library(TrendCatcher)
demo.master.list.path<-system.file("extdata", "BrainMasterList.rda", package = "TrendCatcher") load(demo.master.list.path)
The demo master table is using gene ENSEMBL as row name. For input CSV file using ENSEMBL ID as row name. TrendCatcher provides some easy manipulation functions to add gene SYMBOL column into the master.table.
### In case bioMart has connection issue, you can load from example data #gene.symbol.df<-get_GeneEnsembl2Symbol(ensemble.arr = master.list$master.table$Gene) #master.table.new<-cbind(master.list$master.table, gene.symbol.df[match(master.list$master.table$Gene, gene.symbol.df$Gene), c("Symbol", "description")]) #master.list$master.table<-master.table.new #head(master.list$master.table) demo.master.list.path<-system.file("extdata", "BrainMasterList_Symbol.rda", package = "TrendCatcher") load(demo.master.list.path) head(master.list$master.table)
If the input CSV file using GENE SYMBOL as row name. Just simply add Symbol column to the master.table. Because some function requires the Symbol column.
### ONLY use this command if CSV file is using GENE SYMBOL as row name!!!!!! #master.list$master.table$Symbol<-master.list$master.table$Gene
To look at each single gene trajectory and fitted count. We use function draw_GeneTraj.
gene.symbol.arr<-unique(master.list$master.table$Symbol)[1:6] p<-draw_GeneTraj(master.list = master.list, gene.symbol.arr = gene.symbol.arr, ncol = 3, nrow = 2) p
draw_TrajClusterGrid(master.list = master.list, min.traj.n = 10)
Use hierarchical pie chart to visualize gene trajectory master-pattern and sub-pattern composition. This is useful when comparing two or more projects.
#par(mar=c(1,1,1,1)) #draw_TrajClusterPie(master.list = master.list,inner.radius = 0.7, cex.out = 1, cex.in = 1, fig.title = "Hierarchical Pie Chart")
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