tsne t-Distributed Stochastic Neighbor Embedding: is a method for constructing a low dimensional embedding of high-dimensional data.
| Input projection |
. |
row |
represents the variables (e.g. genes, channels, markers) |
col |
represents the observations (e.g. cells, samples, individuals) |
y-axis |
measurement value |
| Input parameters |
. |
dims |
logical, output dimensionality, default 2 |
initial_dims |
numeric, the number of dimensions that should be retained in the initial PCA step, default 50 |
perplexity |
numeric, perplexity parameter, default is 30 |
theta |
numeric, speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE, default 0.05 |
pca |
numeric, whether an initial PCA step should be performed, default TRUE |
max_iter |
numeric, number of iteration, default 1000 |
pca_center |
logical, should data be centered before pca is applied ? |
pca_scale |
logical, should data be scaled before pca is applied ? |
stop_lying_iter |
numeric, iteration after which the perplexities are no longer exaggerated |
mom_switch_iter |
numeric, iteration after which the final momentum is used |
| Output relations |
. |
tsne1, tsne2 |
first two components containing the new projected values |
The operator is a wrapper of the Rtsne() function from the Rtsne R package.
pca operator, atsne operator, umap operator