Franken: Franken Package

In ldvroditi/Franken: Clustering algorithm suitable for high-dimensional and high-throughput data

Description

Contains functions required for Franken clustering procedure

This function takes in single-cell data from fcs files, flowframes, flowsets or data.table/frames

Usage

Franken(
  readin_files,
  resultsDir,
  marker_ids,
  fseed = NULL,
  xd = 20,
  yd = 20,
  kn = 6,
  dist_method = "ejaccard"
)

Arguments

readin_files	flowSOM object containing data matrix, generated by running ReadInputfrkn first
resultsDir	Directory to store outputs
marker_ids	An array of ids of which data columns to use for clustering
fseed	Seed to be used for SOM and KNN building step. Setting your own seed is highly encouraged for reproducibility
xd	Width of the SOM grid
yd	Height of the SOM grid
kn	Number of nearest neighbors used to build the mutual KNN graph.
dist_method	Distance or Similarity function used to build KNN graph. Extended Jaccard Similarity is the recommended default.

Value

A list containing three items: 1) a flowSOM object 2) metaclustering results on SOM nodes and 3) a matrix of cellsXmarkers outputs

A list with two items: the first is the flowSOM object containing all information (see the vignette for more detailed information about this object), the second is the metaclustering of the nodes of the grid. This is a wrapper function for ReadInput, BuildSOM, BuildKNNGfrkn and walk_metaclustering. Executing them separately may provide more options.

Author(s)

Laura De Vargas Roditi [e-mail]

ldvroditi/Franken documentation built on April 18, 2021, 4:54 p.m.