Training Novices in Generation and Analysis of High‐Dimensional Human Cell Phospho‐Flow Cytometry Data

Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher’s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

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Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding

Cytometry Part A ◽

10.1002/cyto.a.23566 ◽

2018 ◽

Vol 93 (8) ◽

pp. 785-792 ◽

Cited By ~ 10

Author(s):

Emilia Maria Cristina Mazza ◽

Jolanda Brummelman ◽

Giorgia Alvisi ◽

Alessandra Roberto ◽

Federica De Paoli ◽

...

Keyword(s):

Flow Cytometry ◽

Data Visualization ◽

High Dimensional ◽

Flow Cytometry Data ◽

Background Fluorescence ◽

Dimensional Flow

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DAFi: A Directed Recursive Filtering and Clustering Approach to Data-Driven Identification of Cell Populations from Polychromatic Flow Cytometry Data

10.1101/193912 ◽

2017 ◽

Author(s):

Alexandra J. Lee ◽

Ivan Chang ◽

Julie G. Burel ◽

Cecilia S. Lindestam Arlehamn ◽

Daniela Weiskopf ◽

...

Keyword(s):

Flow Cytometry ◽

Data Clustering ◽

Data Driven ◽

High Dimensional ◽

Cell Populations ◽

Recursive Filtering ◽

Flow Cytometry Data ◽

Data Clusters ◽

Polychromatic Flow Cytometry ◽

Recursive Data

AbstractComputational methods for identification of cell populations from high-dimensional flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. We found that combining recursive filtering and clustering with constraints converted from the user manual gating strategy can effectively identify overlapping and rare cell populations from smeared data that would have been difficult to resolve by either a single run of data clustering or manual segregation. We named this new method DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell-based biomarkers, but also makes the results interpretable to experimental scientists as in supervised classification through mapping and merging the high-dimensional data clusters into the user-defined 2D gating hierarchy. By recursive data filtering before clustering, DAFi can uncover small local clusters which are otherwise difficult to identify due to the statistical interference of the irrelevant major clusters. Quantitative assessment of cell type specific characteristics demonstrates that the population proportions calculated by DAFi, while being highly consistent with those by expert centralized manual gating, have smaller technical variance than those from individual manual gating analysis. Visual examination of the dot plots showed that the boundaries of the DAFi-identified cell populations followed the natural shapes of the data distributions. To further exemplify the utility of DAFi, we show that DAFi can incorporate the FLOCK clustering method to identify novel cell-based biomarkers. Implementation of DAFi supports options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

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How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Frontiers in Immunology ◽

10.3389/fimmu.2021.768113 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hannah den Braanker ◽

Margot Bongenaar ◽

Erik Lubberts

Keyword(s):

Flow Cytometry ◽

Dimensional Analysis ◽

High Dimensional ◽

Spectral Flow ◽

Control Data ◽

Single Experiment ◽

Flow Cytometry Data ◽

Healthy Control ◽

The Common ◽

Quality Control Data

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

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