The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This code can install ADAPTSdata3
install.packages(‘devtools’) library(devtools) devtools::install_github(‘sdanzige/ADAPTSdata3’)
library(ADAPTS)
library(ADAPTSdata3)
library(preprocessCore)
library(pheatmap)
library(foreach)
doParallel::registerDoParallel(cores = parallel::detectCores())
set.seed(42)
method <- 'DCQ'#Set to FALSE to use saved version of processor intensive variables
# If set to TRUE, it will probable take 50+ minutes to complete
rebuild <- FALSEStep 1: Build a signature matrix from the normal data
normalData <- log(ADAPTSdata3::normalData.5061+1)Step 1a: Make a gList to rank genes
#The gList has the significantly different genes ranked by expression ratio
# Note, this is slow
if(rebuild==TRUE) {
gList <- ADAPTS::rankByT(normalData,remZinf = TRUE)
} else {
gList <- ADAPTSdata3::gList
}Step 1b: Determine 100 highest variance genes
#Find the most variant genes across cell types
cNames <- sub('\\.+[0-9]+$','',colnames(normalData))
ctMeans <- apply(normalData, 1, function(x){tapply(x, cNames, mean, na.rm=TRUE)})
gVars <- apply(ctMeans, 2, var)
topGenes <- names(tail(sort(gVars),100))Step 1c: Build the seed signature matrix & augment
sigMat1 <- t(ctMeans[,topGenes])
allSCdata <- normalData
colnames(allSCdata) <- cNames
topAug.var100 <- AugmentSigMatrix(origMatrix=sigMat1, fullData=allSCdata, newData=allSCdata, gList=gList, nGenes=1:100, plotToPDF=FALSE, imputeMissing=TRUE, condTol=1.01, postNorm=FALSE, minSumToRem=NA, addTitle=NULL, autoDetectMin=FALSE, calcSpillOver=TRUE)
Step 2: Deconvolve pseudo-bulk
pseudoBulk <- log(rowSums(ADAPTSdata3::normalData.5061, na.rm=TRUE)+1)
cellEst.top100 <- estCellPercent(refExpr = sigMat1, geneExpr = data.frame(pseudoBulk=pseudoBulk), method=method)
cellEst.aug <- estCellPercent(refExpr = topAug.var100$sigMatrix, geneExpr = data.frame(pseudoBulk=pseudoBulk), method=method)
actualFrac <- ADAPTSdata3::enumerateCellTypes(ADAPTSdata3::normalData.5061)
#>
#> acinar.cell alpha.cell
#> 130 541
#> beta.cell co.expression.cell
#> 152 23
#> delta.cell ductal.cell
#> 44 179
#> endothelial.cell epsilon.cell
#> 11 5
#> gamma.cell mast.cell
#> 107 4
#> MHC.class.II.cell PSC.cell
#> 2 32
#> unclassified.endocrine.cell
#> 25
actualFrac <- actualFrac / sum(actualFrac)
actualFrac <- c(actualFrac, 0) * 100
deconTable <- data.frame(top100=cellEst.top100, augmented=cellEst.aug, ref=actualFrac)
colnames(deconTable) <- c('top100','augmented','ref')Caclulate some statistics
AbsError <- abs(deconTable - actualFrac)
deconTableP <- as.data.frame(t(deconTable))
RMSEs <- apply(AbsError, 2, function(x){sqrt(mean(x^2))})
deconTableP$RMSE <- RMSEs
rhos <- apply(deconTable, 2, function(x){cor(x, actualFrac)})
deconTableP$rho <- rhos
rhos.spear <- apply(deconTable, 2, function(x){cor(x, actualFrac, method='spearman')})
deconTableP$rho.spear <- rhos.spear
deconTableP <- t(deconTableP)
colnames(deconTableP) <- c('top100', 'augmented', 'ref')
print(round(deconTableP,2))
#> top100 augmented ref
#> acinar.cell 11.38 11.56 10.36
#> alpha.cell 7.32 7.85 43.11
#> beta.cell 7.46 8.34 12.11
#> co.expression.cell 11.66 9.68 1.83
#> delta.cell 7.11 7.66 3.51
#> ductal.cell 4.36 11.49 14.26
#> endothelial.cell 0.00 2.56 0.88
#> epsilon.cell 7.02 6.11 0.40
#> gamma.cell 8.37 7.63 8.53
#> mast.cell 0.00 2.13 0.32
#> MHC.class.II.cell 0.00 2.68 0.16
#> PSC.cell 0.00 5.96 2.55
#> unclassified.endocrine.cell 35.33 16.37 1.99
#> others 0.00 0.00 0.00
#> RMSE 13.82 10.72 0.00
#> rho 0.05 0.26 1.00
#> rho.spear 0.50 0.69 1.00Show how combining Cell Types that are highly correlated improves correlation Step 3: Deconvolve pseudo-bulk with heirarchical deconvolution
#This is pretty slow
if(rebuild==TRUE) {
hier.top100 <- ADAPTS::hierarchicalSplit(sigMatrix = sigMat1, geneExpr = allSCdata, method=method)
hier.augment <- ADAPTS::hierarchicalSplit(sigMatrix = topAug.var100$sigMatrix, geneExpr = allSCdata, method=method)
} else {
hier.top100 <- ADAPTSdata3::hier.top100
hier.augment <- ADAPTSdata3::hier.augment
}pheatmap(t(hier.top100$deconMatrices[[2]]), cluster_rows = FALSE, cluster_cols = FALSE, main='Top 100 genes:spillover matrix')
pheatmap(t(hier.top100$deconMatrices[[length(hier.top100$deconMatrices)]]), main='Top 100 genes:clustered spillover matrix')
hier.top100$allClusters
#> [[1]]
#> [1] "acinar.cell" "ductal.cell"
#>
#> [[2]]
#> [1] "alpha.cell" "gamma.cell"
#>
#> [[3]]
#> [1] "beta.cell" "co.expression.cell"
#> [3] "delta.cell" "unclassified.endocrine.cell"
#>
#> [[4]]
#> [1] "endothelial.cell" "PSC.cell"
#>
#> [[5]]
#> [1] "epsilon.cell"
#>
#> [[6]]
#> [1] "mast.cell"
#>
#> [[7]]
#> [1] "MHC.class.II.cell"
#conv <- ADAPTS::spillToConvergence(sigMatrix = sigMat1, geneExpr = allSCdata)
pheatmap(t(hier.augment$deconMatrices[[2]]), cluster_rows = FALSE, cluster_cols = FALSE, main='Augmented:spillover matrix')
pheatmap(t(hier.augment$deconMatrices[[length(hier.augment$deconMatrices)]]), main='Augmented:clustered spillover matrix')
hier.augment$allClusters
#> [[1]]
#> [1] "acinar.cell" "ductal.cell"
#>
#> [[2]]
#> [1] "alpha.cell" "beta.cell"
#> [3] "co.expression.cell" "delta.cell"
#> [5] "gamma.cell" "unclassified.endocrine.cell"
#>
#> [[3]]
#> [1] "endothelial.cell" "PSC.cell"
#>
#> [[4]]
#> [1] "epsilon.cell"
#>
#> [[5]]
#> [1] "mast.cell" "MHC.class.II.cell"
#conv <- ADAPTS::spillToConvergence(sigMatrix = sigMat1, geneExpr = allSCdata)
groups <- sapply (unlist(hier.top100$allClusters), function(g) {
which(sapply(hier.top100$allClusters, function(x){g %in% x}))
})
groups <- c(groups, others=max(groups)+1)
comb.top100 <- apply(deconTable, 2, function(x){tapply(x, groups, sum)})
cors.top100 <- apply(comb.top100, 2, function(x){cor(x, comb.top100[,'ref'])})
cors.spear.top100 <- apply(comb.top100, 2, function(x){cor(x, comb.top100[,'ref'], method='spearman')})
rmses.top100 <- apply(comb.top100, 2, function(x){sqrt(mean((x-comb.top100[,'ref'])^2))})
combP.top100 <- as.data.frame(t(comb.top100))
colnames(combP.top100) <- c(sapply(hier.top100$allClusters, function(x){paste(x, collapse='_')}),'others')
combP.top100$RMSE <- rmses.top100
combP.top100$rho <- cors.top100
combP.top100$rho.spear <- cors.spear.top100
combP.top100 <- t(combP.top100)
combP.top100 <- combP.top100[,c(1,3)]
print('Top 100 Combination Predictions')
#> [1] "Top 100 Combination Predictions"
print(round(combP.top100,2))
#> top100
#> acinar.cell_ductal.cell 18.70
#> alpha.cell_gamma.cell 19.12
#> beta.cell_co.expression.cell_delta.cell_unclassified.endocrine.cell 18.49
#> endothelial.cell_PSC.cell 8.37
#> epsilon.cell 0.00
#> mast.cell 0.00
#> MHC.class.II.cell 35.33
#> others 0.00
#> RMSE 17.15
#> rho 0.32
#> rho.spear 0.51
#> ref
#> acinar.cell_ductal.cell 53.47
#> alpha.cell_gamma.cell 13.94
#> beta.cell_co.expression.cell_delta.cell_unclassified.endocrine.cell 19.04
#> endothelial.cell_PSC.cell 8.84
#> epsilon.cell 0.16
#> mast.cell 2.55
#> MHC.class.II.cell 1.99
#> others 0.00
#> RMSE 0.00
#> rho 1.00
#> rho.spear 1.00groups <- sapply (unlist(hier.augment$allClusters), function(g) {
which(sapply(hier.augment$allClusters, function(x){g %in% x}))
})
groups <- c(groups, others=max(groups)+1)
comb.augment <- apply(deconTable, 2, function(x){tapply(x, groups, sum)})
cors.augment <- apply(comb.augment, 2, function(x){cor(x, comb.augment[,'ref'])})
cors.spear.augment <- apply(comb.augment, 2, function(x){cor(x, comb.augment[,'ref'], method='spearman')})
rmses.augment <- apply(comb.augment, 2, function(x){sqrt(mean((x-comb.augment[,'ref'])^2))})
combP.augment <- as.data.frame(t(comb.augment))
colnames(combP.augment) <- c(sapply(hier.augment$allClusters, function(x){paste(x, collapse='_')}),'others')
combP.augment$RMSE <- rmses.augment
combP.augment$rho <- cors.augment
combP.augment$rho.spear <- cors.spear.augment
combP.augment <- t(combP.augment)
combP.augment <- combP.augment[,c(2,3)]
print('Augment Combination Predictions')
#> [1] "Augment Combination Predictions"
print(round(combP.augment,2))
#> augmented
#> acinar.cell_ductal.cell 19.41
#> alpha.cell_beta.cell_co.expression.cell_delta.cell_gamma.cell_unclassified.endocrine.cell 45.84
#> endothelial.cell_PSC.cell 9.76
#> epsilon.cell 2.68
#> mast.cell_MHC.class.II.cell 22.33
#> others 0.00
#> RMSE 16.58
#> rho 0.58
#> rho.spear 0.71
#> ref
#> acinar.cell_ductal.cell 53.47
#> alpha.cell_beta.cell_co.expression.cell_delta.cell_gamma.cell_unclassified.endocrine.cell 32.99
#> endothelial.cell_PSC.cell 8.84
#> epsilon.cell 0.16
#> mast.cell_MHC.class.II.cell 4.54
#> others 0.00
#> RMSE 0.00
#> rho 1.00
#> rho.spear 1.00Step 4: Hierarchical Deconvolution
#cellEst.top100.hier <- ADAPTS::hierarchicalClassify(sigMatrix = sigMat1, toPred = data.frame(pseudoBulk=pseudoBulk), geneExpr = allSCdata, hierarchData = hier.top100, method=method)
cellEst.top100.hier <- ADAPTS::hierarchicalClassify(sigMatrix = sigMat1, toPred = data.frame(pseudoBulk=pseudoBulk), geneExpr = allSCdata, hierarchData = hier.top100, method=method)
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
cellEst.top100.hier
#> pseudoBulk
#> acinar.cell 7.884952
#> ductal.cell 7.853475
#> alpha.cell 7.922658
#> gamma.cell 7.765773
#> beta.cell 11.745648
#> co.expression.cell 16.910532
#> delta.cell 12.268908
#> unclassified.endocrine.cell 20.628756
#> endothelial.cell 0.000000
#> PSC.cell 0.000000
#> epsilon.cell 7.019298
#> mast.cell 0.000000
#> MHC.class.II.cell 0.000000
#> others 0.000000cellEst.augment.hier <- ADAPTS::hierarchicalClassify(sigMatrix = topAug.var100$sigMatrix, toPred = data.frame(pseudoBulk=pseudoBulk), geneExpr = allSCdata, hierarchData = hier.augment, method=method)
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
cellEst.augment.hier
#> pseudoBulk
#> acinar.cell 10.4856529
#> ductal.cell 12.5597381
#> alpha.cell 9.5135593
#> beta.cell 8.5069856
#> co.expression.cell 10.7674625
#> delta.cell 8.4092042
#> gamma.cell 8.0353339
#> unclassified.endocrine.cell 12.2859508
#> endothelial.cell 0.0000000
#> PSC.cell 8.5182963
#> epsilon.cell 6.1087782
#> mast.cell 0.7992621
#> MHC.class.II.cell 4.0097760
#> others 0.0000000Combine and calculate statistics
deconTable.hier <- data.frame(top100.hier=cellEst.top100.hier, augmented.hier=cellEst.augment.hier)
colnames(deconTable.hier) <- c('top100.hier','augmented.hier')
deconTable.all <- cbind(deconTable.hier, deconTable)
deconTableP.all <- as.data.frame(t(deconTable.all))
#AbsError.all <- abs(deconTable.all - actualFrac)
#RMSEs.all <- apply(AbsError.all, 2, function(x){sqrt(mean(x^2))})
#rhos.all <- apply(deconTable.all, 2, function(x){cor(x, actualFrac)})
cors.aug <- apply(deconTable.all, 2, function(x){cor(x, deconTable.all[,'ref'])})
cors.aug.spear <- apply(deconTable.all, 2, function(x){cor(x, deconTable.all[,'ref'], method='spearman')})
rmses.aug <- apply(deconTable.all, 2, function(x){sqrt(mean((x-deconTable.all[,'ref'])^2))})
deconTableP.all$RMSE <- rmses.aug
deconTableP.all$rho <- cors.aug
deconTableP.all$rho.spear <- cors.aug.spear
deconTableP.all <- t(deconTableP.all)
#colnames(deconTableP) <- c('top100', 'augmented', 'ref')
deconTableP.all <- deconTableP.all[,c('top100','top100.hier','augmented','augmented.hier','ref')]
colnames(deconTableP.all) <- c('top','top.hier','aug','aug.hier','ref')
print('Normal Cell Predictions')
#> [1] "Normal Cell Predictions"
print(round(deconTableP.all,2))
#> top top.hier aug aug.hier ref
#> acinar.cell 11.38 7.88 11.56 10.49 10.36
#> ductal.cell 7.32 7.85 7.85 12.56 43.11
#> alpha.cell 7.46 7.92 8.34 9.51 12.11
#> gamma.cell 11.66 7.77 9.68 8.51 1.83
#> beta.cell 7.11 11.75 7.66 10.77 3.51
#> co.expression.cell 4.36 16.91 11.49 8.41 14.26
#> delta.cell 0.00 12.27 2.56 8.04 0.88
#> unclassified.endocrine.cell 7.02 20.63 6.11 12.29 0.40
#> endothelial.cell 8.37 0.00 7.63 0.00 8.53
#> PSC.cell 0.00 0.00 2.13 8.52 0.32
#> epsilon.cell 0.00 7.02 2.68 6.11 0.16
#> mast.cell 0.00 0.00 5.96 0.80 2.55
#> MHC.class.II.cell 35.33 0.00 16.37 4.01 1.99
#> others 0.00 0.00 0.00 0.00 0.00
#> RMSE 13.82 12.09 10.72 10.16 0.00
#> rho 0.05 0.12 0.26 0.39 1.00
#> rho.spear 0.50 0.31 0.69 0.37 1.00Step 5: Deconvolve Diabetes data
pseudoBulk.diabetes <- log(rowSums(ADAPTSdata3::diabetesData.5061, na.rm=TRUE)+1)
pb.d.df <- data.frame(pseudoBulk.diabetes=pseudoBulk.diabetes)
cellEst.d.top100 <- estCellPercent(refExpr = sigMat1, geneExpr = pb.d.df, method=method)
cellEst.d.augment <- estCellPercent(refExpr = topAug.var100$sigMatrix, geneExpr = pb.d.df, method=method)
actualFrac.diabetes <- ADAPTSdata3::enumerateCellTypes(ADAPTSdata3::diabetesData.5061)
#>
#> acinar.cell alpha.cell
#> 55 345
#> beta.cell co.expression.cell
#> 118 16
#> delta.cell ductal.cell
#> 70 207
#> endothelial.cell epsilon.cell
#> 5 2
#> gamma.cell mast.cell
#> 90 3
#> MHC.class.II.cell PSC.cell
#> 3 22
#> unclassified.endocrine.cell
#> 16
actualFrac.diabetes <- actualFrac.diabetes / sum(actualFrac.diabetes)
actualFrac.diabetes <- c(actualFrac.diabetes, 0) * 100
#Heirarchical Deconvolution
cellEst.d.top100.hier <- ADAPTS::hierarchicalClassify(sigMatrix = sigMat1, toPred = pb.d.df, geneExpr = allSCdata, hierarchData = hier.top100, method=method)
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
cellEst.d.augment.hier <- ADAPTS::hierarchicalClassify(sigMatrix = topAug.var100$sigMatrix, toPred = pb.d.df, geneExpr = allSCdata, hierarchData = hier.augment, method=method)
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!
#> missForest iteration 1 in progress...done!
#> missForest iteration 2 in progress...done!deconTable.d.aug <- data.frame(top100.d=cellEst.d.top100, top100.d.hier=cellEst.d.top100.hier, augmented.d=cellEst.d.augment, augmented.d.hier=cellEst.d.augment.hier, ref.d=actualFrac.diabetes)
colnames(deconTable.d.aug) <- c('top.d', 'top.d.hier', 'aug.d', 'aug.d.hier', 'ref.d')
cors.d.aug <- apply(deconTable.d.aug, 2, function(x){cor(x, deconTable.d.aug[,'ref.d'])})
cors.d.aug.spear <- apply(deconTable.d.aug, 2, function(x){cor(x, deconTable.d.aug[,'ref.d'], method='spearman')})
rmses.d.aug <- apply(deconTable.d.aug, 2, function(x){sqrt(mean((x-deconTable.d.aug[,'ref.d'])^2))})
deconTableP.d.aug <- as.data.frame(t(deconTable.d.aug))
deconTableP.d.aug$RMSE <- rmses.d.aug
deconTableP.d.aug$rho <- cors.d.aug
deconTableP.d.aug$rho.spear <- cors.d.aug.spear
print('Deconvolution of diabetes samples')
#> [1] "Deconvolution of diabetes samples"
print(round(t(deconTableP.d.aug),2))
#> top.d top.d.hier aug.d aug.d.hier ref.d
#> acinar.cell 7.40 4.32 10.82 8.89 5.78
#> alpha.cell 7.93 9.01 7.94 14.41 36.24
#> beta.cell 8.04 8.44 8.58 9.35 12.39
#> co.expression.cell 12.33 7.95 10.03 8.55 1.68
#> delta.cell 9.38 11.50 8.13 10.93 7.35
#> ductal.cell 5.93 17.06 12.49 8.90 21.74
#> endothelial.cell 0.00 13.80 2.58 7.91 0.53
#> epsilon.cell 6.56 21.36 5.83 12.12 0.21
#> gamma.cell 8.46 0.00 7.61 0.00 9.45
#> mast.cell 0.00 0.00 1.72 8.51 0.32
#> MHC.class.II.cell 0.00 6.56 2.88 5.83 0.32
#> PSC.cell 0.00 0.00 5.93 0.55 2.31
#> unclassified.endocrine.cell 33.97 0.00 15.47 4.05 1.68
#> others 0.00 0.00 0.00 0.00 0.00
#> RMSE 12.76 10.68 9.44 8.91 0.00
#> rho 0.06 0.24 0.35 0.46 1.00
#> rho.spear 0.46 0.22 0.64 0.39 1.00Try Rescaling predictions based on single cell data
dim(ADAPTSdata3::normalData.5061)
#> [1] 26178 1255
nCounts <- colSums(ADAPTSdata3::normalData.5061)
lnCounts <- log(nCounts)
cellTable <- sub('.cell$', '', sub('\\.+[0-9]+$', '', names(nCounts)))
par(mar=c(11,5,1,1))
boxplot(nCounts~cellTable, main='Counts by Cell Type', las=2, cex.lab = 0.65)
Generate the scaling factors
ref <- mean(tapply(nCounts, cellTable, mean))
relExp <- nCounts / ref
aveRelExpr <- tapply(relExp, cellTable, mean)
par(mar=c(6,5,1,1))
plot(aveRelExpr, xaxt = "n", xlab="")
axis(1, at=1:length(aveRelExpr), labels=names(aveRelExpr), las=2, cex.axis=0.5)
Rescale the precitions based on the single cell counts
deconTableP.rescale.all <- deconTableP.all
for(j in 1:4) {
corNames <- paste(names(aveRelExpr), 'cell', sep='.')
x <- deconTableP.rescale.all[,j]
x[corNames] <- x[corNames] / aveRelExpr
x[corNames] <- 100 * x[corNames] / sum(x[corNames])
x['rho'] <- cor(x[corNames], deconTableP.rescale.all[corNames,'ref'])
x['RMSE'] <- sqrt(mean((x[corNames] - deconTableP.rescale.all[corNames,'ref'])^2))
deconTableP.rescale.all[,j] <- x
}
print(round(deconTableP.rescale.all,2))
#> top top.hier aug aug.hier ref
#> acinar.cell 9.40 7.33 9.76 9.15 10.36
#> ductal.cell 8.45 10.20 9.26 15.32 43.11
#> alpha.cell 7.76 9.28 8.88 10.47 12.11
#> gamma.cell 8.87 6.65 7.53 6.84 1.83
#> beta.cell 4.94 9.19 5.45 7.91 3.51
#> co.expression.cell 3.51 15.34 9.47 7.16 14.26
#> delta.cell 0.00 10.18 1.93 6.26 0.88
#> unclassified.endocrine.cell 7.13 23.59 6.35 13.19 0.40
#> endothelial.cell 11.01 0.00 10.27 0.00 8.53
#> PSC.cell 0.00 0.00 2.73 11.30 0.32
#> epsilon.cell 0.00 8.25 2.86 6.74 0.16
#> mast.cell 0.00 0.00 7.08 0.98 2.55
#> MHC.class.II.cell 38.93 0.00 18.45 4.67 1.99
#> others 0.00 0.00 0.00 0.00 0.00
#> RMSE 14.71 12.18 10.95 10.04 0.00
#> rho 0.03 0.15 0.26 0.46 1.00
#> rho.spear 0.50 0.31 0.69 0.37 1.00Repeat with blind predictions.
deconTableP.d.rescale.all <- t(deconTableP.d.aug)
for(j in 1:4) {
corNames <- paste(names(aveRelExpr), 'cell', sep='.')
x <- deconTableP.d.rescale.all[,j]
x[corNames] <- x[corNames] / aveRelExpr
x[corNames] <- 100 * x[corNames] / sum(x[corNames])
x['rho'] <- cor(x[corNames], deconTableP.d.rescale.all[corNames,'ref.d'])
x['RMSE'] <- sqrt(mean((x[corNames] - deconTableP.d.rescale.all[corNames,'ref.d'])^2))
deconTableP.d.rescale.all[,j] <- x
}
print(round(deconTableP.d.rescale.all,2))
#> top.d top.d.hier aug.d aug.d.hier ref.d
#> acinar.cell 6.68 3.53 9.38 7.48 5.78
#> alpha.cell 9.03 9.28 8.68 15.27 36.24
#> beta.cell 6.11 5.81 6.26 6.61 12.39
#> co.expression.cell 10.87 6.34 8.49 7.01 1.68
#> delta.cell 7.56 8.39 6.29 8.20 7.35
#> ductal.cell 7.48 19.48 15.13 10.45 21.74
#> endothelial.cell 0.00 17.96 3.56 10.59 0.53
#> epsilon.cell 7.49 22.07 6.39 12.89 0.21
#> gamma.cell 7.04 0.00 6.08 0.00 9.45
#> mast.cell 0.00 0.00 2.10 10.06 0.32
#> MHC.class.II.cell 0.00 7.15 3.33 6.54 0.32
#> PSC.cell 0.00 0.00 7.81 0.71 2.31
#> unclassified.endocrine.cell 37.74 0.00 16.50 4.18 1.68
#> others 0.00 0.00 0.00 0.00 0.00
#> RMSE 13.68 11.53 9.70 9.31 0.00
#> rho 0.03 0.17 0.32 0.41 1.00
#> rho.spear 0.46 0.22 0.64 0.39 1.00These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.