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Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data
Kieran O'Neill1, Adrin Jalali, Nima Aghaeepour
1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
We enhanced the flowType and RchyOptimyx pipeline for faster single-cell data analysis. This improved tool supports multi-level biomarker analysis for high-throughput experiments, including flow cytometry and mass cytometry.
Area of Science:
- Computational Biology
- Bioinformatics
Background:
- High-throughput single-cell assays generate complex, high-dimensional data.
- Existing analysis pipelines may lack efficiency and scalability for large datasets.
Purpose of the Study:
- To present an optimized version of the flowType and RchyOptimyx BioConductor pipeline.
- To enhance computational efficiency and analytical capability for single-cell data.
Main Methods:
- Significant improvements to the flowType and RchyOptimyx pipeline.
- Implementation of multi-level biomarker expression analysis.
- Benchmarking for speed and scalability.
Main Results:
- The improved pipeline is 14 times faster than previous versions.
- Accommodates multi-level biomarker expression for up to 96 markers.
- Demonstrates suitability for high-dimensional single-cell data analysis.
Conclusions:
- The enhanced pipeline offers a significant speedup for single-cell data analysis.
- It is well-suited for analyzing data from flow cytometry, mass cytometry, and single-cell RT-qPCR.
- Poised to become integral for high-throughput biological experiments.
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