<!--
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{DEGReport}
%\VignetteKeywords{DifferentialExpression, Visualization, RNASeq, ReportWriting}
-->

```{r}
library(DEGreport)
data(humanSexDEedgeR)
library(edgeR)
```
We are going to do a differential expression analysis with edgeR. We have an object that is comming from the edgeR package. It countains a gene count matrix for 85 TSI HapMap individuals, and the gender information. With that, we are going to apply the `glmFit` function to get genes differentially expressed between males and females.

```{r}
des<-humanSexDEedgeR$design
fit <- glmFit(humanSexDEedgeR,des)
lrt <- glmLRT(fit)
tab<-cbind(lrt$table,p.adjust(lrt$table$PValue,method="BH"))
detags <- rownames(tab[tab[,5]<=0.1,])
plotSmear(humanSexDEedgeR, de.tags=detags)
```
We need to extract the experiment design data.frame where the condition is 
Male or Female.
```{r}
counts<-cpm(humanSexDEedgeR,log=FALSE)
g1<-colnames(counts)[1:41]
g2<-colnames(counts)[42:85]
design<-data.frame(condition=sub("1","Male",sub("0","Female",des[,2])))

```
We are getting the chromosome information for each gene. This way we can colour
genes according autosomic,X or Y chromosomes.

```{r}
library(biomaRt)
mart = useMart("ensembl", dataset="hsapiens_gene_ensembl")
g = getGene( id = detags, type = "ensembl_gene_id", mart = mart)
colors=data.frame(genes=g$ensembl_gene_id,colors=sub("[1-9]{1,2}","Autosomic",g$chromosome_name))
```
Create the report. The main parameters are the name columns in group1, and group2. Then, the count matrix, gene names that are DE, p-values, fold changes and path to create the report. As optional, you can give colours for each gene, and the population size used for permutation.

```{r}
#un comment the las code to create the html
detag10<-detags[1:10]
pval<-tab[,4]
fc<-tab[detag10,1]
```
Run the following lines to create the file

```{r eval=FALSE}

pathreport<-"~/report"
createReport(g1,g2,counts,detag10,pval,fc,pathreport,colors,pop=400)
```

Run the following lines if you want to visualize your expression values by 
condition

```{r eval=FALSE}
degObj(counts,design,"/tmp/degObj.rda")
library(shiny)
runGist(9930881)

```

You can use individual functions, like degRank or degMean. This will 
create specific figures and tables showed in the report. 

```{r dev='jpeg'}
degMean(pval,counts)
degVar(pval,counts)
degMV(g1,g2,pval,counts)
degMB(detags,g1,g2,counts)
degVB(detags,g1,g2,counts)
rank<-degRank(g1,g2,counts[detag10,],fc,400,500)
degPR(rank)
```