ArticleSource code for biology and medicine2013
GenePattern flow cytometry suite.
Article in Source code for biology and medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- High-throughput imaging of mRNA at the single-cell level in human primary immune cells.RNA (New York, N.Y.) · 2022Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies.European journal of immunology · 2017Article
- flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples.Frontiers in oncology · 2016Article
- Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2015Article
- Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.Methods (San Diego, Calif.) · 2015Article
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Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundTraditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.
resultsIn order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.
conclusionsGenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.
Identifiers
What OpenQuestion holds
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.