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. To begin, the user must input a bulk RNA-seq data set, along with a labeled representative single-cell RNA-seq data set that will serve to generate cell-type-specific gene expression profiles. Ideally, the single-cell data set will contain cells from all cell types that may be found in the bulk data. DWLS will return the cell-type composition of the bulk data.
DWLS is written in the R programming language. To apply DWLS to any RNA-seq data set, please refer to the manual, Manual.docx. This includes code to run DWLS on a simple intestinal stem cell example.
This project includes a script containing the core functions and packages, under Deconvolution_functions.R, as well as three applications of DWLS to various data sets. These are each contained in a separate folder, and include:
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Simulation_Schelker: This application uses data from a previous deconvolution analysis by Schelker et al. [1], where bulk data is simulated by adding together gene expression profiles from single-cell data derived from human donor peripheral blood mononuclear cells (PBMCs), tumor-derived melanoma patient samples, and ovarian cancer ascites samples. A total of 27 patient-specific simulated bulk data sets are deconvolved using a gene expression signature generated from the combined single-cell data. The single-cell data set is not included due to the large file size, but can be generated through the Matlab code found at: https://figshare.com/s/711d3fb2bd3288c8483a.
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ISC: In this analysis, we deconvolve bulk intestinal stem cell RNA-seq data under three conditions, using a gene expression signature created from intestinal stem cell single-cell data sets under multiple conditions. Both single-cell and bulk data sets were generated by Yan et al. [2].
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MCA: We generate eight bulk RNA-seq data sets from four healthy mouse tissues (two per tissue), and deconvolve each using a comprehensive set of single-cell data sets from the Mouse Cell Atlas from Han et al. [3]. The single-cell data set is not included due to the large file size, but can be downloaded from: https://figshare.com/s/865e694ad06d5857db4b.
[1] Schelker M, Feau S, Du J, Ranu N, Klipp E, MacBeath G, Schoeberl B, Raue A: Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat Commun 2017, 8:2032.
[2] Yan KS, Janda CY, Chang J, Zheng GXY, Larkin KA, Luca VC, Chia LA, Mah AT, Han A, Terry JM, et al: Non-equivalence of Wnt and R-spondin ligands during Lgr5. Nature 2017, 545:238-242.
[3] Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F, et al: Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 2018, 172:1091-1107.e1017.
