Create a methyl_surro Object — surro

This function takes a methylation matrix and a named vector of weights and returns an object of class methyl_surro. The function aligns the methylation data with the weights, adding missing probes as needed.

Usage

surro_set(methyl, weights, intercept = NULL)

Arguments

methyl: A matrix of methylation data with CpG sites as row names and samples as column names. All values must be numeric.
weights: A named numeric vector with CpG site names, representing the regression weights.
intercept: An optional name of the intercept term within the weights. If provided, the specified intercept will be separated from the weights and stored separately.

Value

An object of class methyl_surro containing the following components:

methyl: The methylation matrix, filtered to include only probes present in the weights, with additional rows of NAs added for weights not present in the methylation data.
weights: A named numeric vector of weights (excluding intercept if specified).
intercept: The numeric value of the intercept, if provided. NULL otherwise.

Details

The function performs the following operations:

Validates input format and structure
Extracts intercept from weights if specified
Filters methylation matrix to include only probes present in weights
Adds rows of NA values for weight probes not present in methylation data
Returns a properly structured methyl_surro object

Missing probes (those in weights but not in methylation data) should be handled using reference_fill before calculating surrogate values.

Examples

# Load Methylation Beta Matrix
data(beta_matrix_comp, package = "MethylSurroGetR")
print(beta_matrix_comp)
#>          samp1      samp2      samp3     samp4      samp5
#> cg01 0.1028646 0.32037324 0.48290240 0.7205963 0.36948887
#> cg02 0.2875775 0.78830514 0.40897692 0.8830174 0.94046728
#> 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 0.04205953 0.3279207 0.95450365
#> cg08 0.8895393 0.69280341 0.64050681 0.9942698 0.65570580
#> cg09 0.8930511 0.09359499 0.60873498 0.9540912 0.09104400
#> cg10 0.8864691 0.46677904 0.41068978 0.5854834 0.14190691
#> cg12 0.1750527 0.51150546 0.14709469 0.4045103 0.69000710
#> cg13 0.9630242 0.90229905 0.69070528 0.7954674 0.02461368
#> cg14 0.1306957 0.59998896 0.93529980 0.6478935 0.61925648
#> 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 0.67299909
#> cg20 0.6567581 0.95447383 0.94772694 0.2197676 0.73707774

# Load Weights Data Frame and create vector
data(wts_df, package = "MethylSurroGetR")
wts_vec_lin <- setNames(wts_df$wt_lin, rownames(wts_df))
print(wts_vec_lin)
#>         cg02         cg03         cg06         cg07         cg08         cg11 
#> -0.009083377 -0.001155999  0.005978497 -0.007562015  0.001218960 -0.005869372 
#>         cg13         cg15         cg17         cg18    Intercept 
#> -0.007449367  0.005066157  0.007900907 -0.002510744  1.211000000 

# Build the methyl_surro Object
surrogate <- surro_set(methyl = beta_matrix_comp,
                       weights = wts_vec_lin,
                       intercept = "Intercept")
#> Added 5 missing probes with NA values (50.0% of weight probes missing from methylation data).
#> Filtered methylation matrix from 15 to 10 probes to match weights.
print(surrogate)
#> $methyl
#>          samp1     samp2      samp3     samp4      samp5
#> cg02 0.2875775 0.7883051 0.40897692 0.8830174 0.94046728
#> cg07 0.8998250 0.2460877 0.04205953 0.3279207 0.95450365
#> cg08 0.8895393 0.6928034 0.64050681 0.9942698 0.65570580
#> cg13 0.9630242 0.9022990 0.69070528 0.7954674 0.02461368
#> cg17 0.1428000 0.4145463 0.41372433 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"

# Example without intercept
surrogate_no_int <- surro_set(methyl = beta_matrix_comp,
                              weights = wts_vec_lin[-which(names(wts_vec_lin) == "Intercept")])
#> Added 5 missing probes with NA values (50.0% of weight probes missing from methylation data).
#> Filtered methylation matrix from 15 to 10 probes to match weights.
print(surrogate_no_int)
#> $methyl
#>          samp1     samp2      samp3     samp4      samp5
#> cg02 0.2875775 0.7883051 0.40897692 0.8830174 0.94046728
#> cg07 0.8998250 0.2460877 0.04205953 0.3279207 0.95450365
#> cg08 0.8895393 0.6928034 0.64050681 0.9942698 0.65570580
#> cg13 0.9630242 0.9022990 0.69070528 0.7954674 0.02461368
#> cg17 0.1428000 0.4145463 0.41372433 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
#> NULL
#> 
#> attr(,"class")
#> [1] "methyl_surro"