DESCRIPTION

Package: EnDecon
Type: Package
Title: EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning
Version: 0.2.0
Author: Jian-Juan Tu, Hui-Sheng Li
Maintainer: Jian-Juan Tu, Hui-Sheng Li<lihs@mails.ccnu.edu.cn>
Description: EnDecon is an ensemble learning method to estimate cell type abundances within spots for spatially resolved transcriptomics data by borrowing strengths from existing cell type deconvolution methods. EnDecon utilizes an optimization strategy for the combination of the base deconvolution results from twelve individual methods (designed for both bulk RNA-seq and SRT data) to produce a consensus deconvolution result. The current implementation of EnDecon integrates twelve state-of-the-art methods:  CARD, cell2location, DeconRNASeq, DWLS, MuSiC (MuSiC weighted and MuSiC all gene), RCTD, SCDC, SpatialDWLS, SPOTlight,Stereoscope, and SVR.
Depends: R (>= 3.5.0), pcaMethods
Imports: methods, 
         SCDC, 
         spacexr, 
         MuSiC, 
         DeconRNASeq, 
         DWLS, 
         Seurat, 
         SPOTlight,
         Giotto,
         spatstat.geom,
         CARD, 
         NMF, 
         utils, 
         stats, 
         graphics, 
         parallel, 
         doParallel, 
         foreach,
         reticulate,
         Biobase, 
         data.table, 
         Matrix,
         abind, 
         STdeconvolve
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.2.1
License: GPL(>= 2)
Encoding: UTF-8
LazyData: true
biocViews:
Remotes:
      renozao/xbioc,
      meichendong/SCDC,
      dmcable/spacexr,
      xuranw/MuSiC,
      RubD/Giotto,
      YingMa0107/CARD