
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] |
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Organizations: |
Delft University of Technology, Delft Bioinformatics Lab, Delft, Zuid Holland (Netherlands)
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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 |