Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Automated summary

In this article, the authors introduce a toolkit called compaRe for large-scale multiparameter data analysis. The toolkit integrates quality control, data bias correction, and data visualization methods, and provides faster and more reliable analysis results through a mass-aware gridding algorithm-based similarity analysis. The results show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles, as well as identify multiple types of phenotypic response patterns in drug response data.