optSNE-class {CytobankAPI}

S4 opt-SNE Class

Description

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.

Value

A Dimensionality Reduction advanced analysis object

Slots

perplexity

numeric representing a rough guess for the number of close neighbors any given cellular event will have, learn more about Dimensionality Reduction perplexity

auto_learning_rate

logical representing whether or not to set auto learning rate

clustering_channels

list 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_file

numeric representing the number of desired events per file

desired_total_events

numeric representing the number of desired total events per file

early_exaggeration

numeric representing how tight natural clusters in the original space are in the embedded space and how much space will be between them

event_sampling_method

character representing the name of event sampling method will be used, learn more about Event Sampling for Dimensionality Reduction analysis

fcsfile_ids

list representing the fcs file ids

gateset_id

numeric representing the selected gate id

learning_rate

numeric representing the learning rate,learn more about opt-SNE learning rate.

max_iterations

numeric representing the maximum number of iterations to perform– typically opt-SNE will automatically stop before this number is reached

normalize_scales

logical representing whether or not to normalize scales

random_seed

numeric 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