10. beta_PCA

10.1. Overview

beta_PCA performs principal component analysis (PCA) on DNA methylation Beta-value matrices to visualize relationships among samples.

The input matrix must have CpGs in rows and samples in columns. CpGs containing missing values across the samples used for PCA are removed. By default, CpG values are standardized before PCA.

10.2. Input Files

10.2.1. Beta matrix

The first column contains CpG IDs and the remaining columns contain sample Beta-values. Common delimiters and compressed input are supported.

Example:

CpG_ID   Sample_01   Sample_02   Sample_03   Sample_04
cg_001   0.831035    0.878022    0.794427    0.880911
cg_002   0.249544    0.209949    0.234294    0.236680
cg_003   0.845065    0.843957    0.840184    0.824286

At least two sample columns are required. Duplicate CpG IDs are reduced to the first occurrence, whereas sample IDs must be unique.

10.2.2. Group file

A two-column sample/group file is required. Comma- and tab-delimited files are supported, with or without a header.

Example:

Sample,Group
Sample_01,normal
Sample_02,normal
Sample_03,tumor
Sample_04,tumor

Samples present in the Beta matrix but absent from the group file are excluded with a warning. At least two samples must be shared between the two files.

10.3. Usage

Basic usage:

beta_PCA \
    -i cirrHCV_vs_normal.data.tsv \
    -g cirrHCV_vs_normal.grp.csv \
    -o HCV_vs_normal

Useful options include:

-n, --n_components, --ncomponent – number of principal components to calculate (default: 2)
-l, --label – add sample IDs to the plot
-c, --marker – plot marker: o, ., ^, s, D, or x (default: o)
-a, --alpha – point opacity between 0 and 1 (default: 0.7)
--legend_location – legend position (default: best)
--loading – write the PCA loading matrix
--no_standardize – skip CpG standardization before PCA
--width / --height – plot size in inches (default: 8 x 8)
--dpi – plot resolution (default: 300)
-o, --out_prefix, --output – output prefix

Display all options with:

beta_PCA -h

10.4. Output

For output prefix HCV_vs_normal, the command writes:

HCV_vs_normal.PCA.tsv – PCA scores for each sample
HCV_vs_normal.PCA_variance.tsv – explained and cumulative variance for each component
HCV_vs_normal.PCA.png – PCA scatter plot

When --loading is used, it also writes:

HCV_vs_normal.PCA_loadings.tsv – CpG loading matrix

The variance explained by each component is also reported to the log.

10.5. Example Data

10.6. Example Figure