Notice: Temporary Suspension of New Experiment Creation
We have temporarily disabled the creation of new experiments as we are continuously running out of space. This issue has been impacting both uploads and downloads from FlowRepository. By taking this step, we aim to make downloads of existing data more reliable.
We apologize for the inconvenience and appreciate your understanding as we work on upgrading our hardware and improving the overall solution.
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Experiment Overview

Repository ID: FR-FCM-ZYTT Experiment name: Predicting cell populations in single cell mass cytometry data MIFlowCyt score: 27.00%
Primary researcher: Tamim Abdelaal PI/manager: Tamim Abdelaal Uploaded by: Tamim Abdelaal
Experiment dates: 2017-10-02 - Dataset uploaded: Dec 2018 Last updated: Dec 2018
Keywords: [mass cytometry] [machine learning] [single-cell] [Cell population prediction] Manuscripts: [30861637] Cytalogo
Organizations: Delft University of Technology, Delft Bioinformatics Lab, Delft, Zuid Holland (Netherlands)
Purpose: Automatic prediction of cell populations in mass cytometry data, using supervised learning algorithms
Conclusion: Linear Discriminant Analysis classifier can accurately predict abundant and rare cell populations, and outperforms semi-supervised and deep learning methods.
Comments: All data files are uploaded in CSV format.
Funding: European Commission of a H2020 MSCA award under proposal number 675743 (ISPIC)
Quality control: None
Download FCS Files or login and see the dataset in your inbox for further annotation details.