| Type: | Package |
| Title: | Gene Expression Deconvolution Using Dampened Weighted Least Squares |
| Version: | 0.1.0 |
| Maintainer: | Adriana Sistig <adriana.sistig@icahn.mssm.edu> |
| Description: | 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 functions. |
| URL: | https://github.com/sistia01/DWLS |
| BugReports: | https://github.com/sistia01/DWLS/issues |
| Depends: | R (≥ 3.5.0) |
| Imports: | quadprog, reshape, Seurat, ROCR, varhandle, dplyr, stats, utils, e1071, MAST, SummarizedExperiment |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| Language: | en-US |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.1.2 |
| Suggests: | testthat (≥ 3.0.0), Matrix (≥ 1.3.3) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-05-19 13:37:17 UTC; adrianasistig |
| Author: | Daphne Tsoucas [aut], Adriana Sistig [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2022-05-24 09:20:01 UTC |
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")
Differential Expression without Idents (Seurat)
Description
This function calculates the differential expression values along with identifying the Idents (through Seurat). The output is saved in an RData file for each unique identity (id).
Usage
DEAnalysisSeurat(scdata, id, path)
Arguments
scdata |
The gene expression datafarme |
id |
The unique identities within the data |
path |
The path for the RData results |
Value
An RData and rds file with the differential expression analysis results for each unique id.
Examples
#dataSC
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataSC.RData"
#dest <- "data/dataSC.RData"
#download.file(url, dest)
#load("data/dataSC.RData")
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"
#download.file(url, 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
#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 -- run on local
#Results in inst/extdata/results
Seurat_DE <- DEAnalysisSeurat(dataSC, labels, "inst/extdata/results")
Differential Expression with Idents (Seurat)
Description
This function calculates the differential expression values along with identifying the Idents (through Seurat). The output is saved in an RData file for each unique identity (id).
Usage
DEAnalysisSeuratIdents(scdata, id, path)
Arguments
scdata |
The gene expression datafarme |
id |
The unique identities within the data |
path |
The path for the RData results |
Value
An RData file with the differential expression analysis results for each unique id.
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"))
#Old Method
#load("data/dataSC_3.RData")
#load("data/trueLabels.RData")
#load("data/dataBulk.RData") #read in bulk data for WT1 (control condition #1)
#load("data/labels.RData") #read in single-cell labels from clustering
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
#Seurat_test2 <- DEAnalysisSeuratIdents(dataSC, labels, "results")
Build Signature matrix using MAST given a differential expression matrix
Description
This function builds a signature matrix using a pre-created differential expression matrix. The input matrix must have the same format as the DEAnalysisMAST() function and must be saved as an RData file ending with _MIST. The file must be named identity_MIST.RData. See exampledata_MIST.RData for more information (inst/man).
Usage
MASTSignatureMatrixGivenDE(
scdata,
id,
path,
diff.cutoff = 0.5,
pval.cutoff = 0.01
)
Arguments
scdata |
The data |
id |
The identities of the genes |
path |
The path to the file results |
diff.cutoff |
This is automatically set to 0.5 |
pval.cutoff |
This is automatically set to 0.01 |
Value
Signature Matrix built using the MAST algorithm
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
#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]
}
#Results in inst/extdata/results -- run on local
#Signature<-buildSignatureMatrixMAST(dataSC,labels,"results",
# diff.cutoff=0.5,pval.cutoff=0.01)
Mean.in.log2space
Description
Applies the log2 to the mean of ((2^x - pseudo count) + pseudo count).
Usage
Mean.in.log2space(x, pseudo.count)
Arguments
x |
Data |
pseudo.count |
A pseudocount value |
Value
Values
Examples
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
Mean.in.log2space(dataBulk, 0.1)
Signature Matrix Using MAST
Description
This function builds a signature matrix using genes identified by the DEAnalysisMAST() function.
Usage
buildSignatureMatrixMAST(
scdata,
id,
path,
diff.cutoff = 0.5,
pval.cutoff = 0.01,
f = 200
)
Arguments
scdata |
The data |
id |
The identities of the genes |
path |
The path to the file results |
diff.cutoff |
This is automatically set to 0.5 |
pval.cutoff |
This is automatically set to 0.01 |
f |
The maximum number of genes (when creating the signature matrix, need to reduce number of genes, between 50:f number of significant genes are chosen). If not set, this number is automatically set to 200. |
Value
Signature Matrix built using the MAST algorithm
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
#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 on local w/ inst/extdata/results folder
#Signature <-
#buildSignatureMatrixMAST(
#dataSC,labels,"inst/extdata/results",diff.cutoff = 0.5,pval.cutoff = 0.01)
Signature Matrix Using Seurat
Description
This function builds a signature matrix using genes identified by the DEAnalysis() function.
Usage
buildSignatureMatrixUsingSeurat(
scdata,
id,
path,
diff.cutoff = 0.5,
pval.cutoff = 0.01,
f = 200
)
Arguments
scdata |
The data |
id |
The identities of the genes |
path |
The path to the file results |
diff.cutoff |
This is automatically set to 0.5 |
pval.cutoff |
The p-value cutoff. This is automatically set to 0.01 |
f |
The maximum number of genes (when creating the signature matrix, need to reduce number of genes, between 50:f number of significant genes are chosen). If not set, this number is automatically set to 200. |
Value
Signature Matrix built using the Seurat algorithm
Single cell data
Description
A subset of the dataSC dataset in inst/extdata/
Usage
dataSC_3
Format
Data
Source
https://pubmed.ncbi.nlm.nih.gov/28467820/
findDampeningConstant
Description
Finds a dampening constant for the weights using cross-validation. The goldStandard is used to define the weights. Multiple values of the dampening constant (multiplier) are tried. For each attempt, the variance of the dampened weighted solution for a subset of genes is calculated (on a randomly selected half of the genes). Note that infinite weights are ignored.The dampening constant that results in least cross-validation variance is chosen. It functions in a nondeterministic manner. The dampening constant defines the maximum value that any weight can take on.
Usage
findDampeningConstant(S, B, goldStandard)
Arguments
S |
List output from trimData$Sig (S) |
B |
List output from trimData$dataBulk (B) |
goldStandard |
Starting point for the weights, determined by solving OLS |
Value
value (dampening constant value)
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solution <- solveOLSInternal(S,B)
findDampeningConstant(S, B, solution)
m.auc
Description
Calculates the AUC of a dataset. The function mainly serves to support the DWLS function.
Usage
m.auc(dataset, grouping)
Arguments
dataset |
Data |
grouping |
Data subdivision |
Value
Matrix of standardized output of AUC calculation
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"))
pseudo.count = 0.1
data.used.log2 <- log2(dataSC+pseudo.count)
colnames(data.used.log2)<-make.unique(colnames(data.used.log2))
diff.cutoff=0.5
id = labels
for (i in unique(id)){
cells.symbol.list2 = colnames(data.used.log2)[which(id==i)]
cells.coord.list2 = match(cells.symbol.list2, colnames(data.used.log2))
cells.symbol.list1 = colnames(data.used.log2)[which(id != i)]
cells.coord.list1= match(cells.symbol.list1, colnames(data.used.log2))
data.used.log2.ordered = cbind(data.used.log2[,cells.coord.list1],
data.used.log2[,cells.coord.list2])
group.v <- c(rep(0,length(cells.coord.list1)),
rep(1, length(cells.coord.list2)))
#ouput
log2.stat.result <- stat.log2(data.used.log2.ordered,
group.v, pseudo.count)
Auc <- m.auc(data.used.log2.ordered, group.v)}
solveDampenedWLS
Description
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).
Note: The function uses solveDampenedWLSj() and findDampeningConstant().
Usage
solveDampenedWLS(S, B)
Arguments
S |
List output from trimData |
B |
List output from trimData |
Value
value (Dampened weighted least squares estimation values)
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solveDampenedWLS(S, B)
solveDampenedWLSj
Description
Solve dampened weighted least squares given a dampening constant.
Note: The function uses solveDampenedWLS() and findDampeningConstant().
Usage
solveDampenedWLSj(S, B, goldStandard, j)
Arguments
S |
List output from trimData$sig (S) |
B |
List output from trimData$bulk (B) |
goldStandard |
Starting point for the weights, this can be determined using solveOLSInternal(S,B) |
j |
The dampening constant, this can be determined using findDampeningConstant(S,B,goldStandard) |
Value
value (Dampened weighted least squares estimation values)
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solution <- solveOLSInternal(S,B)
j <- findDampeningConstant(S,B,solution)
goldStandard <- solveOLSInternal(S,B)
solveDampenedWLSj(S,B,goldStandard,j)
solveOLS
Description
This function solves or the unknown parameters using ordinary least squares (OLS). It is constrained such that cell type numbers are greater than 0.
Usage
solveOLS(S, B)
Arguments
S |
List output from trimData$sig (S) |
B |
List output from trimData$bulk (B) |
Value
Cell-type proportion
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solveOLS(S, B)
solveOLSInternal
Description
This function solves or the unknown parameters using ordinary least squares (OLS) without printing the output. It returns the cell numbers, not the proportions (see solveOLS).
Usage
solveOLSInternal(S, B)
Arguments
S |
List output from trimData$sig (S) |
B |
List output from trimData$bulk (B) |
Value
Cell numbers
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solveOLSInternal(S, B)
solveSVR
Description
Performs a support vector regression (SVR). First, the data is scaled then it solves for the SVR. An svm model is used with the following specifications nu=0.5,scale = TRUE, type = "nu-regression", kernel ="linear",cost = 1.
Nu-support vector regression was performed using the svm function in the e1071 package in R. Parameters were set to nu = 0.5, type = “nu-regression”, kernel = “linear”, cost = 1, and all others to the default values. Bulk data and signature matrices were scaled to -1, 1. These parameter and scaling choices match those specified in Schelker et al. in their MATLAB code, accessed through https://figshare.com/s/865e694ad06d5857db4b. As in Newman et al., model coefficients are extracted from the svm model using t(model$coefs) model$SV, and any negative coefficients are set to zero. The coefficients are then scaled by the sum of the coefficients, such that the scaled coefficients will sum to one.
Citations: Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Schelker, M. et al. Estimation of immune cell content in tumor tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).
Usage
solveSVR(S, B)
Arguments
S |
List output from trimData$sig (S) |
B |
List output from trimData$bulk (B) |
Value
Value (SVR)
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimmed <- trimData(Sig, dataBulk)
S <- trimmed$sig
B <- trimmed$bulk
solveSVR(S, B)
stat.log2
Description
One of the functions required for the differential expression analysis using MAST (DEAnalysisMast()) function.
Usage
stat.log2(data.m, group.v, pseudo.count)
Arguments
data.m |
Data |
group.v |
Groupings |
pseudo.count |
A pseudocount value |
Value
A dataframe of the log2 applied results
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"))
pseudo.count = 0.1
data.used.log2 <- log2(dataSC+pseudo.count)
colnames(data.used.log2)<-make.unique(colnames(data.used.log2))
diff.cutoff=0.5
id = labels
for (i in unique(id)){
cells.symbol.list2 = colnames(data.used.log2)[which(id==i)]
cells.coord.list2 = match(cells.symbol.list2, colnames(data.used.log2))
cells.symbol.list1 = colnames(data.used.log2)[which(id != i)]
cells.coord.list1 = match(cells.symbol.list1, colnames(data.used.log2))
data.used.log2.ordered = cbind(data.used.log2[,cells.coord.list1],
data.used.log2[,cells.coord.list2])}
group.v <- c(rep(0,length(cells.coord.list1)),
rep(1, length(cells.coord.list2)))
trimData
Description
This function trims bulk and single-cell data to contain the same genes. The result is a list of the intersecting genes within the two datasets.
Usage
trimData(Signature_Matrix, bulkdata)
Arguments
Signature_Matrix |
A single-cell signature matrix |
bulkdata |
A bulk dataset |
Value
A list of trimmed bulk and single-cell data.
Examples
#Sig
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/Sig.RData"
#dest <- "data/Sig.RData"
#download.file(url, dest)
#load("data/Sig.RData")
load(system.file("extdata", "Sig.RData", package = "DWLS"))
#dataBulk
#url <- "https://github.com/sistia01/DWLS/raw/main/inst/extdata/dataBulk.RData"
#dest <- "data/dataBulk.RData"
#download.file(url, dest)
#load("data/dataBulk.RData")
load(system.file("extdata", "dataBulk.RData", package = "DWLS"))
trimData(Signature_Matrix = Sig, bulkdata = dataBulk)
v.auc
Description
Uses the prediction() function in order to create standardized output from the data in order to perform an AUC calculation. The calculation results are rounded to the third decimal place. This function serves mainly to support the DWLS function.
Usage
v.auc(data.v, group.v)
Arguments
data.v |
Data |
group.v |
Data subdivision |
Value
Matrix of standardized output of AUC calculation
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"))
pseudo.count = 0.1
data.used.log2 <- log2(dataSC+pseudo.count)
colnames(data.used.log2)<-make.unique(colnames(data.used.log2))
diff.cutoff=0.5
id = labels
for (i in unique(id)){
cells.symbol.list2 = colnames(data.used.log2)[which(id==i)]
cells.coord.list2 = match(cells.symbol.list2, colnames(data.used.log2))
cells.symbol.list1 = colnames(data.used.log2)[which(id != i)]
cells.coord.list1= match(cells.symbol.list1, colnames(data.used.log2))
data.used.log2.ordered = cbind(data.used.log2[,cells.coord.list1],
data.used.log2[,cells.coord.list2])
group.v <- c(rep(0,length(cells.coord.list1)),
rep(1, length(cells.coord.list2)))
#ouput
log2.stat.result <- stat.log2(data.used.log2.ordered,
group.v, pseudo.count)
m.auc=function(data.used.log2.ordered,group.v)
{AUC=apply(data.used.log2.ordered, 1, function(x) v.auc(x,group.v))
AUC[is.na(AUC)]=0.5
return(AUC)}
}