As a less stringent imputation task, we left out batches of individual values and evaluated our ability to impute them
As a less stringent imputation task, we left out batches of individual values and evaluated our ability to impute them. conserved regulatory mechanisms. Here, we report that coupled matrixCtensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV\ and SARS\CoV\2\infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen\binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data…