Component: TSNE

TSNE

This component performs dimensionality reduction with an R wrapper of the C++ implementation of Barnes-Hut-SNE as described in http://lvdmaaten.github.io/tsne/. Remember to cite his paper whenever you use this component.

Version	1.1
Bundle	tools
Categories	Multivariate Statistics
Authors	Julia Casado (julia.casado@helsinki.fi)
Issue tracker	View/Report issues
Requires	R ; installer (bash)
Source files	component.xml tsne.R
Usage	Example with default values TSNE( in = CSV, check_duplicates = false, cost_tol = 1.48e-8, dims = 2, entropy_fast = true, initial_dims = 50, is_distance = false, max_iter = 1000, pca = false, perplexity = 30, seed = -1, theta = 0, verbose = true )

Version

1.1

Bundle

tools

Categories

Multivariate Statistics

Authors

Julia Casado (julia.casado@helsinki.fi)

Issue tracker

View/Report issues

Requires

R ; installer (bash)

Source files

component.xml tsne.R

Usage

Example with default values

Inputs

Name	Type	Mandatory	Description
in	CSV	Mandatory	A numeric matrix on which dimensionality reduction will be applied. Rows represent datapoints, e.g. cells/patients, and columns represent the dimensions, e.g. features or markers, that we want to reduce.

Name

Type

Mandatory

Description

CSV

Mandatory

A numeric matrix on which dimensionality reduction will be applied. Rows represent datapoints, e.g. cells/patients, and columns represent the dimensions, e.g. features or markers, that we want to reduce.

Outputs

Name	Type	Description
out	CSV	A numeric matrix with two or three columns, depending on dims parameter.
entropy	CSV	A matrix of entropy estimates in natural-base units (nats) for each sample. The column "orig" is an estimate of entropy in the original space, while "lots" is an estimate of the Kullback-Leibler divergence from the output to the input space.

Name

Type

Description

out

CSV

A numeric matrix with two or three columns, depending on dims parameter.

entropy

CSV

A matrix of entropy estimates in natural-base units (nats) for each sample. The column "orig" is an estimate of entropy in the original space, while "lots" is an estimate of the Kullback-Leibler divergence from the output to the input space.

Parameters

Name	Type	Default	Description
check_duplicates	boolean	false	It is best to check for duplicates with previous components because for big files this check-up will take too long time.
cost_tol	float	1.48e-8	Tolerance for cost function stall.
dims	int	2	Output dimensionality. Possible values are 2 or 3 because the original method was developed for visualization purposes and not thoroughly tested for larger dimensionality.
entropy_fast	boolean	true	Use fast and cheap entropy approximation.
initial_dims	int	50	Number of dimensions in a preliminary step of dimensionality reduction using PCA. Only read if parameter pca is true.
is_distance	boolean	false	Indicates whether the input is a distance matrix. In the documentation at the time of creating this component it warns that is an experimental feature. Use at own risk.
max_iter	int	1000	Number of iterations.
pca	boolean	false	Recommended for big files, over 5000 datapoints and 100 features. If true, it will run first basic PCA to reduce the dimensions. May result in poor performance for small datasets.
perplexity	int	30	It is a measure of information that in this case can be used as the number of nearest neighbors k that is employed in many manifold learners. If the visualization out of the output shows most of the points clustered like a ball means that the perplexity parameter was too high. It will depend on the size and structure of the data.
seed	int	-1	Seed number to make test cases reproducible. If null, the system generates one every time.
theta	float	0	Variable for Speed/accuracy trade-off. Higher theta means shorter running time and less accuracy of the results. Change only if the dataset is really really big.
verbose	boolean	true	Log the tsne process to terminal

Name

Type

Default

Description

check_duplicates

boolean

false

It is best to check for duplicates with previous components because for big files this check-up will take too long time.

cost_tol

float

1.48e-8

Tolerance for cost function stall.

dims

int

Output dimensionality. Possible values are 2 or 3 because the original method was developed for visualization purposes and not thoroughly tested for larger dimensionality.

entropy_fast

boolean

true

Use fast and cheap entropy approximation.

initial_dims

int

Number of dimensions in a preliminary step of dimensionality reduction using PCA. Only read if parameter pca is true.

is_distance

boolean

false

Indicates whether the input is a distance matrix. In the documentation at the time of creating this component it warns that is an experimental feature. Use at own risk.

max_iter

int

1000

Number of iterations.

pca

boolean

false

Recommended for big files, over 5000 datapoints and 100 features. If true, it will run first basic PCA to reduce the dimensions. May result in poor performance for small datasets.

perplexity

int

It is a measure of information that in this case can be used as the number of nearest neighbors k that is employed in many manifold learners. If the visualization out of the output shows most of the points clustered like a ball means that the perplexity parameter was too high. It will depend on the size and structure of the data.

seed

int

-1

Seed number to make test cases reproducible. If null, the system generates one every time.

theta

float

Variable for Speed/accuracy trade-off. Higher theta means shorter running time and less accuracy of the results. Change only if the dataset is really really big.

verbose

boolean

true

Log the tsne process to terminal

Test cases

Test case	Parameters▼	IN in	OUT out	OUT entropy
case1	properties	in	(missing)	(missing)
	perplexity=3, seed=1234

Test case

Parameters▼

IN
in

OUT
out

OUT
entropy

case1

properties

(missing)

perplexity=3,
seed=1234

Anduril