flowAI: automatic and interactive anomaly discerning tools for flow cytometry data

Bioinformatics. 2016 Aug 15;32(16):2473-80. doi: 10.1093/bioinformatics/btw191. Epub 2016 Apr 10.


Motivation: Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.

Results: We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.

Availability and implementation: R source code available through Bioconductor: http://bioconductor.org/packages/flowAI/ CONTACTS: mongianni1@gmail.com or Anis_Larbi@immunol.a-star.edu.sg

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Algorithms*
  • Cluster Analysis
  • Data Interpretation, Statistical
  • Flow Cytometry*
  • Humans
  • Software*