DWLS: Gene Expression Deconvolution Using Dampened Weighted Least
The rapid development of single-cell transcriptomic technologies
has helped uncover the cellular heterogeneity within cell populations.
However, bulk RNA-seq continues to be the main workhorse for quantifying
gene expression levels due to technical simplicity and low cost. To most
effectively extract information from bulk data given the new knowledge
gained from single-cell methods, we have developed a novel algorithm to
estimate the cell-type composition of bulk data from a single-cell
RNA-seq-derived cell-type signature. Comparison with existing methods using
various real RNA-seq data sets indicates that our new approach is more
accurate and comprehensive than previous methods, especially for the
estimation of rare cell types. More importantly,our method can detect
cell-type composition changes in response to external perturbations,
thereby providing a valuable, cost-effective method for dissecting the
cell-type-specific effects of drug treatments or condition changes.
As such, our method is applicable to a wide range of biological and
clinical investigations. Dampened weighted least squares ('DWLS') is an
estimation method for gene expression deconvolution, in which the cell-type
composition of a bulk RNA-seq data set is computationally inferred.
This method corrects common biases towards cell types that are
characterized by highly expressed genes and/or are highly prevalent, to
provide accurate detection across diverse cell types. See:
<https://www.nature.com/articles/s41467-019-10802-z.pdf> for more
information about the development of 'DWLS' and the methods behind our
||R (≥ 3.5.0)
||quadprog, reshape, Seurat, ROCR, varhandle, dplyr, stats, utils, e1071, MAST, SummarizedExperiment
||testthat (≥ 3.0.0), Matrix (≥ 1.3.3)
||Daphne Tsoucas [aut],
Adriana Sistig [aut, cre]
||Adriana Sistig <adriana.sistig at icahn.mssm.edu>
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