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comparison galaxy/asca_wrapper.R @ 1:20395c0079ae draft

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author marie-tremblay-metatoul
date Mon, 30 Jul 2018 07:47:12 -0400
parents c5f11e6f8f99
children
comparison
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0:c5f11e6f8f99 1:20395c0079ae
162 eigenvalues <- data.frame(1:length(unique(design[,1])), result[[1]]$'1'$svd$var.explained[1:length(unique(design[,1]))]) 162 eigenvalues <- data.frame(1:length(unique(design[,1])), result[[1]]$'1'$svd$var.explained[1:length(unique(design[,1]))])
163 colnames(eigenvalues) <- c("PC", "explainedVariance") 163 colnames(eigenvalues) <- c("PC", "explainedVariance")
164 barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component") 164 barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")
165 noms <- levels(as.factor(samDF[, listArguments$factor1])) 165 noms <- levels(as.factor(samDF[, listArguments$factor1]))
166 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="1", interaction=0, factorName=listArguments$factor1, factorModalite=noms) 166 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="1", interaction=0, factorName=listArguments$factor1, factorModalite=noms)
167 Date.loadings <- data.matrix(result[[5]][,2:3]) 167 f1.loadings <- data.matrix(result[[5]][,2:3])
168 Date.loadings.leverage <- diag(Date.loadings%*%t(Date.loadings)) 168 f1.loadings.leverage <- diag(f1.loadings%*%t(f1.loadings))
169 names(Date.loadings.leverage) <- colnames(xMN) 169 names(f1.loadings.leverage) <- colnames(xMN)
170 Date.loadings.leverage <- sort(Date.loadings.leverage, decreasing=TRUE) 170 f1.loadings.leverage <- sort(f1.loadings.leverage, decreasing=TRUE)
171 barplot(Date.loadings.leverage[Date.loadings.leverage > 0.001], main="PC1 loadings") 171 barplot(f1.loadings.leverage[f1.loadings.leverage > 0.001], main="PC1 loadings")
172 } 172 }
173 if (data.asca.permutation[2] < as.numeric(listArguments[["threshold"]])) 173 if (data.asca.permutation[2] < as.numeric(listArguments[["threshold"]]))
174 { 174 {
175 eigenvalues <- data.frame(1:length(unique(design[,2])), result[[1]]$'2'$svd$var.explained[1:length(unique(design[,2]))]) 175 eigenvalues <- data.frame(1:length(unique(design[,2])), result[[1]]$'2'$svd$var.explained[1:length(unique(design[,2]))])
176 colnames(eigenvalues) <- c("PC", "explainedVariance") 176 colnames(eigenvalues) <- c("PC", "explainedVariance")
177 barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component") 177 barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")
178 noms <- levels(as.factor(samDF[, listArguments$factor2])) 178 noms <- levels(as.factor(samDF[, listArguments$factor2]))
179 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="2", interaction=0, factorName=listArguments$factor2, factorModalite=noms) 179 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="2", interaction=0, factorName=listArguments$factor2, factorModalite=noms)
180 Date.loadings <- data.matrix(result[[5]][,4:5]) 180 f2.loadings <- data.matrix(result[[5]][,4:5])
181 Date.loadings.leverage <- diag(Date.loadings%*%t(Date.loadings)) 181 f2.loadings.leverage <- diag(f2.loadings%*%t(f2.loadings))
182 names(Date.loadings.leverage) <- colnames(xMN) 182 names(f2.loadings.leverage) <- colnames(xMN)
183 Date.loadings.leverage <- sort(Date.loadings.leverage, decreasing=TRUE) 183 f2.loadings.leverage <- sort(f2.loadings.leverage, decreasing=TRUE)
184 barplot(Date.loadings.leverage[Date.loadings.leverage > 0.001], main="PC1 loadings") 184 barplot(f2.loadings.leverage[f2.loadings.leverage > 0.001], main="PC1 loadings")
185 } 185 }
186 if (data.asca.permutation[3] < as.numeric(listArguments[["threshold"]])) 186 if (data.asca.permutation[3] < as.numeric(listArguments[["threshold"]]))
187 { 187 {
188 eigenvalues <- data.frame(1:(length(unique(design[,1]))*length(unique(design[,2]))), result[[1]]$'12'$svd$var.explained[1:(length(unique(design[,1]))*length(unique(design[,2])))]) 188 eigenvalues <- data.frame(1:(length(unique(design[,1]))*length(unique(design[,2]))), result[[1]]$'12'$svd$var.explained[1:(length(unique(design[,1]))*length(unique(design[,2])))])
189 colnames(eigenvalues) <- c("PC", "explainedVariance") 189 colnames(eigenvalues) <- c("PC", "explainedVariance")
191 noms1 <- data.matrix(levels(as.factor(samDF[, listArguments$factor1]))) 191 noms1 <- data.matrix(levels(as.factor(samDF[, listArguments$factor1])))
192 noms2 <- data.matrix(levels(as.factor(samDF[, listArguments$factor2]))) 192 noms2 <- data.matrix(levels(as.factor(samDF[, listArguments$factor2])))
193 noms <- apply(noms1, 1, FUN=function(x){paste(x, "-", noms2, sep="")}) 193 noms <- apply(noms1, 1, FUN=function(x){paste(x, "-", noms2, sep="")})
194 noms <- apply(noms, 1, FUN=function(x){c(noms)}) 194 noms <- apply(noms, 1, FUN=function(x){c(noms)})
195 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="12", interaction=1, factorModalite=noms[,1]) 195 ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="12", interaction=1, factorModalite=noms[,1])
196 Date.loadings <- data.matrix(result[[5]][,6:7]) 196 f1f2.loadings <- data.matrix(result[[5]][,6:7])
197 Date.loadings.leverage <- diag(Date.loadings%*%t(Date.loadings)) 197 f1f2.loadings.leverage <- diag(f1f2.loadings%*%t(f1f2.loadings))
198 names(Date.loadings.leverage) <- colnames(xMN) 198 names(f1f2.loadings.leverage) <- colnames(xMN)
199 Date.loadings.leverage <- sort(Date.loadings.leverage, decreasing=TRUE) 199 f1f2.loadings.leverage <- sort(f1f2.loadings.leverage, decreasing=TRUE)
200 barplot(Date.loadings.leverage[Date.loadings.leverage > 0.001], main="PC1 loadings") 200 barplot(f1f2.loadings.leverage[f1f2.loadings.leverage > 0.001], main="PC1 loadings")
201 } 201 }
202 dev.off() 202 dev.off()
203 } 203 }
204 204
205 tryCatch({ 205 tryCatch({