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  "Title": "Gene Expression Deconvolution Using Dampened Weighted Least\nSquares",
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  "Authors@R": "c(person(\"Daphne\", \"Tsoucas\", email = \"dtsoucas@gmail.com\", role = \"aut\"),\nperson(\"Adriana\", \"Sistig\", email = \"adriana.sistig@icahn.mssm.edu\",\nrole = c(\"aut\",\"cre\")))",
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  "Description": "The rapid development of single-cell transcriptomic\ntechnologies has helped uncover the cellular heterogeneity\nwithin cell populations. However, bulk RNA-seq continues to be\nthe main workhorse for quantifying gene expression levels due\nto technical simplicity and low cost. To most effectively\nextract information from bulk data given the new knowledge\ngained from single-cell methods, we have developed a novel\nalgorithm to estimate the cell-type composition of bulk data\nfrom a single-cell RNA-seq-derived cell-type signature.\nComparison with existing methods using various real RNA-seq\ndata sets indicates that our new approach is more accurate and\ncomprehensive than previous methods, especially for the\nestimation of rare cell types. More importantly,our method can\ndetect cell-type composition changes in response to external\nperturbations, thereby providing a valuable, cost-effective\nmethod for dissecting the cell-type-specific effects of drug\ntreatments or condition changes. As such, our method is\napplicable to a wide range of biological and clinical\ninvestigations. Dampened weighted least squares ('DWLS') is an\nestimation method for gene expression deconvolution, in which\nthe cell-type composition of a bulk RNA-seq data set is\ncomputationally inferred. This method corrects common biases\ntowards cell types that are characterized by highly expressed\ngenes and/or are highly prevalent, to provide accurate\ndetection across diverse cell types. See:\n<https://www.nature.com/articles/s41467-019-10802-z.pdf> for\nmore information about the development of 'DWLS' and the\nmethods behind our functions.",
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  "Date/Publication": "2022-05-19 13:36:10 UTC",
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