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Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We propose a modification to MuSiC entitled \u2018DeTREM\u2019 which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05476-w","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T09:03:06Z","timestamp":1695114186000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM"],"prefix":"10.1186","volume":"24","author":[{"given":"Nicholas K.","family":"O\u2019Neill","sequence":"first","affiliation":[]},{"given":"Thor D.","family":"Stein","sequence":"additional","affiliation":[]},{"given":"Junming","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Habbiburr","family":"Rehman","sequence":"additional","affiliation":[]},{"given":"Joshua D.","family":"Campbell","sequence":"additional","affiliation":[]},{"given":"Masanao","family":"Yajima","sequence":"additional","affiliation":[]},{"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lindsay A.","family":"Farrer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"issue":"6531","key":"5476_CR1","doi-asserted-by":"publisher","first-page":"eaba5257","DOI":"10.1126\/science.aba5257","volume":"371","author":"A Kuchina","year":"2020","unstructured":"Kuchina A, Brettner LM, Paleologu L, Roco CM, Rosenberg AB, Carignano A, et al. 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