Per-channel basis normalization methods for flow cytometry data.
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ABSTRACT:
SUBMITTER: Hahne F
PROVIDER: S-EPMC3648208 | biostudies-literature | 2010 Feb
REPOSITORIES: biostudies-literature
Publications
Per-channel basis normalization methods for flow cytometry data.
Hahne Florian F Khodabakhshi Alireza Hadj AH Bashashati Ali A Wong Chao-Jen CJ Gascoyne Randy D RD Weng Andrew P AP Seyfert-Margolis Vicky V Bourcier Katarzyna K Asare Adam A Lumley Thomas T Gentleman Robert R Brinkman Ryan R RR
Cytometry. Part A : the journal of the International Society for Analytical Cytology 20100201 2
Between-sample variation in high-throughput flow cytometry data poses a significant challenge for analysis of large-scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical step before analysis, particularly in large-scale data sets from clinical trials, where group-specific ...[more]
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Project description:Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially if a template-gating approach is used without further correction to the gates. In this article, a computational framework is presented for normalizing the fluorescence intensity of multiple markers in specific cell populations across samples that is suitable for high-throughput processing of large clinical trial datasets. Previous approaches to normalization have been global and applied to all cells or data with debris removed. They provided no mechanism to handle specific cell subsets. This approach integrates tightly with the gating process so that normalization is performed during gating and is local to the specific cell subsets exhibiting variability. This improves peak alignment and the performance of the algorithm. The performance of this algorithm is demonstrated on two clinical trial datasets from the HIV Vaccine Trials Network (HVTN) and the Immune Tolerance Network (ITN). In the ITN data set we show that local normalization combined with template gating can account for sample-to-sample variability as effectively as manual gating. In the HVTN dataset, it is shown that local normalization mitigates false-positive vaccine response calls in an intracellular cytokine staining assay. In both datasets, local normalization performs better than global normalization. The normalization framework allows the use of template gates even in the presence of sample-to-sample staining variability, mitigates the subjectivity and bias of manual gating, and decreases the time necessary to analyze large datasets.
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Project description:The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the past two decades. However, the use of arbitrary units has limited the usefulness of these data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously, but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express green fluorescent protein (GFP) from a set of commonly used constitutive promoters. We then demonstrate the package's power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry or colony counting experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.
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