HVPs identification and clustering

The chi-square statistics derived by zMAP along with the associated numbers of degrees of freedom were summed across MS runs for each protein, giving rise to a p-value that assessed the overall expression variability of the protein. This functionality is designed to select hypervariable proteins (HVPs) from the output files of zMAP and cluster them to obtain multiple expression signatures. Then, pathway enrichment analysis was conducted on these signatures to reveal biological insights.

Chi-square and p-value file

Output file zmap_chi_square_pvalue.txt from zMAP. Example file: HVP_example_zmap_chi_square_pvalue.txt

z-statistic file

Output file z_statistic_table.txt from zMAP. Example file: HVP_example_z_statistic_table.txt

Sample information file

The sample information file is a three-column, tab-delimited file with the first row identifying the columns. The column names are Sample_id, Sample_condition, and MS_run. Example file: HVP_example_sample_info.txt

Number of HVP clusters

HVPs are hierarchically clustered into multiple clusters revealing diverse expression signatures across different sample conditions.

Minimum proteins within each cluster

If the number of proteins within certain clusters falls below the specified minimum, these clusters will be merged into a single cluster labeled as cluster_0. As a result, the final number of clusters may be fewer than what the user initially specified.

Number of top-ranked DEPs

Top-ranked proteins are selected for heatmap visualization.

Number of DEP clusters

Top-ranked DEPs are clustered into multiple clusters based on K-means.

FDR

(BH-adjusted pvalue cutoff for significance.)