Integrative clustering analysis
We applied deep molecular and cellular phenotyping (CyTOF, Olink proteomics, BMMC and MM RNA sequencing) to interrogate the TME. SNF was used to integrate these four distinct data types collected at baseline from 20 patients. SNF analyses multiple layers of data to gain a more holistic understanding, capturing shared and complementary information to distinguish true biological signals from noise by weighing down the weak data layer-specific information captured by single-layer analysis. This enables more accurate molecular classification with a significantly lower sample size requirement than single -omics methods (Unsupervised integrative clustering analysis at baseline schema 
The SNF derived groups were compared with the CyTOF derived group with regards to association with overall survival and duration of response (DOR) (The unsupervised SNF algorithm was applied to data collected on-treatment from 15 patients, fusing the patient similarity networks obtained from CyTOF, CD138-negative mRNA and CD138-positive mRNA features (Integrative analysis of on-treatment samples schema 