Fig. 3
From: OmicsOne: associate omics data with phenotypes in one-click
The phenotype-associated feature discovery procedures (including differential expression analysis, Dimensionality reduction, and feature clustering). A Interactive volcano plot of the result of differential expression analysis using hypothesis tests and multiple tests corrections applied on the glycoproteome data of HGSOC. B Interactive box plot for each feature (glycopeptide) expressed in different phenotypes (e.g., Tumor vs. Non-Tumor samples) in the glycoproteome data set of HGSOC. C Dimensionality reduction using principal component analysis (PCA) for most variant features in the proteome data set of HGSOC. D Variance ratio values of top 10 principal components (PCs) used in the PCA model applied on the proteome data set of HGSOC. The top 10 features contributed to each PC are provided in the hover data information. E Clustering analysis for features identified in the proteome data set of HGSOC associated with the phenotype of pathological status (Tumor and Non-tumor)