10. beta_PCA.py

10.1. Description

This program performs PCA (principal component analysis) for samples.

Example of input data file

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
...

Example of input group file

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

Notes

• Rows with missing values will be removed
• Beta values will be standardized into z scores
• Only the first two components will be visualized
• Variance% explained by each component will be printed to screen

Options:

--version	show program’s version number and exit
-h, --help	show this help message and exit
-i INPUT_FILE, --input_file=INPUT_FILE
	Tab-separated data frame file containing beta values with the 1st row containing sample IDs and the 1st column containing CpG IDs.
-g GROUP_FILE, --group=GROUP_FILE
	Comma-separated group file defining the biological groups of each sample. Different groups will be colored differently in the PCA plot. Supports a maximum of 20 groups.
-n N_COMPONENTS, --ncomponent=N_COMPONENTS
	Number of components. default=2
-l, --label	If True, sample ids will be added underneath the data point. default=False
-c PLOT_CHAR, --char=PLOT_CHAR
	Ploting character: 1 = ‘dot’, 2 = ‘circle’. default=1
-a PLOT_ALPHA, --alpha=PLOT_ALPHA
	Opacity of dots. default=0.5
-x LEGEND_LOCATION, --loc=LEGEND_LOCATION
	Location of legend panel: 1 = ‘topright’, 2 = ‘bottomright’, 3 = ‘bottomleft’, 4 = ‘topleft’. default=1
-o OUT_FILE, --output=OUT_FILE
	The prefix of the output file.

10.2. Input files (examples)

• cirrHCV_vs_normal.data.tsv
• cirrHCV_vs_normal.grp.csv

10.3. Command

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

10.4. Output files

• HCV_vs_normal.PCA.r
• HCV_vs_normal.PCA.tsv
• HCV_vs_normal.PCA.pdf