title: Data quality assessment of ungated flow cytometry data in high throughput experiments.
abstract: BACKGROUND
  The recent development of semiautomated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data.
  
  
  METHODS
  We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized functions and methods in the Bioconductor package rflowcyt. We demonstrate the use of these approaches by investigating two independent sets of high throughput flow cytometry data.
  
  
  RESULTS
  We found that graphical representations can reveal substantial nonbiological differences in samples. Empirical Cumulative Distribution Function and summary scatterplots were especially useful in the rapid identification of problems not identified by manual review.
  
  
  CONCLUSIONS
  Graphical exploratory data analytic tools are quick and useful means of assessing data quality. We propose that the described visualizations should be used as quality assessment tools and where possible, be used for quality control.
authors: Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. nlemeur@fhcrc.org
authors: Nolwenn
authors: N
authors: Le Meur
authors: Anthony
authors: A
authors: Rossini
authors: Maura
authors: M
authors: Gasparetto
authors: Clay
authors: C
authors: Smith
authors: Ryan R
authors: RR
authors: Brinkman
authors: Robert
authors: R
authors: Gentleman
mesh_terms: D000465:Algorithms; D000911:Antibodies, Monoclonal / Q000494:pharmacology; D058846:Antibodies, Monoclonal, Murine-Derived; D015703:Antigens, CD / Q000032:analysis; D000970:Antineoplastic Agents / Q000494:pharmacology; D016477:Artifacts; D015415:Biomarkers / Q000032:analysis; D045744:Cell Line, Tumor; D002469:Cell Separation / Q000379:methods / Q000592:standards*; D002470:Cell Survival / Q000187:drug effects; D016000:Cluster Analysis; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D005434:Flow Cytometry / Q000379:methods / Q000592:standards*; D006086:Graft vs Host Disease / Q000175:diagnosis / Q000276:immunology; D006801:Humans; D008904:Miniaturization / Q000592:standards; D011786:Quality Control; D015203:Reproducibility of Results; D000069283:Rituximab; D012984:Software; D013997:Time Factors
chemical_list: D000911:Antibodies, Monoclonal; D058846:Antibodies, Monoclonal, Murine-Derived; D015703:Antigens, CD; D000970:Antineoplastic Agents; D015415:Biomarkers; D000069283:Rituximab