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Visualization
BarBoxComutationDotForestHeatmapHistogramLollipopMafsummaryMaftitvManhattanOncoprintPM-AdiversityPM-comp-corPM-comp-funcPM-comp-taxaPM-DistributionPM-HclusterPM-HeatmapPM-Marker-CorrPM-Marker-RFscorePM-Marker-TestPM-NetworkPM-PcaPM-PcoaPM-plot-taxaQ-QSurvivalUpsetVenn
Home Visualization
PM-Pca
program
PM-Pca
PM-Pca

Data

Data(adb)
Example file
Meta(txt)
Example file

Parameters

false
True
False
References
Yuzhu Chen JL, Yufeng Zhang, Mingqian Zhang, Zheng Sun, Gongchao Jing, Shi Huang , Xiaoquan Su. Parallel-Meta Suite: interactive and rapid microbiome data analysis on multiple platforms. 2022.
https://github.com/qdu-bioinfo/parallel-meta-suite
Instructions

Perform principal component analysis (PCA) and plot

Use the functions in the vegan package to process the distance matrix, perform principal component analysis, and calculate the scores of the first three principal components. Based on the classification information in the metadata, draw a PCA plot for each categorical variable, and use ggplot2 to generate scatter plots including PC1 vs PC2, PC1 vs PC3, and PC2 vs PC3.

Contributor(s)
XiaoQuan Su
suxq@qdu.edu.cn
#Runs
24
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