| DEAnalysisMAST {DWLS} | R Documentation |
DEAnalysisMAST
Description
Perform DE analysis using MAST. Dampened weighted least squares (DLWS) 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. First, solve OLS then use the solution to find a starting point for the weights. Next, the dampened weighted least squares is performed. The weights are iterated until convergence then the dampening constant for weights is found using cross-validation (with decreasing step size for convergence).
DWLS captures ISC composition changes across conditions. One of the most important applications of deconvolution methods is in the identification of cell-type composition variations across conditions.
Note: The function uses solveDampenedWLSj() and findDampeningConstant().
Usage
DEAnalysisMAST(scdata, id, path)
Arguments
scdata |
The gene expression datafarme |
id |
The unique identities within the data |
path |
The path for the RData results |
Value
matrix The resulting matrix is a gene by cell-type signature matrix. The cell-type signature matrix is constructed using a representative single-cell data set, such that all cell types expected in the bulk data are also represented in the single-cell data (the converse need not be true). The single-cell data is first clustered to reveal its constituent cell types.The function will return 3 different files: an RData file, an rds file, and a csv file.
Examples
#dataSC
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataSC.RData"
#dest <- "data/dataSC.RData"
#load(download.file(url, tempfile(data/dataSC.RData))
#load("dataSC.RData")
#SOLUTION
load(system.file("extdata", "dataSC.RData", package = "DWLS"))
#dataBulk
url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
dest <- "data/dataBulk.RData"
load(download.file(url, tempfile(dest)))
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
#labels
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/labels.RData"
#dest <- "data/labels.RData"
#download.file(url, dest)
#load("data/labels.RData")
load(system.file("extdata", "labels.RData", package = "DWLS"))
#data('trueLabels', package = "DWLS")
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/trueLabels.RData"
#dest <- "data/trueLabels.RData"
#download.file(url, dest)
#load("data/trueLabels.RData")
load(system.file("extdata", "trueLabels.RData", package = "DWLS"))
labels<-trueLabels
#Old Method
#load("data/dataBulk.RData") #read in bulk data for WT1 (control condition #1)
#load("data/labels.RData") #read in single-cell labels from clustering
#data('dataSC_3', package = "DWLS")
#dataSC <- dataSC_3
labels<-trueLabels
#Old Method
#load("data/dataBulk.RData") #read in bulk data for WT1 (control condition #1)
#load("data/labels.RData") #read in single-cell labels from clustering
#data('dataSC_3', package = "DWLS")
#dataSC <- dataSC_3
labels<-trueLabels
#Change to real labels
newcat<-c("NonCycISC","CycISC","TA","Ent","PreEnt","Goblet","Paneth","Tuft",
"EE")
for (i in 1:length(newcat)){
labels[which(labels==(i-1))]<-newcat[i]
}
#Run deconvolution
#Results are in inst/extdata/results folder -- run on local
#Example code below
Mast_test <- DEAnalysisMAST(dataSC, labels, "inst/extdata/results")