Unmix AutoSpectral

Unmix using the AutoSpectral method to extract autofluorescence and optimize fluorophore signatures at the single cell level.

unmix.autospectral(
  raw.data,
  spectra,
  af.spectra,
  spectra.variants = NULL,
  weighted = FALSE,
  weights = NULL,
  calculate.error = FALSE,
  use.dist0 = TRUE,
  verbose = TRUE
)

Arguments

raw.data

Expression data from raw fcs files. Cells in rows and detectors in columns. Columns should be fluorescent data only and must match the columns in spectra.

spectra

Spectral signatures of fluorophores, normalized between 0 and 1, with fluorophores in rows and 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.

spectra.variants

Named list (names are fluorophores) carrying matrices of spectral signature variations for each fluorophore. Prepare using get.spectral.variants. Default is NULL.

weighted

Logical, whether to use ordinary or weighted least squares unmixing as the base algorithm. Default is FALSE and will use OLS.

weights

Optional numeric vector of weights (one per fluorescent detector). Default is NULL, in which case weighting will be done by channel means (Poisson variance). Only used if weighted.

calculate.error

Logical, whether to calculate the RMSE unmixing model accuracy and include it as an output. Default is FALSE.

use.dist0

Logical, controls whether the selection of the optimal AF signature for each cell is determined by which unmixing brings the cell closest to 0 (use.dist0 = TRUE) or by which unmixing minimizes the per-cell residual (use.dist0 = FALSE). Default is TRUE.

verbose

Logical, whether to send messages to the console. Default is TRUE.

Value

Unmixed data with cells in rows and fluorophores in columns.