Remove covariate effects from methylayion values by fitting probe-specific linear models
GetResiduals(
dnam,
betaToM = TRUE,
epsilon = 1e-08,
pheno_df,
covariates_char,
nCores_int = 1L,
...
)data frame or matrix of methylation values with row names = CpG IDs and column names = sample IDs. This is often the genome-wide array data.
indicates if methylation beta values (ranging from [0, 1])
should be converted to M values (ranging from (-Inf, Inf)). Note that if
beta values are the input to dnam, then betaToM should be set
to TRUE, otherwise FALSE.
When transforming beta values to M values, what should be done
to values exactly equal to 0 or 1? The M value transformation would yield
-Inf or Inf which causes issues in the statistical model. We
thus replace all values exactly equal to 0 with 0 + epsilon, and
we replace all values exactly equal to 1 with 1 - epsilon. Defaults
to epsilon = 1e-08.
a data frame with phenotype and covariates, with variable
Sample indicating sample IDs.
character vector for names of the covariate variables
Number of computing cores to be used when executing code in parallel. Defaults to 1 (serial computing).
Dots for additional arguments passed to the cluster constructor.
See CreateParallelWorkers for more information.
output a matrix of residual values in the same dimension as
dnam
This function fits an ordinary linear model predicting methylation values for each probe from the specified covariates. This process will be useful in scenarios where methylation values in a region or at an individual probe are known a priori to have differential methylation independent of the disease or condition of interest.
data(betasChr22_df)
data(pheno_df)
GetResiduals(
dnam = betasChr22_df[1:10, 1:10],
betaToM = TRUE,
pheno_df = pheno_df,
covariates_char = c("age.brain", "sex", "slide")
)
#> Phenotype data is not in the same order as methylation data. We used column Sample in phenotype data to put these two files in the same order.
#> GSM1443279 GSM1443326 GSM1443389 GSM1443434 GSM1443475
#> cg00004192 0.58640646 -0.49767945 -0.06457134 -0.212505570 0.27933449
#> cg00004775 -0.21730195 0.49050104 0.15860444 0.576896507 -0.55293114
#> cg00012194 0.05144935 -0.12025395 -0.08577237 -0.174337410 0.25759590
#> cg00013618 -0.20931589 -0.41968875 -0.09221866 0.134788358 0.87188266
#> cg00014104 -0.19491518 0.01044988 0.05291720 -0.205485110 0.75532381
#> cg00014733 0.09079996 0.02703190 -0.13772317 -0.003066361 -0.07431340
#> cg00017461 -0.23998567 0.15939361 0.03531002 -0.024738248 -0.03561947
#> cg00021762 -0.43595886 -0.26037770 0.19643290 -0.082914671 0.08327474
#> cg00022145 0.06637352 -0.38586662 -0.60636590 0.355595751 0.11474364
#> cg00024416 0.25931864 -0.20265774 -0.35289785 0.596480609 -0.86818331
#> GSM1443547 GSM1443573 GSM1443577 GSM1443640 GSM1443663
#> cg00004192 0.063698756 0.06457134 0.15377362 -0.24765878 -0.12536953
#> cg00004775 -0.293663232 -0.15860444 0.22103453 -0.16411811 -0.06041765
#> cg00012194 0.149947780 0.08577237 -0.22311432 -0.00409128 0.06280393
#> cg00013618 0.128276611 0.09221866 -0.54441256 0.06534103 -0.02687146
#> cg00014104 0.037841256 -0.05291720 -0.71285649 0.10681760 0.20282424
#> cg00014733 -0.106690470 0.13772317 -0.09044167 0.22118393 -0.06450389
#> cg00017461 -0.012415153 -0.03531002 -0.08846412 0.16945040 0.07237864
#> cg00021762 0.083745738 -0.19643290 0.37353586 0.26066407 -0.02196918
#> cg00022145 -0.005477893 0.60636590 -0.33524293 0.52130004 -0.33142552
#> cg00024416 0.066775059 0.35289785 0.71794320 -0.17137536 -0.39830109