clustering.flow: Clustering FCS data

In jgarces02/FlowCT: A semi-automated workflow for deconvolution of immunophenotypic data and objective reporting on large datasets

Clustering FCS data

Description

This function offers multiple automatic clustering methods for a FCS.SCE object. Those methods available are:

1. SOM: Self-Organizing Map (SOM) based on FlowSOM package.

2. Phenograph: An implementation of PhenoGraph algorithm (here for more information).

3. Seurat: Clustering method based on Seurat's single-cell analysis.

4. PARC: R implementation for Phenotyping by Accelerated Refined Community-partitioning (PARC) method, see Python module.

Usage

clustering.flow(
  fcs.SCE,
  assay.i = "normalized",
  method,
  scale = FALSE,
  markers.to.use = "all",
  num.k,
  seurat.res = 0.4,
  seurat.dims = 1:10
)

Arguments

fcs.SCE	A fcs.SCE object generated through FlowCT::fcs.SCE().
assay.i	Name of matrix stored in the fcs.SCE object from which calculate SOM clustering. Default = "normalized".
method	What method should be used for clustering purposes. Available ones are "SOM", "Phenograph", "Seurat" and "PARC".
scale	Should be data be scale before SOM clustering? (only available for this method). Default = "FALSE".
markers.to.use	Markers used for considering within the clustering calculation. Default = "all".
num.k	Number of clusters to calculate for methods "SOM" and "Phenograph".
seurat.res	Seurat's resolution to calculate clustering (it indicates the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters). Default = 0.4.
seurat.dims	Number of dimensions to calculated with Seurat's method. Default = 1:10.

Examples

## Not run: 
fcs <- clustering.flow(fcs, method = "som", num.k = 20)
fcs <- clustering.flow(fcs, method = "phenograph", num.k = 40)
fcs <- clustering.flow(fcs, method = "Seurat", seurat.res = 0.5)
fcs <- clustering.flow(fcs, method = "parc", assay.i = "transformed")

## End(Not run)

jgarces02/FlowCT documentation built on March 28, 2023, 12:42 p.m.