Description Usage Arguments Details Value Author(s) References See Also
cyto_map
is a convenient wrapper to produce dimensionreduced maps of
cytometry data using PCA, tSNE, FItSNE, UMAP and EmbedSOM. These
dimensionality reduction functions are called using the default settings, but
can be altered by passing relvant arguments through cyto_map
. To see a
full list of customisable parameters refer to the documentation for each of
these functions by clicking on the links below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54  cyto_map(x, ...)
## S3 method for class 'GatingSet'
cyto_map(
x,
parent = "root",
select = NULL,
channels = NULL,
display = 1,
type = "UMAP",
merge_by = "all",
split = TRUE,
names = NULL,
save_as = NULL,
inverse = FALSE,
trans = NULL,
plot = TRUE,
seed = NULL,
...
)
## S3 method for class 'flowSet'
cyto_map(
x,
select = NULL,
channels = NULL,
display = 1,
type = "UMAP",
merge_by = "all",
split = TRUE,
names = NULL,
save_as = NULL,
inverse = FALSE,
trans = NULL,
plot = TRUE,
seed = NULL,
...
)
## S3 method for class 'flowFrame'
cyto_map(
x,
channels = NULL,
display = 1,
type = "UMAP",
split = TRUE,
names = NULL,
save_as = NULL,
inverse = FALSE,
trans = NULL,
plot = TRUE,
seed = NULL,
...
)

x 
object of class 
... 
additional arguments passed to the called dimension reduction function. Links to the documentation for these functions can be found below. 
parent 
name of the parent population to extract from

select 
designates which samples should be used for mapping when a

channels 
vector of channels names indicating the channels that should be used by the dimension reduction algorithm to compute the 2dimensional map, set to all channels with assigned markers by default. Restricting the number of channels can greatly improve processing speed and resolution. 
display 
total number of events to map, all events in the combined data are mapped by default. 
type 
dimension reduction type to use to generate the map, supported options include "PCA", "tSNE", "FItSNE", "UMAP" and "EmbedSOM". Users can also supply the name of a function to perform custom mappings. 
merge_by 
vector of experimental variables to split and merge samples into groups prior to mapping, set to "all" by default to create a single consensus map. 
split 
logical indicating whether samples merged using

names 
original names of the samples prior to merging using

save_as 
passed to 
inverse 
logical indicating whether the data should be inverse
transformed prior to writing FCS files, set to FALSE by default. Inverse
transformations of 
trans 
object of class 
plot 
logical indicating whether the constructed map should be plotted
using 
seed 
integer to set seed prior to mapping to ensure more consistent results between runs. 
If you use cyto_map
to map your cytometry data, be sure to cite the
publication that describes the dimensionality reduction algorithm that you
have chosen to use. References to these publications can be found in the
references section of this document.
flowFrame, flowSet, GatingHierarchy or GatingSet containing the mapped projection parameters.
Dillon Hammill, Dillon.Hammill@anu.edu.au
N. B. Erichson, S. Voronin, S. L. Brunton and J. N. Kutz. 2019. Randomized Matrix Decompositions Using R. Journal of Statistical Software, 89(11), 148. http://doi.org/10.18637/jss.v089.i11.
N. Halko, P. Martinsson, and J. Tropp. "Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions" (2009). (available at arXiv http://arxiv.org/abs/0909.4061).
Gabriel K. (1971). The biplot graphical display of matrices with application to principal component analysis. Biometrika 58, 453–467. doi:10.1093/biomet/58.3.453.
Maaten, L. van der, & Hinton, G. (2008). Visualizing Data using tSNE. Journal of Machine Learning Research 9, 2579–2605. http://www.jmlr.org/papers/volume9/vandermaaten08a/.
Linderman, G., Rachh, M., Hoskins, J., Steinerberger, S., Kluger., Y. (2019). Fast interpolationbased tSNE for improved visualization of singlecell RNAseq data. Nature Methods. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402590/.
McInnes, L., & Healy, J. (2018). UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426.
Kratochvíl, M., Koladiya, A., Balounova, J., Novosadova, V., Fišer, K., Sedlacek, R., Vondrášek, J., and Drbal, K. (2018). Rapid singlecell cytometry data visualization with EmbedSOM. Preprint at https://www.biorxiv.org/content/10.1101/496869v1.
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