ArticleBioinformatics advances2023
tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- CellFuse Enables Multimodal Integration of Single-Cell and Spatial Proteomics Data for Systems-Level Analysis in Cancer.Cancer research · 2026Article
- Peripheral immunosuppressive and immunostimulatory signatures of severity and pain in spinal cord injury.Journal of neuroinflammation · 2026Article
- Cellular and molecular characterisation of the peripheral immune environment in migraine.Pain reports · 2025Article
- Annotation-free discovery of disease-relevant cells in single-cell datasets.Science advances · 2025Article
- Unveiling the power of high-dimensional cytometry data with cyCONDOR.Nature communications · 2024Article
- The tidyomics ecosystem: enhancing omic data analyses.Nature methods · 2024Article
- ThebioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface. Availability and implementation: {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). Supplementary information: Supplementary data are available at
Identifiers
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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.