Get Fluorophore Variants — get.fluor.variants • AutoSpectral

Assesses variation in the spectral signature of a single-stained flow cytometry control sample. Uses SOM-based clustering on the brightest positive events in the file.

Usage

get.fluor.variants(
  fluor,
  file.name,
  control.dir,
  asp,
  spectra,
  af.spectra,
  n.cells,
  som.dim,
  figures,
  output.dir,
  verbose,
  spectral.channel,
  universal.negative,
  control.type,
  raw.thresholds,
  unmixed.thresholds,
  flow.channel,
  refine = TRUE,
  problem.quantile = 0.95,
  variant.fill.color = "red",
  variant.fill.alpha = 0.7,
  median.line.color = "black",
  median.linewidth = 1
)

Arguments

fluor: The name of the fluorophore.
file.name: A named vector of file names for the samples.
control.dir: The directory containing the control files.
asp: The AutoSpectral parameter list.
spectra: A matrix containing the spectral data. Fluorophores in rows, detectors in columns.
af.spectra: Spectral signatures of autofluorescences, normalized between 0 and 1, with fluorophores in rows and detectors in columns. Prepare using get.af.spectra.
n.cells: Numeric. Number of cells to use for defining the variation in spectra. Up to n.cells cells will be selected as positive events in the peak channel for each fluorophore, above the 99.5th percentile level in the unstained sample.
som.dim: Numeric. Number of x and y dimensions to use in the SOM for clustering the spectral variation.
figures: Logical, controls whether the variation in spectra for each fluorophore is plotted in output.dir. Default is TRUE.
output.dir: File path to whether the figures and .rds data file will be saved. Default is NULL, in which case asp$variant.dir will be used.
verbose: Logical, default is TRUE. Set to FALSE to suppress messages.
spectral.channel: A vector of spectral channels.
universal.negative: A named vector of unstained negative samples, with names corresponding to the fluorophores.
control.type: Character, either "beads" or "cells". Determines the type of control sample being used and the subsequent processing steps.
raw.thresholds: A named vector of numerical values corresponding to the threshold for positivity in each raw detector channel. Determined by the 99.5th percentile on the unstained sample, typically.
unmixed.thresholds: A named vector of numerical values corresponding to the threshold for positivity in each unmixed channel. Determined by the 99.5th percentile on the unstained sample, typically after single-cell AF unmixing.
flow.channel: A named vector of peak raw channels, one per fluorophore.
refine: Logical, default is TRUE. Controls whether to perform a second round of variation measurement on "problem cells", which are those with the highest spillover, as defined by problem.quantile.
problem.quantile: Numeric, default 0.95. The quantile for determining which cells will be considered "problematic" after unmixing with per-cell AF extraction. Cells in the problem.quantile or above with respect to total signal in the fluorophore (non-AF) channels after per-cell AF extraction will be used to determine additional autofluorescence spectra, using a second round of clustering and modulation of the previously selected autofluorescence spectra. A value of 0.95 means the top 5% of cells, those farthest from zero, will be selected for further investigation.
variant.fill.color: Color for the shaded region indicating the range of variation in the spectra. Feeds to fill in geom_ribbon. Default is "red".
variant.fill.alpha: Transparency (alpha) for the color in variant.fill.color. How intense the color of the variant spectra will be. Default is 0.7
median.line.color: Color for the line representing the median or optimized single spectrum. Default is "black".
median.linewidth: Width of the line for the single optimized spectrum. Default is 1.

Value

A matrix with the flow expression data.

References

Van Gassen S et al. (2015). "FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data." Cytometry Part A, 87(7), 636-645. doi:10.1002/cyto.a.22625 Wehrens R, Kruisselbrink J (2018). “Flexible Self-Organizing Maps in kohonen 3.0.” Journal of Statistical Software, 87(7), 1-18. doi:10.18637/jss.v087.i07