modelMatSelection: Retrieve normalised topic-cell and region-topic assignments

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

Retrieve normalised topic-cell and region-topic assignments

Description

Retrieve topic-cell and region-topic assignments

Usage

modelMatSelection(object, target, method, all.regions = FALSE)

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()).
all.regions	If target is region, whether to return a matrix with all regions or only regions belonging to binarized topics (see binarizecisTopics()).

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'}).

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