# Titre de niveau 1
## Titre de niveau 2
### Titre de niveau 3
#### Titre de niveau 4

*Un texte en italique*
_Un texte aussi en italique_

**Un texte en gras**
__Un texte aussi en gras__

* Item 1
* Item 2
  * Item 2.1
  * Item 2.2

1. Item 1
2. Item 2
3. Item 3

![Github](adresse/de/l'image)

[Github](https://github.com/)

Mireille Sitbon a dit : 
> L'informatique, ça fait gagner beaucoup de temps... 
> à condition d'en avoir beaucoup devant soi !

- [ ] Tache 1
- [ ] Tache 2
- [x] Tache 3

Colonne 1 | Colonne 2
----------|----------
1         |2
3         |4    

python3 -m pip install --upgrade pip
python3 -m pip install jupyter

conda install -c anaconda jupyter 

jupyter notebook

docker run --rm -p 8888:8888 -p 4040:4040 -v ${PWD}:/home/jovyan/work jupyter/all-spark-notebook

Executing the command: jupyter notebook
[I 08:31:13.762 NotebookApp] Writing notebook server cookie secret to /home/jovyan/.local/share/jupyter/runtime/notebook_cookie_secret
[I 08:31:14.645 NotebookApp] JupyterLab extension loaded from /opt/conda/lib/python3.7/site-packages/jupyterlab
[I 08:31:14.645 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 08:31:14.647 NotebookApp] Serving notebooks from local directory: /home/jovyan
[I 08:31:14.647 NotebookApp] The Jupyter Notebook is running at:
[I 08:31:14.648 NotebookApp] http://(716ae9fc149a or 127.0.0.1):8888/?token=2996432e3056fc62c38bd40d135824a5d586f59cc244c6f9
[I 08:31:14.648 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 08:31:14.653 NotebookApp]

    To access the notebook, open this file in a browser:
        file:///home/jovyan/.local/share/jupyter/runtime/nbserver-6-open.html
    Or copy and paste one of these URLs:
        http://(716ae9fc149a or 127.0.0.1):8888/?token=2996432e3056fc62c38bd40d135824a5d586f59cc244c6f9

FROM rocker/binder

## Copies your repo files into the Docker Container
USER root
COPY . ${HOME}
RUN chown -R ${NB_USER} ${HOME}

## Become normal user again
USER ${NB_USER}

docker run --rm -p 8888:8888 -p 4040:4040 -e JUPYTER_ENABLE_LAB=yes -v ${PWD}:/home/jovyan/work jupyter/all-spark-notebook

python3 -m pip install jupyterlab
jupyter lab

conda install -c conda-forge jupyterlab
jupyter lab

docker run --rm -d -p 80:8888 --name fair_bioinfo -v ${PWD}:/home/rstudio tdenecker/fair_bioinfo

---
title: "R Notebook"
output:
  html_document:
    df_print: paged
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
plot(cars)
```

```{r echo=FALSE}
plot(cars)
```

```{r results="hide"}
plot(cars)
```

```{r warning=FALSE, message=FALSE}
plot(cars)
```

downloadButton("reportBTN", "Generate report")

output$reportBTN <- downloadHandler(
    filename = paste0("report_",data$date,".html"),
    content = function(file) {
      params <- list(si = si,
                     date = data$date,
                     dataCondition = data$conditions,
                     dataCountTable = data$countTable,
                     dataSummary = data$summary,
                     colorColConds = color$colConds,
                     deseqRV_resDESeq = deseqRV$resDESeq, 
                     logFC= input$logFC,
                     pvalue= input$pvalue,
                     tableParams=  c(ColHisto = input$colHisto,
                                     ColCondA = input$colCondA,
                                     ColCondB = input$colCondB,
                                     fitType = input$fitType,
                                     pvalue = input$pvalue,
                                     logFC= input$logFC,
                                     pAdjustMethod = input$pAdjustMethod))
      rmarkdown::render("report.Rmd", output_file = file,
                        params = params,
                        envir = new.env(parent = globalenv())
      )
    }
  )

---
title: "FAIR_Bioinfo analysis"
params:
  dataCondition: NA
  si: NA
  colorColConds: NA
  dataSummary: NA
  dataCountTable : NA
  deseqRV_resDESeq: NA
  pvalue: NA
  logFC: NA
  tableParams: NA
  date: NA
  
output:
  html_document:
    df_print: paged
---

# Description of raw data

The objective of this application is to find the differentially expressed genes after using the FAIR_Bioinfo workflow

## Conditions

The count data files and associated biological conditions are listed in the following table :

```{r echo = F}
params$dataCondition
```


## Count table

After loading the data we first have a look at the raw data table itself. The data table contains one row per annotated feature and one column per sequenced sample. Row names of this table are feature IDs (unique identifiers). The table contains raw count values representing the number of reads that map onto the features. For this project, there are 7659 features in the count data table.

```{r echo = F}
params$dataCountTable
```

Looking at the summary of the count table provides a basic description of these raw counts (min and max values, median, etc).

```{r echo = F}
params$dataSummary
```

## Total read count per sample
Next figure shows the total number of mapped reads for each sample. Reads that map on multiple locations on the transcriptome are counted more than once, as far as they are mapped on less than 50 different loci. We expect total read counts to be similar within conditions, they may be different across conditions. Total counts sometimes vary widely between replicates. This may happen for several reasons, including:

- different rRNA contamination levels between samples (even between biological replicates);
- slight differences between library concentrations, since they may be difficult to measure with high precision.;

```{r echo = F}
    barplot(colSums(params$dataCountTable), ylab = "Total read count per sample",
            main = "Total read count", col = params$colorColConds,
            names = colnames(params$dataCountTable))
```

[...]

## Volcano plot


```{r echo = F}
inter = cbind(x = params$deseqRV_resDESeq$log2FoldChange,
              y = -log10(params$deseqRV_resDESeq$padj), 
              feature = rownames(params$deseqRV_resDESeq), 
              SE = params$deseqRV_resDESeq$lfcSE)
    inter = na.omit(inter)
    inter = as.data.frame(inter)
    inter[,1] = as.numeric(as.character(inter[,1]))
    inter[,2] = as.numeric(as.character(inter[,2]))
    
    color = rep("black", nrow(inter))
    pos = which(abs(inter$x) >= params$logFC & inter$y >= -log10(params$pvalue))
    color[pos] = "red"
    
    plot_ly(inter, x = ~x, y = ~y, type = 'scatter', mode = 'markers',
            text = ~paste("Feature: ", feature, '<br>lfcSE:', SE),
            marker = list(color = color)) %>%
      layout(title = 'Volcano plot',
             shapes=list(list(type='line', x0=min(inter$x)-1, x1= max(inter$x)+1, y0=-log10(params$pvalue), y1=-log10(params$pvalue), line=list(dash='dot', width=1)),
                         list(type='line', x0=-params$logFC, x1= -params$logFC, y0=0, y1=max(inter$y), line=list(dash='dot', width=1)),
                         list(type='line', x0=params$logFC, x1= params$logFC, y0=0, y1=max(inter$y), line=list(dash='dot', width=1))),
             yaxis = list(zeroline = FALSE, title= "-log10(adjusted pvalue)"),
             xaxis = list(zeroline = FALSE, title= "log2(fold change)"))
    
```

# Parameters

```{r echo = F}
params$tableParams

inter = cbind(names(params$tableParams),params$tableParams)
colnames(inter) = c("Params", "Values")
write.table( inter, paste0("params_",params$date,".txt"), 
            sep = "\t",
            quote = F, row.names = F)
```

# R session information

The versions of the R software and Bioconductor packages used for this analysis are listed below. It is important to save them if one wants to re-perform the analysis in the same conditions.

```{r echo = F}
params$si
```

h3("Parameters"),
            fileInput("ParamsFile",label = NULL,
                      buttonLabel = "Browse...",
                      placeholder = "No file selected")

observeEvent(input$run, {

[...]

if(!is.null(input$ParamsFile$datapath)){
    data$Params <- read.csv2(input$ParamsFile$datapath,
                             header = T,
                             sep = "\t",
                             quote = ""
    )
    
    colourpicker::updateColourInput(session, "colHisto", value = data$Params[1,2])
    colourpicker::updateColourInput(session, "colCondA", value = data$Params[2,2])
    colourpicker::updateColourInput(session, "colCondB", value = data$Params[3,2])
    updateSelectInput(session, "fitType",  selected = data$Params[4,2])
    updateNumericInput(session, "pvalue", value = data$Params[5,2])
    updateNumericInput(session, "logFC", value = data$Params[6,2])
    updateSelectInput(session, "pAdjustMethod",  selected = data$Params[7,2])
}

[...]

})