diff --git a/R/MetaDataAnalysis.R b/R/MetaDataAnalysis.R index fae1679..8b4dee6 100644 --- a/R/MetaDataAnalysis.R +++ b/R/MetaDataAnalysis.R @@ -91,11 +91,11 @@ MetaAnalysis <- function(InputData, # Extract loadings for each PC PCA.res_Loadings <- as.data.frame(PCA.res$rotation)%>% - rownames_to_column("FeatureID") + tibble::rownames_to_column("FeatureID") #--- 2. Merge with demographics PCA.res_Info <- merge(x=SettingsFile_Sample%>%rownames_to_column("UniqueID") , y=PCA.res_Info%>%rownames_to_column("UniqueID"), by="UniqueID", all.y=TRUE)%>% - column_to_rownames("UniqueID") + tibble::column_to_rownames("UniqueID") #--- 3. convert columns that are not numeric to factor: ## Demographics are often non-numerical, categorical explanatory variables, which is often stored as characters, sometimes integers @@ -149,7 +149,7 @@ MetaAnalysis <- function(InputData, #Add explained variance into the table: prop_var_ex <- as.data.frame(((PCA.res[["sdev"]])^2/sum((PCA.res[["sdev"]])^2))*100)%>%#To compute the proportion of variance explained by each component in percent, we divide the variance by sum of total variance and multiply by 100(variance=standard deviation ^2) - rownames_to_column("PC")%>% + tibble::rownames_to_column("PC")%>% mutate(PC = paste("PC", PC, sep=""))%>% dplyr::rename("Explained_Variance"=2) @@ -243,7 +243,7 @@ MetaAnalysis <- function(InputData, select(term, PC, Explained_Variance) Data_Heat <- reshape2::dcast( Data_Heat, term ~ PC, value.var = "Explained_Variance")%>% - column_to_rownames("term")%>% + tibble::column_to_rownames("term")%>% mutate_all(~replace(., is.na(.), 0)) #Plot @@ -325,11 +325,11 @@ MetaPK <- function(InputData, if(is.null(SettingsInfo)==TRUE){ MetaData <- names(SettingsFile_Sample) SettingsFile_Sample <- SettingsFile_Sample%>% - rownames_to_column("SampleID") + tibble::rownames_to_column("SampleID") }else{ MetaData <- SettingsInfo SettingsFile_Sample_subset <- SettingsFile_Sample[, MetaData, drop = FALSE]%>% - rownames_to_column("SampleID") + tibble::rownames_to_column("SampleID") } # Convert into a pathway DF diff --git a/vignettes/Metadata Analysis.Rmd b/vignettes/Metadata Analysis.Rmd index 26daf91..3b04361 100644 --- a/vignettes/Metadata Analysis.Rmd +++ b/vignettes/Metadata Analysis.Rmd @@ -59,6 +59,7 @@ library(magrittr) library(dplyr) library(rlang) library(tidyr) +library(tibble) #Please install the Biocmanager Dependencies: #BiocManager::install("clusterProfiler") @@ -263,16 +264,16 @@ Here we can also use the `MetaproViz::VizVolcano()` function to plot comparisons ```{r} #Early versus Late Stage MetaProViz::VizVolcano(PlotSettings="Compare", - InputData=ResList[["EarlyStage"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), - InputData2= ResList[["LateStage"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), + InputData=ResList[["EarlyStage"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), + InputData2= ResList[["LateStage"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), ComparisonName= c(InputData="EarlyStage", InputData2= "LateStage"), PlotName= "EarlyStage-TUMOR_vs_NORMAL compared to LateStage-TUMOR_vs_NORMAL", Subtitle= "Results of DMA" ) # Young versus Old MetaProViz::VizVolcano(PlotSettings="Compare", - InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), - InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), + InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), + InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), ComparisonName= c(InputData="Young", InputData2= "Old"), PlotName= "Young-TUMOR_vs_NORMAL compared to Old-TUMOR_vs_NORMAL", Subtitle= "Results of DMA" ) @@ -301,14 +302,14 @@ ggVennDiagram::ggVennDiagram(list(Top = top_entries, MetaData_Metab <- merge(x=Tissue_MetaData, - y= MetaRes[["res_summary"]][, c(1,5:6) ]%>%column_to_rownames("FeatureID"), + y= MetaRes[["res_summary"]][, c(1,5:6) ]%>%tibble::column_to_rownames("FeatureID"), by=0, all.y=TRUE)%>% column_to_rownames("Row.names") #Make a Volcano plot: MetaProViz::VizVolcano(PlotSettings="Standard", - InputData=ResList[["TissueType"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), + InputData=ResList[["TissueType"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), SettingsFile_Metab = MetaData_Metab, SettingsInfo = c(color = "MainDriver_Term"), PlotName= "TISSUE_TYPE-TUMOR_vs_NORMAL", @@ -347,14 +348,14 @@ Now we can use this information to colour code our volcano plot: ```{r, eval=FALSE} #Add metabolite information such as KEGG ID or pathway to results MetaData_Metab <- merge(x=Tissue_MetaData, - y= MCAres[["MCA_2Cond_Results"]][, c(1, 14:15)]%>%column_to_rownames("Metabolite"), + y= MCAres[["MCA_2Cond_Results"]][, c(1, 14:15)]%>%tibble::column_to_rownames("Metabolite"), by=0, all.y=TRUE)%>% - column_to_rownames("Row.names") + tibble::column_to_rownames("Row.names") MetaProViz::VizVolcano(PlotSettings="Compare", - InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), - InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), + InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), + InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), ComparisonName= c(InputData="Young", InputData2= "Old"), SettingsFile_Metab = MetaData_Metab, PlotName= "Young-TUMOR_vs_NORMAL compared to Old-TUMOR_vs_NORMAL", @@ -367,14 +368,14 @@ MetaProViz::VizVolcano(PlotSettings="Compare", ```{r, echo=FALSE, warning=FALSE, fig.align="left", fig.width=7, fig.height=5} #Add metabolite information such as KEGG ID or pathway to results MetaData_Metab <- merge(x=Tissue_MetaData, - y= MCAres[["MCA_2Cond_Results"]][, c(1, 14:15)]%>%column_to_rownames("Metabolite"), + y= MCAres[["MCA_2Cond_Results"]][, c(1, 14:15)]%>%tibble::column_to_rownames("Metabolite"), by=0, all.y=TRUE)%>% - column_to_rownames("Row.names") + tibble::column_to_rownames("Row.names") MetaProViz::VizVolcano(PlotSettings="Compare", - InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), - InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%column_to_rownames("Metabolite"), + InputData=ResList[["Young"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), + InputData2= ResList[["Old"]][["DMA"]][["TUMOR_vs_NORMAL"]]%>%tibble::column_to_rownames("Metabolite"), ComparisonName= c(InputData="Young", InputData2= "Old"), SettingsFile_Metab = MetaData_Metab, PlotName= "Young-TUMOR_vs_NORMAL compared to Old-TUMOR_vs_NORMAL", @@ -393,12 +394,12 @@ LoadKEGG() comparisons <- names(ResList) for(comparison in comparisons){ #Ensure that the Metabolite names match with KEGG IDs or KEGG trivial names. - DMA <- merge(Tissue_MetaData%>%rownames_to_column("Metabolite") ,ResList[[comparison]][["DMA"]][["TUMOR_vs_NORMAL"]], by="Metabolite", all.y=TRUE) + DMA <- merge(Tissue_MetaData%>%tibble::rownames_to_column("Metabolite") ,ResList[[comparison]][["DMA"]][["TUMOR_vs_NORMAL"]], by="Metabolite", all.y=TRUE) DMA <- DMA[,c(1,10,12:17)] DMA <- DMA[complete.cases(DMA),-1]#we remove metabolites that do not have a KEGG ID/KEGG pathway #remove dublons - DMA_Select <- DMA%>%distinct(KEGG, .keep_all = TRUE)%>%remove_rownames()%>%column_to_rownames("KEGG") + DMA_Select <- DMA%>%distinct(KEGG, .keep_all = TRUE)%>%remove_rownames()%>%tibble::column_to_rownames("KEGG") #Perform ORA DM_ORA_res[[comparison]] <- MetaProViz::StandardORA(InputData= DMA_Select, #Input data requirements: column `t.val` and column `Metabolite`