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 DSS 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 BSseq object previously constructed from DSS-formatted files (e.g., via makeobject.r).Process Overview:Loads a pre-saved R environment (Loaded_Dataframe.RData) containing the BSobj.Defines a two-factor experimental design (timepoint × condition).Fits a multi-factor model using DMLfit.multiFactor().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.Inputs:Loaded_Dataframe.RData containing a BSseq object (BSobj).Sample metadata encoded directly in the script:timepoint: e.g., Day6, Day12, ..., Day30condition: e.g., control, diapauseOutputs:dmls.condition.txt: DMLs by conditiondmls.timepoint.txt: DMLs by timepointdmls.txt: DMLs by interactionOptional output: top-ranked CpGs and DMR regions for each testSoftware Requirements:R (≥ 4.0)Packages: DSS, bsseqUsage Notes:The design matrix assumes 40 samples with balanced replicates across timepoints and conditions. Adjust timepoint and condition vectors if your dataset differs.Adjust thresholds and coefficients in DMLtest.multiFactor() and callDMR() as needed for your specific biological questions.Ensure BSobj is correctly matched to sample names used in the experimental design.