In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 5 ( 2023-5-5), p. e1011049-
Abstract:
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 C. elegans 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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1011049
DOI:
10.1371/journal.pcbi.1011049.g001
DOI:
10.1371/journal.pcbi.1011049.g002
DOI:
10.1371/journal.pcbi.1011049.g003
DOI:
10.1371/journal.pcbi.1011049.g004
DOI:
10.1371/journal.pcbi.1011049.s001
DOI:
10.1371/journal.pcbi.1011049.s002
DOI:
10.1371/journal.pcbi.1011049.s003
DOI:
10.1371/journal.pcbi.1011049.s004
DOI:
10.1371/journal.pcbi.1011049.s005
DOI:
10.1371/journal.pcbi.1011049.s006
DOI:
10.1371/journal.pcbi.1011049.s007
DOI:
10.1371/journal.pcbi.1011049.s008
DOI:
10.1371/journal.pcbi.1011049.s009
DOI:
10.1371/journal.pcbi.1011049.s010
DOI:
10.1371/journal.pcbi.1011049.s011
DOI:
10.1371/journal.pcbi.1011049.s012
DOI:
10.1371/journal.pcbi.1011049.s013
DOI:
10.1371/journal.pcbi.1011049.s014
DOI:
10.1371/journal.pcbi.1011049.s015
DOI:
10.1371/journal.pcbi.1011049.s016
DOI:
10.1371/journal.pcbi.1011049.s017
DOI:
10.1371/journal.pcbi.1011049.s018
DOI:
10.1371/journal.pcbi.1011049.s019
DOI:
10.1371/journal.pcbi.1011049.s020
DOI:
10.1371/journal.pcbi.1011049.s021
DOI:
10.1371/journal.pcbi.1011049.s022
DOI:
10.1371/journal.pcbi.1011049.r001
DOI:
10.1371/journal.pcbi.1011049.r002
DOI:
10.1371/journal.pcbi.1011049.r003
DOI:
10.1371/journal.pcbi.1011049.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
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