ArticleComputational and structural biotechnology journal2021
Data processing workflow for large-scale immune monitoring studies by mass cytometry.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The βScience advances · 2025Article
- Haematology dimension reduction, a large scale application to regular care haematology data.BMC medical informatics and decision making · 2025Article
- Does age in months influence Chinese Holstein dairy cow production performance after controlling for days in milk?Frontiers in veterinary science · 2025Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer.Breast cancer research : BCR · 2024Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.BMC bioinformatics · 2024Article
- Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings.Methods in molecular biology (Clifton, N.J.) · 2024Review
- ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Protocol for large scale whole blood immune monitoring by mass cytometry and Cyto Quality Pipeline.STAR protocols · 2022Article
- Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like
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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.