Systematic Design, Generation, and Application of Synthetic Datasets for Flow Cytometry

References

1. 1.↵
   1. Phillips W.,
   2. Medcalf N.,
   3. Dalgarno K.,
   4. Makatoris H.,
   5. Sharples S.,
   6. Srai J.,
   7. Hourd P.,
   8. Kapletia D.

   Redistributed Manufacturing in Healthcare: Creating New Value through Disruptive Innovation; 2018. White Paper, UK EPSRC Redistributed Manufacturing in Healthcare Network. https://uwerepository.worktribe.com/output/999236 (accessed November 5, 2020)
2. 2.↵
   1. Pedregosa F.,
   2. Varoquaux G.,
   3. Gramfort A.,
   4. Michel V.,
   5. Thirion B.,
   6. Grisel O.,
   7. Blondel M.,
   8. Prettenhofer P.,
   9. Weiss R.,
   10. Dubourg V.,
   11. Vanderplas J.,
   12. Passos A.,
   13. Cournapeau D.,
   14. Brucher M.,
   15. Perrot M.,
   16. Duchesnay É.

   Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res 2011, 12 (85), 2825–2830.

   OpenUrlCrossRefPubMed
3. 3.↵
   1. Ros G.,
   2. Sellart L.,
   3. Materzynska J.,
   4. Vazquez D.,
   5. Lopez A. M.

   The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016, 3234–3243.
4. 4.↵
   1. Wrenninge M.,
   2. Unger J.

   Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing. 2018. https://arxiv.org/abs/1810.08705 (accessed Nov 20, 2020)
5. 5.↵
   1. Tremblay J.,
   2. To T.,
   3. Birchfield S.

   Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2018, 2038–2041.
6. 6.↵
   1. Hagiwara A.,
   2. Warntjes M.,
   3. Hori M.,
   4. Andica C.,
   5. Nakazawa M.,
   6. Kumamaru K. K.,
   7. Abe O.,
   8. Aoki S.

   SyMRI of the Brain: Rapid Quantification of Relaxation Rates and Proton Density, with Synthetic MRI, Automatic Brain Segmentation, and Myelin Measurement. Invest. Radiol. 2017, 52 (10), 647–657.

   OpenUrl
7. 7.↵
   1. Ratanaprasatporn L.,
   2. Chikarmane S. A.,
   3. Giess C. S.

   Strengths and Weaknesses of Synthetic Mammography in Screening. RadioGraphics 2017, 37 (7), 1913–1927.

   OpenUrl
8. 8.↵
   1. Niazi M. K. K.,
   2. Parwani A. V.,
   3. Gurcan M. N.

   Digital Pathology and Artificial Intelligence. Lancet Oncol. 2019, 20 (5), e253–e261.

   OpenUrlCrossRefPubMed
9. 9.↵
   1. Perez L.,
   2. Wang J.

   The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. 2017. https://arxiv.org/abs/1712.04621 (accessed Jan 5, 2021)
10. 10.↵
    1. Arvaniti E.,
    2. Claassen M.

    Sensitive Detection of Rare Disease-Associated Cell Subsets via Representation Learning. Nat. Commun. 2017, 8, (1), 14825.

    OpenUrlCrossRef
11. 11.↵
    The European Parliament and the Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC. Off. J. Eur. Union 2016, 1–88.
12. 12.↵
    1. Sugar I. P.,
    2. Sealfon S. C.

    Misty Mountain Clustering: Application to Fast Unsupervised Flow Cytometry Gating. BMC Bioinformatics 2010, 11 (1), 502.

    OpenUrlCrossRefPubMed
13. 13.↵
    1. Samusik N.,
    2. Good Z.,
    3. Spitzer M. H.,
    4. Davis K. L.,
    5. Nolan G. P.

    Automated Mapping of Phenotype Space with Single-Cell Data. Nat. Methods 2016, 13 (6), 493–496.

    OpenUrl
14. 14.↵
    1. Naim I.,
    2. Datta S.,
    3. Rebhahn J.,
    4. Cavenaugh J. S.,
    5. Mosmann T. R.,
    6. Sharma G.

    SWIFT-Scalable Clustering for Automated Identification of Rare Cell Populations in Large, High-Dimensional Flow. Cytometry 2014, 85 (5), 408–421.

    OpenUrl
15. 15.↵
    1. Pyne S.,
    2. Hu X.,
    3. Wang K.,
    4. Rossin E.,
    5. Lin T.-I.,
    6. Maier L. M.,
    7. Baecher-Allan C.,
    8. McLachlan G. J.,
    9. Tamayo P.,
    10. Hafler D. A.,
    11. De Jager P. L.,
    12. Mesirov J. P.

    Automated High-Dimensional Flow Cytometric Data Analysis. Proc. Natl. Acad. Sci. U.S.A. 2009, 106 (21), 8519–8524.

    OpenUrlAbstract/FREE Full Text
16. 16.↵
    1. Ge Y.,
    2. Sealfon S. C.

    FlowPeaks: A Fast Unsupervised Clustering for Flow Cytometry Data via K-Means and Density Peak Finding. Bioinformatics 2012, 28 (15), 2052–2058.

    OpenUrlCrossRefPubMedWeb of Science
17. 17.↵
    1. Zare H.,
    2. Shooshtari P.,
    3. Gupta A.,
    4. Brinkman R. R.

    Data Reduction for Spectral Clustering to Analyze High Throughput Flow Cytometry Data. BMC Bioinformatics 2010, 11 (1), 403.

    OpenUrlCrossRefPubMed
18. 18.↵
    1. Bendall S. C.,
    2. Davis K. L.,
    3. Amir E. A. D.,
    4. Tadmor M. D.,
    5. Simonds E. F.,
    6. Chen T. J.,
    7. Shenfeld D. K.,
    8. Nolan G. P.,
    9. Pe’Er D.

    Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development. Cell 2014, 157 (3), 714–725.

    OpenUrlCrossRefPubMedWeb of Science
19. 19.↵
    1. Bigos M.

    Separation Index: An Easy-to-Use Metric for Evaluation of Different Configurations on the Same Flow Cytometer. Curr. Protoc. Cytom. 2007, 40 (1), 1–21.

    OpenUrl
20. 20.↵
    1. Telford W. G.,
    2. Babin S. A.,
    3. Khorev S. V.,
    4. Rowe S. H.

    Green Fiber Lasers: An Alternative to Traditional DPSS Green Lasers for Flow Cytometry. Cytometry 2009, 75A (12), 1031–1039.

    OpenUrl
21. 21.↵
    1. Aghaeepour N.,
    2. Finak G.,
    3. Hoos H.,
    4. Mosmann T. R.,
    5. Brinkman R.,
    6. Gottardo R.,
    7. Scheuermann R. H.,
    8. Gottardo R.,
    9. Scheuermann R. H

    , The FlowCAP Consortium. Critical Assessment of Automated Flow Cytometry Data Analysis Techniques. Nat. Methods 2013, 10 (3), 228–238.

    OpenUrlCrossRefPubMedWeb of Science
22. 22.↵
    1. Weber L. M.,
    2. Robinson M. D.

    Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data. Cytometry 2016, 89 (12), 1084–1096. https://doi.org/10.1002/cyto.a.23030.

    OpenUrl
23. 23.↵
    1. Spidlen J.,
    2. Breuer K.,
    3. Rosenberg C.,
    4. Kotecha N.,
    5. Brinkman R. R.

    FlowRepository: A Resource of Annotated Flow Cytometry Datasets Associated with Peer-Reviewed Publications. Cytometry 2012, 81A (9), 727–731.

    OpenUrl
24. 24.↵
    International Organization for Standardization. ISO 13528:2005 Statistical Methods for Use in Proficiency Testing by Interlaboratory Comparison. ISO: Geneva, 2005.
25. 25.↵
    1. Wang L.,
    2. Abbasi F.,
    3. Ornatsky O.,
    4. Cole K. D.,
    5. Misakian M.,
    6. Gaigalas A. K.,
    7. He H.-J.,
    8. Marti G. E.,
    9. Tanner S.,
    10. Stebbings R.

    Stebbings, R. Human CD4 + Lymphocytes for Antigen Quantification: Characterization Using Conventional Flow Cytometry and Mass. Cytometry 2012, 81A (7), 567–575.

    OpenUrlPubMed
26. 26.↵
    1. Cheung M.,
    2. Campbell J. J.,
    3. Whitby L.,
    4. Thomas R. J.,
    5. Braybrook J.,
    6. Petzing J. N.

    Current Trends in Flow Cytometry Automated Data Analysis Software. Cytometry 2021, 99 (10), 1007–1021.

    OpenUrl
27. 27.↵
    1. Qiu P.

    Toward Deterministic and Semiautomated SPADE Analysis. Cytometry 2017, 91 (3), 281–289.

    OpenUrl
28. 28.↵
    1. Qiu W.,
    2. Joe H.

    Separation Index and Partial Membership for Clustering. Comput. Stat. Data Anal. 2006, 50 (3), 585–603.

    OpenUrl
29. 29.↵
    1. Azzalini A.,
    2. Capitanio A.

    Statistical Applications of the Multivariate Skew Normal Distribution. J. Royal Statistical Soc. B. 1999, 61 (3), 579–602.

    OpenUrl
30. 30.↵
    1. Fleiss J. L.,
    2. Levin B.,
    3. Paik M. C.

    Statistical Methods for Rates and Proportions, 3rd ed.; John Wiley & Sons: Hoboken, NJ, 2003.
31. 31.↵
    1. Tharwat A.

    Classification Assessment Methods. Appl. Comput. Inf. 2021, 17 (1), 168–192.

    OpenUrl
32. 32.↵
    1. Burel J. G.,
    2. Qian Y.,
    3. Lindestam Arlehamn C.,
    4. Weiskopf D.,
    5. Zapardiel-Gonzalo J.,
    6. Taplitz R.,
    7. Gilman R. H.,
    8. Saito M.,
    9. de Silva A. D.,
    10. Vijayanand P.,
    11. Scheuermann R. H.,
    12. Sette A.,
    13. Peters B.

    An Integrated Workflow to Assess Technical and Biological Variability of Cell Population Frequencies in Human Peripheral Blood by Flow Cytometry. J. Immunol. 2017, 198 (4), 1748–1758.

    OpenUrlAbstract/FREE Full Text
33. 33.↵
    1. Salati S.,
    2. Zini R.,
    3. Bianchi E.,
    4. Testa A.,
    5. Mavilio F.,
    6. Manfredini R.,
    7. Ferrari S.

    Role of CD34 Antigen in Myeloid Differentiation of Human Hematopoietic Progenitor Cells. Stem Cells 2008, 26 (4), 950–959.

    OpenUrlCrossRefPubMed
34. 34.↵
    1. Joanes D. N.,
    2. Gill C. A.

    Comparing Measures of Sample Skewness and Kurtosis. J. Royal Statistical Soc. D. 1998, 47 (1), 183–189.

    OpenUrl
35. 35.↵
    1. Bruggner R.V.,
    2. Linderman M. D.,
    3. Sachs K.,
    4. Nolan G. P.,
    5. Plevritis S. K.

    1. Qiu P.,
    2. Simonds E. F.,
    3. Bendall S. C.,
    4. Gibbs K. D.

    , Jr.; Bruggner R.V., Linderman M. D., Sachs K., Nolan G. P., Plevritis S. K. Extracting a Cellular Hierarchy from High-Dimensional Cytometry Data with SPADE. Nat. Biotechnol. 2011, 29 (10), 886–893.

    OpenUrlCrossRefPubMed
36. 36.↵
    1. Grant R.,
    2. Coopman K.,
    3. Medcalf N.,
    4. Silva-Gomes S.,
    5. Campbell J. J.,
    6. Kara B.,
    7. Braybrook J.,
    8. Petzing J.

    Understanding the Contribution of Operator Measurement Variability within Flow Cytometry Data Analysis for Quality Control of Cell and Gene Therapy Manufacturing. Measurement 2020, 150, 106998.

    OpenUrl
37. 37.↵
    1. Grant R.,
    2. Coopman K.,
    3. Medcalf N.,
    4. Silva-Gomes S.,
    5. Campbell J. J.,
    6. Kara B.,
    7. Braybrook J.,
    8. Petzing J. N.

    Quantifying Operator Subjectivity within Flow Cytometry Data Analysis as a Source of Measurement Uncertainty and the Impact of Experience on Results. PDA J. Pharm. Sci. Technol. 2021, 75 (1), 33–47.

    OpenUrlAbstract/FREE Full Text