Differential Methylation Analysis Using Multi-Factor Design in BS-seq Data

This R script performs differential methylation analysis on bisulfite sequencing (BS-seq) data using a multi-factor experimental design. It leverages the <b>DSS</b> package to model methylation levels across multiple timepoints and biological conditions (e.g., control vs. diapause). The script identifies both differentially methylated loci (DMLs) and regions (DMRs), and supports statistical testing of main effects and interactions.The analysis is based on a <code>BSseq</code> object previously constructed from DSS-formatted files (e.g., via <code>makeobject.r</code>).<b>Process Overview:</b>Loads a pre-saved R environment (<code>Loaded_Dataframe.RData</code>) containing the <code>BSobj</code>.Defines a two-factor experimental design (timepoint × condition).Fits a multi-factor model using <code>DMLfit.multiFactor()</code>.Conducts hypothesis tests for condition, timepoint, and interaction effects.Calls significant DMRs and DMLs based on p-value and FDR thresholds.Outputs result tables to disk for further analysis or visualization.<b>Inputs:</b><code>Loaded_Dataframe.RData</code> containing a <code>BSseq</code> object (<code>BSobj</code>).Sample metadata encoded directly in the script:<code>timepoint</code>: e.g., Day6, Day12, ..., Day30<code>condition</code>: e.g., control, diapause<b>Outputs:</b><code>dmls.condition.txt</code>: DMLs by condition<code>dmls.timepoint.txt</code>: DMLs by timepoint<code>dmls.txt</code>: DMLs by interactionOptional output: top-ranked CpGs and DMR regions for each test<b>Software Requirements:</b>R (≥ 4.0)Packages: <code>DSS</code>, <code>bsseq</code><b>Usage Notes:</b>The design matrix assumes 40 samples with balanced replicates across timepoints and conditions. Adjust <code>timepoint</code> and <code>condition</code> vectors if your dataset differs.Adjust thresholds and coefficients in <code>DMLtest.multiFactor()</code> and <code>callDMR()</code> as needed for your specific biological questions.Ensure <code>BSobj</code> is correctly matched to sample names used in the experimental design.