Flow cytometry bioinformatics

Abstract

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

Figure 1. Schematic diagram of a flow cytometer, showing focusing of the fluid sheath, laser, optics (in simplified form, omitting focusing), photomultiplier tubes (PMTs), analogue-to-digital converter, and analysis workstation.

Figure 2. An example pipeline for analysis of FCM data and some of the Bioconductor packages relevant to each step.

Figure 3. Comparison of consensus of eight independent manual gates (polygons) and automated gates (colored dots).

The consensus of the manual gates and the algorithms were produced using the CLUE package . Figure reproduced with permission from .

Figure 4. Cell populations in a high-dimensional mass-cytometry dataset manually gated after dimension reduction using 2-D layout for a minimum spanning tree.

Figure reproduced from the data provided in .

Figure 5. An example of probability binning, created using the flowFP Bioconductor package.

The dots represent individual events in an FCS file. The rectangles represent the bins.

Figure 6. Overview of the flowType/RchyOptimyx pipeline for identification of correlates of protection against HIV.

First, tens of thousands of cell populations are identified by combining one-dimensional partitions (panel 1). The cell populations are then analyzed using a statistical test (and Bonferroni's method for multiple testing correction) to identify those correlated with the survival information. Panel 3 shows a complete gating hierarchy describing all possible strategies for gating that cell population. This graph can be mined to identify the “best” gating strategy (i.e., the one in which the most important markers appear earlier). These hierarchies for all selected phenotypes are demonstrated in panel 4. In panel 5, these hierarchies are merged into a single graph that summarizes the entire dataset and demonstrates the trade-off between the number of markers involved in each phenotype and the significance of the correlation with the clinical outcome (e.g., as measured by the KaplanMeier estimator in panel 6). Figure reproduced in part from (public domain) and .

Figure 7. Representation of flow cytometry data from an instrument with three scatter channels and 13 fluorescent channels.

Only the values for the first 30 (of hundreds of thousands) of cells are shown.