ReviewCurrent hematologic malignancy reports2021
High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies.
Review in Current hematologic malignancy reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewHigh-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. These pipelines are based on publicly available R libraries, put together in a systematic and functional fashion, therefore free of cost. RECENT
findingsIn 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 Cytobank (Kotecha et al., 2010), FlowJo (FlowJo™ Software) and FCS Express (https://denovosoftware.com) 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 RShiny (https://shiny.rstudio.com) application that ultimately offers a user-friendly, intuitive environment that can be used to analyze flow cytometry data. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.