flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification

Malek, Mehrnoush; Taghiyar, Mohammad Jafar; Chong, Lauren C.; Finak, Greg; Gottardo, Raphaël; Brinkman, Ryan R.

doi:10.1093/bioinformatics/btu677

Cited by 120 publications

(135 citation statements)

References 5 publications

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“…In such a case, it is nevertheless advisable to randomly select some samples and manually check for artefacts to get an idea of the specific dataset characteristics, noise and artefacts, as quality control is even more important for subsequent automatic analysis than for manual analysis. Following quality control, samples will be further pre-processed by removing debris, dead cells, doublets, either manually or automatically (using for example the openCyto 37 or flowDensity 38 packages to approximate a manual gating). Also boundary effects (data points at the margin of the data range) should be removed, and possibly a pre-gating step on a specific population of interest for further ana lysis can be performed (for example, using the flowQ 34 and flowStats 36 packages).…”

Section: Algorithmic Benchmarking and Software Availabilitymentioning

confidence: 99%

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Computational flow cytometry: helping to make sense of high-dimensional immunology data

2016

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Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

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Section: Algorithmic Benchmarking and Software Availabilitymentioning

confidence: 99%

“…This may result in negative populations being split further into negative and very negative populations owing purely to data spread. Methods based on one-class classifiers have been explored to include FMOs as reference controls 70 , and both the flowDensity 38 and openCyto 37 frameworks allow dealing with FMO controls.…”

Section: Automated Gating Techniquesmentioning

confidence: 99%

Computational flow cytometry: helping to make sense of high-dimensional immunology data

2016

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“…Spectral clustering [33] and density-based cell population identification [34] for the analysis of FACS data has been proposed. For CyTOF data, subpopulations have been identified with a regularized regression-based method [35] and a graph-based method with community detection to maximize “modularity” [36] .…”

Section: Correlating the Data Points: Computational Data Analysis Of mentioning

confidence: 99%

Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots

Marr

Zhou

Huang

2016

Current Opinion in Biotechnology

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Single-cell analyses of transcript and protein expression profiles – more precisely, single-cell resolution analysis of molecular profiles of cell populations – have now entered center stage with the wide application of single-cell qPCR, single-cell RNA-Seq and CytOF. These high-dimensional population snapshots techniques are complemented by low-dimensional time-resolved microscopy-based monitoring methods of individual cells. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has stimulated a series of computational analysis methods for data visualization, dimensionality reduction, and “cluster” (subpopulation) identification. The next step is to go beyond collecting data and correlating data points with computational analyses: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the “clouds of points”, each representing a cell in state space, and their structure as manifestation of the regulation by the molecular network. This control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize not only key methods of data acquisition and computational analysis but also explain the principles that link the single-cell resolution measurements to dynamical systems theory.

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“…We used an automatic gating step to select only the alive T-cells for further analysis. To do this, we used the R flowDensity package (20). This package can automatically determine an optimal split in a single dimension of the dataset.…”

Section: Preprocessingmentioning

confidence: 99%

“…First, we used the flowDensity algorithm (20) again to determine splits on ten dimensions: FSC-A, SSC-A, G710-A (CD4), G660-A (CD27), G610-A (CD107-A), G560-A, (CD154), R710-A (CCR7), V800-A (CD8), V585-A (CD57), V545-A (CD45RO). This algorithm uses the density distribution of the cells to determine the best possible split.…”

Section: Feature Extractionmentioning

confidence: 99%

FloReMi: Flow density survival regression using minimal feature redundancy

et al. 2015

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Advances in flow cytometry bioinformatics have resulted in a wide variety of clustering, classification and visualization techniques. To objectively evaluate the performance of such methods, common benchmarks such as the FlowCAP initiative have proven to be of great value. In this work, we report on a novel method, FloReMi, which was developed to tackle the most recent FlowCAP IV challenge. This challenge was formulated as a survival modeling problem, where participants were expected to design a model to predict the time until progression to AIDS for HIV patients. It is known that variability in progression rate cannot be fully predicted by simple CD4 1 T cell counts. However, it is hypothesized that the immunopathogenesis established early in HIV already indicates the course of future disease. Adequately estimating the progression rate of HIV patients is crucial in their treatment. Using an automated pipeline to preprocess the data, and subsequently identify and select informative cell subsets, a survival regression method based on random survival forests was built, which obtained the best results of all submitted approaches to the FlowCAP IV challenge. V C 2015 International Society for Advancement of Cytometry

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flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 120 publications

References 5 publications

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots

FloReMi: Flow density survival regression using minimal feature redundancy

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