2. Analyze trajectories of DDEGs.

1. Load TrendCatcher R package.

library(TrendCatcher)

2. Now read the demo master.list.

demo.master.list.path<-system.file("extdata", "BrainMasterList.rda", package = "TrendCatcher")
load(demo.master.list.path)

3. ID convention.

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)

##                    Gene  pattern start.idx end.idx     dynTime  dynSign start.t
## 1330 ENSMUSG00000025283 up_down_      1_4_    4_6_ 6_24_48_72_ +_+_+_+_   0_48_
## 1857 ENSMUSG00000028967 up_down_      1_2_    2_6_    6_24_48_   +_+_+_    0_6_
## 2886 ENSMUSG00000039236 up_down_      1_2_    2_6_       6_24_     +_+_    0_6_
## 4179 ENSMUSG00000078920 up_down_      1_2_    2_6_    6_24_48_   +_+_+_    0_6_
## 4437 ENSMUSG00000105987 up_down_      1_4_    4_6_ 6_24_48_72_ +_+_+_+_   0_48_
## 973  ENSMUSG00000022221 up_down_      1_3_    3_6_ 6_24_48_72_ +_+_+_+_   0_24_
##        end.t         pattern_str dyn.p.val dyn.p.val.adj   Symbol
## 1330 48_168_ 0h_up_48h_down_168h  1.11e-16  4.615657e-13     Sat1
## 1857  6_168_  0h_up_6h_down_168h  1.11e-16  4.615657e-13   Errfi1
## 2886  6_168_  0h_up_6h_down_168h  1.11e-16  4.615657e-13    Isg20
## 4179  6_168_  0h_up_6h_down_168h  1.11e-16  4.615657e-13    Ifi47
## 4437 48_168_ 0h_up_48h_down_168h  2.22e-16  7.385052e-13 AI506816
## 973  24_168_ 0h_up_24h_down_168h  3.33e-16  9.231315e-13    Ripk3
##                                                                             description
## 1330      spermidine/spermine N1-acetyl transferase 1 [Source:MGI Symbol;Acc:MGI:98233]
## 1857             ERBB receptor feedback inhibitor 1 [Source:MGI Symbol;Acc:MGI:1921405]
## 2886                  interferon-stimulated protein [Source:MGI Symbol;Acc:MGI:1928895]
## 4179            interferon gamma inducible protein 47 [Source:MGI Symbol;Acc:MGI:99448]
## 4437                    expressed sequence AI506816 [Source:MGI Symbol;Acc:MGI:2140929]
## 973  receptor-interacting serine-threonine kinase 3 [Source:MGI Symbol;Acc:MGI:2154952]

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 

4. Plot Individual Gene Trajectory

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

#### 5. Plot Gene Trajectories grouped by their sub-type trajectory pattern.

draw_TrajClusterGrid(master.list = master.list, min.traj.n = 10)

6. Plot Gene Trajectory Composition.

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")