# SpatialDecon: mixed cell deconvolution for spatial and/or bulk gene expression
# data
# Copyright (C) 2020, NanoString Technologies, Inc.
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
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# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
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# with this program. If not, see https://www.gnu.org/licenses/.
# Contact us:
# NanoString Technologies, Inc.
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#' SpatialDecon: A package for estimating mixed cell type abundance in the regions of
#' spatially-resolved gene expression studies
#'
#' The SpatialDecon package estimates mixed cell type abundance in the regions
#' of spatially-resolved gene
#' expression studies, using the method of Danaher & Kim (2020), "Advances in
#' mixed cell deconvolution enable
#' quantification of cell types in spatially-resolved gene expression data."
#' It is also appropriate to apply to bulk gene expression data.
#'
#' @section functions:
#' Functions to help set up deconvolution:
#' \itemize{
#' \item derive_GeoMx_background Estimates the background levels from GeoMx
#' experiments
#' \item collapseCellTypes reformats deconvolution results to merge
#' closely-related cell types
#' \item download_profile_matrix Downloads a cell profile matrix.
#' \item safeTME: a data object, a matrix of immune cell profiles for use in
#' tumor-immune deconvolution.
#' }
#' Deconvolution functions:
#' \itemize{
#' \item spatialdecon runs the core deconvolution function
#' \item reverseDecon runs a transposed/reverse deconvolution problem, fitting
#' the data as a function of cell abundance estimates.
#' Used to measure genes' dependency on cell mixing and to calculate gene
#' residuals from cell mixing.
#' }
#' Plotting functions:
#' \itemize{
#' \item florets Plot cell abundance on a specified x-y space, with each point
#' a cockscomb plot showing the cell abundances of that region/sample.
#' \item TIL_barplot Plot abundances of tumor infiltrating lymphocytes (TILs)
#' estimated from the safeTME cell profile matrix
#' }
#' @examples
#' data(mini_geomx_dataset)
#' data(safeTME)
#' data(safeTME.matches)
#' # estimate background:
#' mini_geomx_dataset$bg <- derive_GeoMx_background(
#' norm = mini_geomx_dataset$normalized,
#' probepool = rep(1, nrow(mini_geomx_dataset$normalized)),
#' negnames = "NegProbe"
#' )
#' # run basic decon:
#' res0 <- spatialdecon(
#' norm = mini_geomx_dataset$normalized,
#' bg = mini_geomx_dataset$bg,
#' X = safeTME
#' )
#' # run decon with bells and whistles:
#' res <- spatialdecon(
#' norm = mini_geomx_dataset$normalized,
#' bg = mini_geomx_dataset$bg,
#' X = safeTME,
#' cellmerges = safeTME.matches,
#' cell_counts = mini_geomx_dataset$annot$nuclei,
#' is_pure_tumor = mini_geomx_dataset$annot$AOI.name == "Tumor"
#' )
#' @docType package
#' @name SpatialDecon-package
NULL
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