29. dmc_logit

29.1. Overview

dmc_logit performs differential CpG analysis using logistic regression on methylated-read and total-read counts.

The input matrix must have CpGs/probes in rows and samples in columns. Each methylation value is represented as:

methylated_count,total_count

where both values are non-negative integers, methylated_count <= total_count, and total_count > 0.

The first variable in the group/covariate file is treated as the primary grouping variable. Additional variables are included as covariates.

29.2. Statistical Model

For each CpG, the model fitted in R is:

glm(cbind(m, t - m) ~ covariates, family = ...)

where m is the methylated-read count and t is the total-read count.

Two GLM families are available:

Value	Family	Description
`1`	`quasibinomial`	Default. Allows for overdispersion relative to a standard binomial model.
`2`	`binomial`	Standard binomial logistic regression.

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

29.3. Requirements

dmc_logit requires Rscript to be available in PATH.

29.4. Input Files

29.4.1. Methylation count 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
cg_001   12,14       26,37       0,18        10,24
cg_002   18,18       47,54       19,23       18,19

Sample IDs must be unique.

Plain text, .gz, .bz2, and other formats supported by the CpGtools reader may be used.

Fields that do not match the methylated,total pattern are treated as missing values. Syntactically valid count pairs are considered errors when total_count <= 0 or methylated_count > total_count.

29.4.2. Group and Covariate File

The group/covariate file 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 count and covariate values before model fitting.

29.5. Usage

Quasibinomial model:

dmc_logit \
    -i test_04_TwoGroup.tsv.gz \
    -g test_04_TwoGroup.grp.csv \
    -o output_quasibin

Binomial model:

dmc_logit \
    -i test_04_TwoGroup.tsv.gz \
    -g test_04_TwoGroup.grp.csv \
    -f 2 \
    -o output_bin

Available options are:

-i, --input_file – methylated/total count matrix
-g, --group – group/covariate file
-f, --family {1,2} – GLM family; 1 = quasibinomial, 2 = binomial (default: 1)
-o, --output – output prefix
--version – show the CpGtools version

Display all options with:

dmc_logit -h

29.6. Output

For output prefix output_quasibin, the command writes:

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

29.6.1. Final p-value Table

<prefix>.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.

29.6.2. Full Model Results

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

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

29.7. Important Behavior

Per-CpG fitting uses na.omit in R, so samples with missing count or covariate values are removed for that CpG.
Input rows with fewer data fields than the header are padded with NA; extra values are ignored with a warning.
The primary grouping variable must be categorical.
If all methylated counts for a CpG sum to zero, the model p-values are set to missing.
When the primary grouping variable has multiple factor levels, dmc_logit 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.