Abstract

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW is equipped with modules to remove various sources of bias. Benchmarking tests show that O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.