- "probability": For surrogates estimated with binomial models

Estimate Surrogate Values

estimates <- surro_calc(methyl_surro = lin_surrogate,
                        transform = "linear")
print(estimates)

   samp1    samp2    samp3    samp4    samp5 
1.197718 1.202317 1.201093 1.199796 1.201382

Piping Commands

estimates <- beta_matrix_miss |>
  surro_set(weights = wts_vec_lin, intercept = "Intercept") |>
  impute_obs(method = "mean") |>
  reference_fill(reference = ref_vec_mean, type = "probes") |>
  surro_calc(transform = "linear")

print(estimates)

   samp1    samp2    samp3    samp4    samp5 
1.197718 1.202317 1.201093 1.199796 1.201382

Thank You

Text-based version of this tutorial is available HERE.
Please feel free to reach out with any questions.

https://bit.ly/jagoode

MethylSurroGetR

Some Quick Notes

Notes on Slide Navigation

🚧 Under Development 🚧

What is MethylSurroGetR?

What is MethylSurroGetR?

Install & Load

Load Example Methylation Data

Load Example Surrogate Weights

Create mehtyl_surro Object

Create mehtyl_surro Object

Two Types of Missing Values

Two Types of Missing Values

Check for Missing Values

Check for Missing Values

Impute Missing Observations

Impute Missing Observations

Fill Missing Probes

Fill Missing Probes

Fill Missing Probes

Estimate Surrogate Values

Estimate Surrogate Values

Piping Commands

Thank You

Create `mehtyl_surro` Object

Create `mehtyl_surro` Object