Data processing workflow for large-scale immune monitoring studies by mass cytometry.

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 CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batc

SUBMITTER: Rybakowska P 

PROVIDER: S-EPMC8188119 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature