Evidence map›Paper›PMID 25167361›Full record

ArticlePLoS computational biology2014

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.

Greg Finak, Jacob Frelinger, Wenxin Jiang, Evan W Newell, John Ramey, Mark M Davis, Spyros A Kalams, Stephen C De Rosa, Raphael Gottardo

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.

0numbers the graph read from it
0cells of the map it votes in
123citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

123 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Opsonization Improves Dual Proteomic Analysis ofJournal of proteome research · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Androgens inhibit protective CD8Nature communications · 2025
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

63 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Greg FinakVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Jacob FrelingerVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Wenxin JiangVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Evan W NewellAgency for Science Technology and Research, Singapore Immunology Network, Singapore.
John RameyVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Mark M DavisDepartment of Microbiology and Immunology, Stanford University, Stanford, California, United States of America; Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, California, United States of America; The Howard Hughes Medical Institute, Stanford University, Stanford, California, United States of America.
Spyros A KalamsInfectious Diseases Division, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
Stephen C De RosaVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America; Department of Laboratory Medicine, University of Washington, Seattle, Washington, United States of America.
Raphael GottardoVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America; Department of Statistics, University of Washington, Seattle, Washington, United States of America.

Funding

LC: HIV Vaccine Trials NetworkUM1AI068618 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Margaret Juliana McElrath · 2011 to 2026
$483.6M
SDMC: HIV Vaccine Trials NetworkUM1AI068635 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Peter B. Gilbert, Yunda Huang · 2011 to 2026
$385.9M
The Statistical and Computational Analysis of Flow Cytometry DataR01EB008400 · NIBIB · FRED HUTCHINSON CANCER RESEARCH CENTER · PI GOTTARDO, RAPHAEL · 2008 to 2015
$2.9M
Immune signatures of protection induced by whole parasite malaria vaccinesU19AI089986 · NIAID · SEATTLE BIOMEDICAL RESEARCH INSTITUTE · PI MCELRATH, MARGARET JULIANA, STUART, KENNETH D · 2010 to 2015
$2.3M
NIAID NIH HHS U19 AI089986NIAID NIH HHS UM1 AI068618NIAID NIH HHS UM1 AI068635NIBIB NIH HHS R01 EB008400
6 · The paper itself

Abstract

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

Indexed as

SoftwareCD8-Positive T-LymphocytesComputational BiologyDatabases, FactualFlow CytometryHumansReproducibility of Results

Identifiers

PMID25167361
PMCPMC4148203

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.