Matrix prior for data transfer between single cell data types in latent Dirichlet allocation.
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 informa...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-05-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011049 |
| _version_ | 1797818614157410304 |
|---|---|
| author | Alan Min Timothy Durham Louis Gevirtzman William Stafford Noble |
| author_facet | Alan Min Timothy Durham Louis Gevirtzman William Stafford Noble |
| author_sort | Alan Min |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T09:11:33Z |
| format | Article |
| id | doaj.art-f9d6061a0afb4e61be049f68830cdb19 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-13T09:11:33Z |
| publishDate | 2023-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f9d6061a0afb4e61be049f68830cdb192023-05-27T05:30:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-05-01195e101104910.1371/journal.pcbi.1011049Matrix prior for data transfer between single cell data types in latent Dirichlet allocation.Alan MinTimothy DurhamLouis GevirtzmanWilliam Stafford NobleSingle 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.https://doi.org/10.1371/journal.pcbi.1011049 |
| spellingShingle | Alan Min Timothy Durham Louis Gevirtzman William Stafford Noble Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. PLoS Computational Biology |
| title | Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. |
| title_full | Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. |
| title_fullStr | Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. |
| title_full_unstemmed | Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. |
| title_short | Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. |
| title_sort | matrix prior for data transfer between single cell data types in latent dirichlet allocation |
| url | https://doi.org/10.1371/journal.pcbi.1011049 |
| work_keys_str_mv | AT alanmin matrixpriorfordatatransferbetweensinglecelldatatypesinlatentdirichletallocation AT timothydurham matrixpriorfordatatransferbetweensinglecelldatatypesinlatentdirichletallocation AT louisgevirtzman matrixpriorfordatatransferbetweensinglecelldatatypesinlatentdirichletallocation AT williamstaffordnoble matrixpriorfordatatransferbetweensinglecelldatatypesinlatentdirichletallocation |