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Managing Multi-center Flow Cytometry Data for Immune Monitoring.

Publication ,  Journal Article
White, S; Laske, K; Welters, MJ; Bidmon, N; van der Burg, SH; Britten, CM; Enzor, J; Staats, J; Weinhold, KJ; Gouttefangeas, C; Chan, C
Published in: Cancer Inform
2014

With the recent results of promising cancer vaccines and immunotherapy1-5, immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization21-23, as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.

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Published In

Cancer Inform

DOI

ISSN

1176-9351

Publication Date

2014

Volume

13

Issue

Suppl 7

Start / End Page

111 / 122

Location

United States

Related Subject Headings

  • Bioinformatics
  • 4003 Biomedical engineering
  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
Chicago
ICMJE
MLA
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White, S., Laske, K., Welters, M. J., Bidmon, N., van der Burg, S. H., Britten, C. M., … Chan, C. (2014). Managing Multi-center Flow Cytometry Data for Immune Monitoring. Cancer Inform, 13(Suppl 7), 111–122. https://doi.org/10.4137/CIN.S16346
White, Scott, Karoline Laske, Marij Jp Welters, Nicole Bidmon, Sjoerd H. van der Burg, Cedrik M. Britten, Jennifer Enzor, et al. “Managing Multi-center Flow Cytometry Data for Immune Monitoring.Cancer Inform 13, no. Suppl 7 (2014): 111–22. https://doi.org/10.4137/CIN.S16346.
White S, Laske K, Welters MJ, Bidmon N, van der Burg SH, Britten CM, et al. Managing Multi-center Flow Cytometry Data for Immune Monitoring. Cancer Inform. 2014;13(Suppl 7):111–22.
White, Scott, et al. “Managing Multi-center Flow Cytometry Data for Immune Monitoring.Cancer Inform, vol. 13, no. Suppl 7, 2014, pp. 111–22. Pubmed, doi:10.4137/CIN.S16346.
White S, Laske K, Welters MJ, Bidmon N, van der Burg SH, Britten CM, Enzor J, Staats J, Weinhold KJ, Gouttefangeas C, Chan C. Managing Multi-center Flow Cytometry Data for Immune Monitoring. Cancer Inform. 2014;13(Suppl 7):111–122.

Published In

Cancer Inform

DOI

ISSN

1176-9351

Publication Date

2014

Volume

13

Issue

Suppl 7

Start / End Page

111 / 122

Location

United States

Related Subject Headings

  • Bioinformatics
  • 4003 Biomedical engineering
  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis