R and cytometry: workflows

Main workflows to start with are:

• the pipeline from Nowicka et al. from Mark Robinson’s lab.
• the primer of BioSurf for the data scientist (R pipeline). See also the valuable primer for the cytometrist.

Mark Robinson’s lab does a great job developing package and workflows:

• CATALYST (Cytometry dATa anALYSis Tools) allows Preprocessing and Differential discovery.
• diffcyt (Differential discovery in high-dimensional cytometry via high-resolution clustering)  proposes a workflow for analyzing differential abundances (DA) and differential states (DS, median expression of cell state markers by sample).
• censcyt (Differential abundance analysis with a right censored covariate in high-dimensional cytometry) address the challenge of censoring analysis.

Yvan Saeys’s lab proposes the FlowSOM package developed by Sofie Van Gassen. FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees.

Jonathan Irish’s lab provides recent methods, T-REX (Tracking Responders Expanding) and RAPID (Risk Assessment Population and Identification). MEM (Marker Enrichment Modeling) allows to ease cluster annotation.