Abstract: The multi-parameter data produced via flow cytometry and other biological analyses techniques can generate enormous amounts of data, which can take extensive time and/or computational resources to complete. Embodiments provided herein allow for adaptive sub-sampling of such data prior to analysis, allowing for such analyses to be performed while satisfying certain performance criteria. Such performance criteria may include, for example, keeping the latency of the analysis below a specified duration. This can allow analysis of data to be performed in real time as the data is generated, e.g., as flow cytometry data is generated by a cell counter or other flow cytometry instrument. This can also permit for data analyses to be iteratively developed or improved in less time by adaptively sub-sampling the data prior to re-analysis, so that the total time between iterations is reduced.

Abstract: The multi-parameter data produced via flow cytometry and other biological analyses techniques can generate enormous amounts of data, which can take extensive time and/or computational resources to complete. Embodiments provided herein allow for adaptive sub-sampling of such data prior to analysis, allowing for such analyses to be performed while satisfying certain performance criteria. Such performance criteria may include, for example, keeping the latency of the analysis below a specified duration. This can allow analysis of data to be performed in real time as the data is generated, e.g., as flow cytometry data is generated by a cell counter or other flow cytometry instrument. This can also permit for data analyses to be iteratively developed or improved in less time by adaptively sub-sampling the data prior to re-analysis, so that the total time between iterations is reduced.