| optSNE-class | R Documentation |
A opt-SNE object that holds pertinent opt-SNE advanced analysis run information. This class should never be called explicitly. If a user would like to create a new Cytobank opt-SNE object, utilize the dimensionality_reduction.new function, or any other opt-SNE endpoints that return opt-SNE objects documented in the 'Details' section.
A Dimensionality Reduction advanced analysis object
perplexitynumeric representing a rough guess for the number of close neighbors any given cellular event will have, learn more about Dimensionality Reduction perplexity
auto_learning_ratelogical representing whether or not to set auto learning rate
clustering_channelslist the channels selected for the Dimensionality Reduction analysis, this can be either a list of short channel IDs (integer) OR long channel names (character)
desired_events_per_filenumeric representing the number of desired events per file
desired_total_eventsnumeric representing the number of desired total events per file
early_exaggerationnumeric representing how tight natural clusters in the original space are in the embedded space and how much space will be between them
event_sampling_methodcharacter representing the name of event sampling method will be used, learn more about Event Sampling for Dimensionality Reduction analysis
fcsfile_idslist representing the fcs file ids
gateset_idnumeric representing the selected gate id
learning_ratenumeric representing the learning rate,learn more about opt-SNE learning rate.
max_iterationsnumeric representing the maximum number of iterations to perform– typically opt-SNE will automatically stop before this number is reached
normalize_scaleslogical representing whether or not to normalize scales
random_seednumeric representing the seed, Dimensionality Reduction picks a random seed each run, but if users want reproducible data, setting the same seed will allow them to do this
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