Data Cleaning Function
In advanced analysis, the quality of the target samples is extremely important, and correct results may not be obtained with samples containing noise, etc. Therefore, this software also includes a data cleaning function to remove noise and outliers. Spectral Flow Analysis supports the following data cleaning algorithms.
- flowAI
- PeacoQC
- flowCut
- flowClean
For details about the operation, see “Removing Events Containing Noise and Outliers (Data Cleaning Function).”
flowAI: G. Monaco, H. Chen, et al. "flowAI: automatic and interactive anomaly discerning tools for flow cytometry data," Bioinformatics 32, 16, 2473-2480, 2016.
PeacoQC: A. Emmaneel, K.Quintelier, et al. "PeacoQC: Peak‐based selection of high quality cytometry data." Cytometry Part A 101.4, 325-338, 2022.
flowCut: J. Meskas, D Yokosawa, et al. "flowCut: An R package for automated removal of outlier events and flagging of files based on time versus fluorescence analysis." Cytometry Part A 103.1, 71-81, 2023.
flowClean: K. Fletez‐Brant, J. Špidlen, et al. "flow C lean: Automated identification and removal of fluorescence anomalies in flow cytometry data." Cytometry Part A 89.5, 461-471, 2016.
