Association with clinical feature

samples underwent grouping using the z-statistic matrix derived from HVPs, with the intent of correlating proteomic subgroups to clinical and molecular features. We then employed either the Chi-square test or Fisher's exact test to examine the association between proteomic subgroups and discrete sample features. This choice was made based on the number of categories within each feature. For continuous features, we utilized Student’s t-test or ANOVA.

ANOVA was further applied to pinpoint differentially expressed proteins (DEPs) among sample groups. Hierarchical clustering of proteins ensued, driven by the z-statistic matrix, revealing distinctive expression signatures. Following this, pathway enrichment analysis was conducted for each set of proteins.

z-statistic file

Please supply z-statistic file. Example file: AssClinical_example_z_statistic_table.txt

Sample cluster file

It is a two column, comma-delimited file with the first line identifying the columns. Example file: AssClinical_example_sample_cluster.txt

Clinical information file

It is a tab-delimited file with clinical features in columns and samples in rows.Example file: AssClinical_example_clinical_info.txt

Clinical features(discrete variable name)(For example, Histology,Degree,Stage,Lymph_node,Abnormal_SCC)

Clinical features(continuous variable name)(For example, Tumor_size_cm,Age)

Color file

Tab-delimited file without column names. The first column represents discrete features, and the second column is the hexadecimal color code corresponding to the feature. Example file: AssClinical_example_discrete_color.txt

Colorbar file

Tab-delimited file without column names. The first column represents continous features, and the second column is the colormaps in Matplotlib for each feature. Example file: AssClinical_example_continuous_colorbar.txt

FDR(BH-adjusted p-value cutoff for ANOVA)

Protein clusters