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High-dimensional immune monitoring for chimeric antigen receptor T cell therapies

Pruteanu-Malinici, Iulian and Sharma, Sujata and Quinn, David and Melenhorst, Jos (2020) High-dimensional immune monitoring for chimeric antigen receptor T cell therapies. Springer-Nature's Current Hematologic Malignancy Reports. ISSN Current Hematologic Malignancy Reports

Abstract

Purpose of Review: high-dimensional flow cytometry experiments have become a method of
choice for high-throughput integration and characterization of cell populations. Here, we
present a summary of state-of-the-art R-based pipelines used for differential analyses of
cytometry data, largely based on Chimeric Antigen Receptor (CAR) T cell therapies.

Recent Findings: in recent years, existing tools tailored to analyze complex high-dimensional
data such as single cell RNA sequencing (scRNAseq) have been successfully ported to
cytometry studies due to the similar nature of flow cytometry and scRNAseq platforms.
Existing environments like Cytobank24, FlowJo27 and FCS Express 28 already offer a variety of
these ported tools, but they either come at a premium or are fairly complicated to manage by an
inexperienced user. To mitigate these limitations, experienced cytometrists and
bioinformaticians usually incorporate these functions into an R-Shiny 17 application that
ultimately offers a user-friendly, intuitive environment that can be used to analyze flow
cytometry data.

Summary: computational tools and Shiny based tools are the perfect answer to the ever-growing
dimensionality and complexity of flow cytometry data, by offering a dynamic, yet user friendly
exploratory space, tailored to bridge the space between the lab experimental world and the
computational, machine learning space.

Item Type: Article
Keywords: clustering, annotation, K-means, PCA, UMAP, tSNE
Date Deposited: 30 Jan 2021 00:45
Last Modified: 30 Jan 2021 00:45
URI: https://oak.novartis.com/id/eprint/43560

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