Dataset Information

A computational framework to emulate the human perspective in flow cytometric data analysis.

ABSTRACT:

Background

In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.

Results

To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.

Conclusions

The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.

SUBMITTER: Ray S

PROVIDER: S-EPMC3341382 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

A computational framework to emulate the human perspective in flow cytometric data analysis.

Ray Surajit S Pyne Saumyadipta S

PloS one 20120501 5

<h4>Background</h4>In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not ...[more]

PMID: 22563466

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Project description:BACKGROUND: A useful application of flow cytometry is the investigation of cell receptor-ligand interactions. However such analyses are often compromised due to problems interpreting changes in ligand binding where the receptor expression is not constant. Commonly, problems are encountered due to cell treatments resulting in altered receptor expression levels, or when cell lines expressing a transfected receptor with variable expression are being compared. To overcome this limitation we have developed a Microsoft Excel spreadsheet that aims to automatically and effectively simplify flow cytometric data and perform statistical tests in order to provide a clearer graphical representation of results. RESULTS: To demonstrate the use and advantages of this new spreadsheet method we have investigated the binding of the transmembrane adhesion receptor CD44 to its ligand hyaluronan. In the first example, phorbol ester treatment of cells results in both increased CD44 expression and increased hyaluronan binding. By applying the spreadsheet method we effectively demonstrate that this increased ligand binding results from receptor activation. In the second example we have compared AKR1 cells transfected either with wild type CD44 (WT CD44) or a mutant with a truncated cytoplasmic domain (CD44-T). These two populations do not have equivalent receptor expression levels but by using the spreadsheet method hyaluronan binding could be compared without the need to generate single cell clones or FACS sorting the cells for matching CD44 expression. By this method it was demonstrated that hyaluronan binding requires a threshold expression of CD44 and that this threshold is higher for CD44-T. However, at high CD44-T expression, binding was equivalent to WT CD44 indicating that the cytoplasmic domain has a role in presenting the receptor at the cell surface in a form required for efficient hyaluronan binding rather than modulating receptor activity. CONCLUSION: Using the attached spreadsheets and instructions, a simple post-acquisition method for analysing bivariate flow cytometry data is provided. This method constitutes a straightforward improvement over the standard graphical output of flow cytometric data and has the significant advantage that ligand binding can be compared between cell populations irrespective of receptor expression levels.

Dataset Information

A computational framework to emulate the human perspective in flow cytometric data analysis.

Background

Results

Conclusions

Publications

A computational framework to emulate the human perspective in flow cytometric data analysis.

Similar Datasets

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