You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Matrix prior for data transfer between single cell data types in latent Dirichlet allocation
Contributor:
Min, Alan;
Durham, Timothy;
Gevirtzman, Louis;
Noble, William Stafford
imprint:
Public Library of Science (PLoS), 2023
Published in:PLOS Computational Biology
Language:
English
DOI:
10.1371/journal.pcbi.1011049
ISSN:
1553-7358
Origination:
Footnote:
Description:
<jats:p>Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to generate. This motivates a method to leverage information from previously generated large scale scATAC-seq or scRNA-seq data to guide our analysis of new scATAC-seq datasets. We analyze scATAC-seq data using latent Dirichlet allocation (LDA), a Bayesian algorithm that was developed to model text corpora, summarizing documents as mixtures of topics defined based on the words that distinguish the documents. When applied to scATAC-seq, LDA treats cells as documents and their accessible sites as words, identifying “topics” based on the cell type-specific accessible sites in those cells. Previous work used uniform symmetric priors in LDA, but we hypothesized that nonuniform matrix priors generated from LDA models trained on existing data sets may enable improved detection of cell types in new data sets, especially if they have relatively few cells. In this work, we test this hypothesis in scATAC-seq data from whole <jats:italic>C. elegans</jats:italic> nematodes and SHARE-seq data from mouse skin cells. We show that nonsymmetric matrix priors for LDA improve our ability to capture cell type information from small scATAC-seq datasets.</jats:p>