{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:36:43Z","timestamp":1774618603588,"version":"3.50.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HG009937"],"award-info":[{"award-number":["R01 HG009937"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CCF-1750472"],"award-info":[{"award-number":["CCF-1750472"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CNS-1763680"],"award-info":[{"award-number":["CNS-1763680"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Droplet-based single-cell RNA-seq (dscRNA-seq) data are being generated at an unprecedented pace, and the accurate estimation of gene-level abundances for each cell is a crucial first step in most dscRNA-seq analyses. When pre-processing the raw dscRNA-seq data to generate a count matrix, care must be taken to account for the potentially large number of multi-mapping locations per read. The sparsity of dscRNA-seq data, and the strong 3\u2019 sampling bias, makes it difficult to disambiguate cases where there is no uniquely mapping read to any of the candidate target genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a Bayesian framework for information sharing across cells within a sample, or across multiple modalities of data using the same sample, to improve gene quantification estimates for dscRNA-seq data. We use an anchor-based approach to connect cells with similar gene-expression patterns, and learn informative, empirical priors which we provide to alevin\u2019s gene multi-mapping resolution algorithm. This improves the quantification estimates for genes with no uniquely mapping reads (i.e. when there is no unique intra-cellular information). We show our new model improves the per cell gene-level estimates and provides a principled framework for information sharing across multiple modalities. We test our method on a combination of simulated and real datasets under various setups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The information sharing model is included in alevin and is implemented in C++14. It is available as open-source software, under GPL v3, at https:\/\/github.com\/COMBINE-lab\/salmon as of version 1.1.0.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa450","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T15:17:53Z","timestamp":1592925473000},"page":"i292-i299","source":"Crossref","is-referenced-by-count":25,"title":["A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification"],"prefix":"10.1093","volume":"36","author":[{"given":"Avi","family":"Srivastava","sequence":"first","affiliation":[{"name":"Stony Brook University Department of Computer Science, , Stony Brook 11794, NY, USA"}]},{"given":"Laraib","family":"Malik","sequence":"additional","affiliation":[{"name":"Stony Brook University Department of Computer Science, , Stony Brook 11794, NY, USA"}]},{"given":"Hirak","family":"Sarkar","sequence":"additional","affiliation":[{"name":"University of Maryland Computer Science Department, , College Park 20742, MD, USA"}]},{"given":"Rob","family":"Patro","sequence":"additional","affiliation":[{"name":"University of Maryland Computer Science Department, , College Park 20742, MD, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"2024021913334963600_btaa450-B1","year":"2017"},{"key":"2024021913334963600_btaa450-B2","year":"2018"},{"key":"2024021913334963600_btaa450-B3","year":"2019"},{"key":"2024021913334963600_btaa450-B4","first-page":"757096","article-title":"Splotch: robust estimation of aligned spatial temporal gene expression data","author":"\u00c4ij\u00f6","year":"2019"},{"key":"2024021913334963600_btaa450-B5","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1038\/s41592-019-0576-7","article-title":"Exploring single-cell data with deep multitasking neural networks","volume":"16","author":"Amodio","year":"2019","journal-title":"Nat. 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