PeacoQC: Peak-based selection of high quality cytometry data.

ABSTRACT: In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree an

SUBMITTER: Emmaneel A 

PROVIDER: S-EPMC9293479 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature