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12 Updates and Issues

2026-01-18

Source: vignettes/articles/12_Updates_And_Issues.Rmd
12_Updates_And_Issues.Rmd

Updates and news

  • Version 0.8.1: More fluorophores, rearranging detectors if needed

  • Version 0.8.2: Support for Mosaic and Xenith cytometers

  • Version 0.8.3: Patch for error introduced in 0.8.2

  • Version 0.8.4: Changes to error messaging in check.control.file

  • Version 0.8.5: Improvements to keyword handling in writing FCS files

  • Version 0.8.6: Improvements to plotting, fluorophore matching

  • Version 0.8.7: Support for Symphony A5 SE and Cytek Northern Lights. More improvements to plotting. Marker names will now be added to the control file based on matches in the FCS file names, where possible. The Hotspot(TM) matrix will be calculated and plotted as per the preprint by Peter Mage et al.

  • Version 0.9.0:

    • Changes to get.spectral.variants, including fixing of previously user-modifiable parameters, low-level denoising of spectra and a bug patch for situations with beads using internal negatives.
    • More checks in check.control.file.
    • New parallel backend.
    • Faster AutoSpectral unmixing in base R.
    • Adjustments to reduce any discontinuities produced during unmixing.
    • See also updates in AutoSpectralRcpp, including a large speed up and general improvement to the Poisson IRLS unmixing.
    • Patch to reload.flow.control bug affecting ID7000 samples.
    • Patch to define.flow.control() affecting universal negative definitions and impacting on clean.controls().
    • Calculation of the unmixing matrix (Moore-Penrose pseudoinverse) will now be done using singular value decomposition svd() for numerical stability for all approaches. Up to now, it has been done with normal equations via solve(). This should be better in edge cases. In most cases, the only difference will be floating point error. Calculation time is equivalent because almost all of the computational effort is on projecting the raw data into the unmixed space via the unmixing matrix, not calculating the unmixing matrix.
    • New functions to save.unmixing.matrix and calculate.weights
    • Patches to define.flow.control that were causing redundant gates to be created.
    • Code legibility formatting.

  • Version 0.9.1:

  • Switch to FlowSOM::SOM() from EmbedSOM::SOM().

  • Patch to appending “-A” suffix to parameter names.

  • Version 0.9.2:

  • Faster base R per-cell optimization.

  • Patch to writing of “-A” in the channel names of FCS files.

  • Version 1.0.0:

  • Changes to unmix.autospectral() to speed up processing and reduce discontinuities in the resulting unmixed data.

  • Version 1.0.0 brings a revamp to how AutoSpectral identifies the best spectra on a per-cell basis. The theory behind it remains the same–we are still trying to identify the variation in the autofluorescence and fluorophores that best reduces the residual on a per-cell basis. Now, however, we do not need to do that using brute force. Instead, we can search only through variants (or autofluorescences) that align with a given cell’s residual. Thus we can pre-screen the variants to a select few and then test just those. This means we can figure out the solution in way less time. It also means that a native R implementation of the algorithm is possible in R in a somewhat reasonable time frame. So, that may help for anyone struggling to use the fast C++ version in AutoSpectralRcpp. Specifics on this will be detailed in an article on GitHub and Colibri Cytometry.

  • Since we can now quickly identify which variants are useful for a given cell, we can test more variants, allowing a finer-grained view of the variation, which may improve unmixing quality.

  • Autofluorescence extraction and fluorophore variation extraction are now modified to search for more variation, focusing on “problematic” cells that remain far from where they should be when the first batch of variation is applied. This is most helpful for extracting autofluorescence in complex tissue samples, where AutoSpectral previously struggled to deal with the last few messy cells.

  • Speed in unmixing should be the biggest change, particularly if you run using AutoSpectralRcpp.

  • When extracting autofluorescence using get.af.spectra(), you will now get a set of plots showing you the unmixed data for the channels most affected by the autofluorescence (“worst channels”). The same channels will be plotted after a single round of autofluorescence extraction per cell (as in AutoSpectral v0.9.2 and earlier) as well as after the second round, using data from more difficult cells. To see this, run with refine = TRUE, which is the default setting now.

  • Autofluorescence is now assigned to each cell using a shortcut to “project” where the AF will impact on fluorophore or residual space. This is especially fast for residual-based assignment.

  • Perhaps most importantly, discontinuities that sometimes appeared in the data after unmixing using per-cell-fluorophore optimization, particularly with the “fast” approximation, should now be gone or at least greatly diminished.

  • Another change to the base unmixing functions to provide a speed-up when unmixing in a per-cell loop. When performing standard OLS or WLS unmixing outside of per-cell optimization, singular value decomposition will still be used to calculate the unmixing matrix.