runtSNE: Run tSNE on the cell-cisTopic/region-cisTopic distributions

In aertslab/cisTopic: cisTopic: Probabilistic modelling of cis-regulatory topics from single cell epigenomics data

Run tSNE on the cell-cisTopic/region-cisTopic distributions

Description

Run tSNE on the cell-cisTopic/region-cisTopic distributions.

Usage

runtSNE(object, target, method = "Z-score", seed = 123, ...)

Arguments

object	Initialized cisTopic object, after the [e-mail] has been filled.
target	Whether dimensionality reduction should be applied on cells ('cell') or regions (region). Note that for speed and clarity reasons, dimesionality reduction on regions will only be done using the regions assigned to topics with high confidence (see binarizecisTopics()).
method	Select the method for processing the cell assignments: 'Z-score' and 'Probability'. In the case of regions, an additional method, 'NormTop' is available (see getRegionScores()).
seed	Integer for making the results reproducible.
...	See Rtsne from the package Rtsne.

Details

'Z-score' computes the Z-score for each topic assingment per cell/region. 'Probability' divides the topic assignments by the total number of assignments in the cell/region in the last iteration plus alpha. If using 'NormTop', regions are given an score defined by: \beta_{w, k} (\log \beta_{w,k} - 1 / K \sum_{k'} \log \beta_{w,k'}).

Value

Returns a cisTopic object with the tSNE coordinates stored in object@dr in object@dr$cell$tSNE or object@dr$region$tSNE depending on the target.

Examples

cisTopicObject <- runtSNE(cisTopicobject, target='cell', method='Z-score')
cisTopicObject

aertslab/cisTopic documentation built on April 6, 2024, 9:31 p.m.