28. dmc_glm

28.1. Overview

dmc_glm performs differential CpG analysis using a Gaussian generalized linear model (GLM) fitted to DNA methylation Beta-values.

The input matrix must have CpGs/probes in rows and samples in columns. The first variable in the group/covariate file is treated as the primary grouping variable; additional variables are included as covariates.

For each CpG, the model fitted in R is:

glm(y ~ covariates, family = gaussian)

Raw p-values for the primary grouping variable are extracted from the R model output and adjusted for multiple testing using the CpGtools Benjamini-Hochberg procedure.

28.2. Requirements

dmc_glm requires Rscript to be available in PATH.

28.3. Input Files

28.3.1. Beta-value matrix

The first row contains sample IDs and the first column contains CpG/probe IDs.

Example:

CpG_ID      Sample_01   Sample_02   Sample_03   Sample_04
cg00001099  0.775       0.812       0.623       0.598
cg00000363  0.611       0.602       0.470       0.455

Sample IDs must be unique.

Non-numeric values and NaN are treated as missing values. Plain-text, .gz, and .bz2 inputs supported by the CpGtools reader may be used.

28.3.2. Group and covariate file

The group/covariate file is parsed by CpGtools and may contain a primary grouping variable plus additional covariates.

The first variable must be categorical. Additional variables may be categorical or continuous.

Example:

Sample,Group,Sex,Age
Sample_01,Control,F,52
Sample_02,Control,M,47
Sample_03,Case,F,61
Sample_04,Case,M,58

Every sample listed in the group file must occur in the methylation matrix.

Samples present in the methylation matrix but absent from the group file are ignored by assigning NA to their covariates before model fitting.

28.4. Usage

Basic two-group analysis:

dmc_glm \
    -i test_05_TwoGroup.tsv.gz \
    -g test_05_TwoGroup.grp.csv \
    -o GLM_2G

Analysis with additional covariates:

dmc_glm \
    -i test_05_TwoGroup.tsv.gz \
    -g test_05_TwoGroup.grp2.csv \
    -o GLM_2G

Available options are:

-i, --input_file – Beta-value matrix
-g, --group – group/covariate file
-o, --output – output prefix
--version – show the CpGtools version

Display all options with:

dmc_glm -h

28.5. Output

For output prefix GLM_2G, the command writes:

GLM_2G.r – generated R script;
GLM_2G.results.txt – full GLM coefficient and p-value table;
GLM_2G.warnings.txt – stderr produced by Rscript;
GLM_2G.pval.txt – original methylation table with primary-variable p-values and adjusted p-values appended.

28.5.1. Final p-value table

GLM_2G.pval.txt preserves the original input rows and appends:

Column	Description
`pval`	Raw p-value for the first coefficient associated with the primary grouping variable.
`adj.pval`	Benjamini-Hochberg FDR-adjusted p-value.

If a valid primary-variable p-value is unavailable for a CpG, both fields are written as NaN.

28.5.2. Full model results

GLM_2G.results.txt contains estimated coefficients and p-values for all terms returned by the R GLM, including the intercept, primary grouping variable, and any additional covariates.

For categorical variables, R creates factor-level coefficients. The exact column names therefore depend on the variable names and factor levels.

28.6. Important Behavior

CpGs with missing Beta-values are fitted using the available observations through R’s na.omit behavior.
Input rows with fewer values than the header are padded with NA; extra values are ignored with a warning.
The primary grouping variable must be categorical.
When the primary grouping variable has multiple factor levels, dmc_glm currently extracts the first p-value column whose name starts with the primary variable name. The final pval therefore represents one factor coefficient rather than an omnibus test of the entire factor.
If the per-CpG R model fails, that CpG may be absent from <prefix>.results.txt and receives NaN in the final p-value table.