Learn R Programming

FlowSOM (version 1.4.0)

Using self-organizing maps for visualization and interpretation of cytometry data

Description

FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees.

Copy Link

Version

Version

1.4.0

License

GPL (>= 2)

Maintainer

Sofie Van Gassen

Last Published

February 15th, 2017

Functions in FlowSOM (1.4.0)

BuildMST

Build Minimal Spanning Tree
FlowSOM

Run the FlowSOM algorithm
PlotMarker

Plot marker values
CountGroups

Calculate differences in cell counts between groups
PlotGroups

Plot differences between groups
PlotStarsSD

Plot standard deviation star charts
PlotVariable

Plot a variable for all nodes
SaveClustersToFCS

Write FlowSOM clustering results to the original FCS files
MapDataToCodes

Assign nearest node to each datapoint
PlotNumbers

Plot the index of each node
Initialize

Select k well spread points from X
PeaksAndValleys

Find peaks and valleys in one-dimensional data
plotStarLegend

Plot legend for star plot
UpdateNodeSize

Update nodesize of FlowSOM object
ReadInput

Read fcs-files or flowframes
BuildSOM

Build a self-organizing map
PlotStars

Plot star charts
ProcessGatingML

Process a gatingML file
NewData

Map new data to a FlowSOM grid
PlotPies

Plot comparison with other clustering
FMeasure

F measure
FlowSOMSubset

FlowSOM subset
PlotCenters

Plot cluster centers on a 2D plot
PlotClusters2D

Plot nodes on scatter plot
metaClustering_consensus

MetaClustering
MetaClustering

MetaClustering
Purity

Calculate mean weighted cluster purity
AddFlowFrame

Add a flowFrame to the data variable of the FlowSOM object
Dist.MST

Calculate distance matrix using a minimal spanning tree neighbourhood
QueryStarPlot

Query a certain cell type
AggregateFlowFrames

Aggregate multiple fcs files together
SOM

Build a self-organizing map