1. Overview

The CpGtools package provides a collection of Python programs designed to annotate, perform quality control (QC), visualize, and analyze DNA methylation data generated from the following Illumina platforms:

The CpGtools modules are organized into four main categories:

CpG position analysis modules
CpG signal analysis modules
Differential CpG analysis modules
Predictive modules (under development)

1.1. CpG Position Analysis Modules

These modules focus on analyzing CpG genomic locations and their annotations.

Name	Description
CpG_aggregation.py	Aggregate proportion values of CpGs that located in give genomic regions (eg. CpG islands, promoters, exons, etc.).
CpG_anno_position.py	Add annotation information CpGs according to their genomic coordinates.
CpG_anno_probe.py	Add annotation information to 450K/850K probes.
CpG_density_gene_centered.py	Generate the CpG density (count) profile over gene body and the up/down-stream intergenic regions.
CpG_distrb_chrom.py	Calculate the distribution of CpG over chromosomes.
CpG_distrb_gene_centered.py	Calculate the distribution of CpG over gene-centered genomic regions.
CpG_distrb_region.py	Calculate the distribution of CpG over user-specified genomic regions.
CpG_logo.py	Generate a DNA motif logo and matrices for a given set of CpGs.
CpG_to_gene.py	Assign CpGs to their putative target genes. It uses the algorithm similar to GREAT.

1.2. CpG Signal Analysis Modules

These modules analyze CpG methylation beta values across samples and genomic regions.

Name	Description
beta_PCA.py	Perform PCA (principal component analysis) for samples.
beta_jitter_plot.py	Generate jitter plot (a.k.a. strip chart) and bean plot for each sample.”
beta_m_conversion.py	Convert Beta-value into M-value or vice versa.
beta_profile_gene_centered.py	Calculate the methylation profile (i.e., average beta value) for genomic regions around genes.
beta_profile_region.py	Calculate methylation profile (i.e. average beta value) around the user-specified genomic regions.
beta_stacked_barplot.py	Create stacked barplot for each sample. The stacked barplot showing the proportions of CpGs whose beta values are falling into [0,0.25], [0.25,0.5], [0.5,0.75],[0.75,1]
beta_stats.py	Summarize basic information on CpGs located in each genomic region.
beta_tSNE.py	Perform t-SNE (t-Distributed Stochastic Neighbor Embedding) analysis for samples.
beta_topN.py	Select the top N most variable CpGs (according to standard deviation) from the input file.
beta_trichotmize.py	Use Bayesian Gaussian Mixture model to trichotmize beta values into three status: ‘Un-methylated’,’Semi-methylated’, ‘Full-methylated’, and ‘unassigned’.
beta_UMAP.py	Perform UMAP (Uniform Manifold Approximation and Projection) for samples.
beta_selectNbest.py	Select the K best features using ANOVA, Mutual information or Chi-squared stat.
beta_combat.py	Corrects batch effect using the combat algorithm.

—

1.3. Differential CpG Analysis Modules

These modules identify CpGs that are differentially methylated between experimental or biological groups.

Name	Description
dmc_Bayes.py	Differential CpG analysis using the Bayesian approach. (for 450K/850K data)
dmc_bb.py	Differential CpG analysis using the beta-binomial model. (for RRBS/WGBS count data)
dmc_fisher.py	Differential CpG analysis using Fisher’s Exact Test. (for RRBS/WGBS count data)
dmc_glm.py	Differential CpG analysis using the GLM generalized liner model. (for 450K/850K data)
dmc_logit.py	Differential CpG analysis using logistic regression model. (for RRBS/WGBS count data)
dmc_nonparametric.py	Differential CpG analysis using Mann-Whitney U test for two group comparison, and the Kruskal-Wallis H-test for multiple groups comparison.
dmc_ttest.py	Differential CpG analysis using T test. (for 450K/850K data)

1.4. Predictive Modules

These modules aim to predict phenotypes or biological attributes from DNA methylation profiles.

Name	Description
predict_sex.py	Predict sex based on the semi-methylation (also known as genomic imprinting) ratio.