Fig. 1

Proteomic data processing and bioinformatic analysis. Flowchart illustrating proteomic data handling and processing. Each enclosed box indicates specific data acquisition and processing approaches used to determine patient stratification and clustering as a function of the urine proteome. Box 1) shows data acquisition by mass spectrometry and initial data handling post proteome search by MaxQuant. High stringency identifications below 1% false discovery rate (FDR < 1%) are the initial list cut parameter followed by removal of single peptide identifications, thus increasing stringency. Box 2) shows data filtering and missing value imputations calculated in Metaboanalyst from the comma separated value (CSV) converted files from the initial proteome identification lists and removal of any patients deemed as CKD5 to diminish data skewing. Box 3) shows the re-clustering approach from initial groups of patients based on UPCR > 2 that would normally be grouped together based on clinical data (UPCR, GFR etc.) K-means clustering was used to identify patients with UPCR > 2 and determine if sub-groups existed based on urine proteome differences. Box 4) candidate selection by supervised multivariate analysis of patient clusters to determine proteins with the most influence and drivers of group separation. Box 5) shows pathways and ontological network analyses to define changes in patient groups.