differentialAbundance

content/DifferentialAbundance.md

@@ -21,18 +21,17 @@ but this happeds automatically when running the nextflow run nf-core/differentia
 ![nfpull](nextflowpull.png)
 
 
-
 # R Shiny App
 - one of the key outputs from the nf-core/DifferentialAbundance pipeline is an Shiny app.
 - This runs R processes in the background and presents the data as a site.
 - One way to run the app is to kick it off in R studio, to do this you can download the App directory and use the following R code to open it.
 
-### download the app directory to you local pc and use R studio
-***optional*** 
+## Launching the App on your local R Studio
+***option 1***
 
-The easiest way to run your R shiny app is using R studio, using the following code, howover on the day we are going to host the App with the same Nectar VM you have been using.
+The easiest way to run your R shiny app is using R studio.
 
-Firstly download the nf-core/DifferentialAbundance outputs which you have generated to your pc using scp
+Firstly, you will need to download the nf-core/DifferentialAbundance outputs which you have generated to your pc using scp.
 
 ```bash
 IP=[yourIPaddress]
@@ -42,37 +41,133 @@ scp -r workshop@$IP:/home/workshop/workshop/nfDifferentialAbundance/outs/ ~/work
 
 cd ~/workshop_RNAseq/nfDifferentialAbundance/outs
 ```
+Open R studio, and paste the following R code. This also requires installation of a few packages in Rstudio including the ***shinyngs** package. The installation code is hashed out in this case.
 
-Then open Rstudio and copy this code 
+Within R studio, in the top left panel you can paste this code
 ```r
-##To run shiny App Locally
+##To run shiny App
 # run this in R studio
 
-#######################
-
 # install packages
 install.packages("remotes")
 remotes::install_github("pinin4fjords/shinyngs")
 library(remotes)
 library(shinyngs)
 library(markdown)
 
-#######################
-
-# navigate to where the shiny app is, where you have downloaded it to.
-
+# if you have downloaded the app locally then you will need to add your path to the working directory
+# if you downloaded it to the directory described above, this is where it will be  '~/workshop_RNAseq/nfDifferentialAbundance'
 setwd("/Users/danielthomson/workshop_RNAseq/nfDifferentialAbundance/outs/shinyngs_app/SAGC_Workshop_RNAseq")
 
 #####
-esel <- readRDS("data.rds") # your need to navigate to the 'shinyngs_app' directory
+esel <- readRDS("data.rds") # you need to navigate to the 'shinyngs_app' directory
 app <- prepareApp("rnaseq", esel)
 shiny::shinyApp(app$ui, app$server)
-
 ```
 
-# Using Rstudio via Nectar
-***optional***
+## Using Rstudio via Nectar
+***option 2***
+
+You can also run Rstudio from a virual machine. In some cases this is preferable when dealing with large datasets, where you can control the compute resources used. \
+
 | setting | Description                                                                    |
 |------|----------------------------------------------------------------------------------------|
 |Host  |	use the instance IP address, or the hostname [hostname].[project].cloud.edu.au  |
 |Login |	use the username you created in the Configure Application dialog                |
+
+
+you shoulud see the following login page, access with your passoword\
+![Nectar Rstudio](Nectar_Rstudio.png)
+
+
+Within R studio, in the top left panel you can paste this code, making sure the path to the app directory is correct.
+```r
+##To run shiny App
+# run this in R studio
+
+#######################
+# install packages
+#install.packages("remotes")
+#if (!require("BiocManager", quietly = TRUE))
+#  install.packages("BiocManager")
+#BiocManager::install(c("SummarizedExperiment", "GSEABase", "limma"), force = TRUE)
+#devtools::install_github('pinin4fjords/shinyngs', force = TRUE)
+
+library(remotes)
+library(shinyngs)
+library(markdown)
+
+#######################
+
+# navigate to where the shiny app is
+# if you are working on Rstudio running on your nectar instance, then this should be in the 'out' directory from where you ran the nf-core/DiferentialAbundance pipeline
+setwd("/home/workshop/workshop/nfDifferentialAbundance/outs/shinyngs_app/SAGC_Workshop_RNAseq/")
+
+#####
+esel <- readRDS("data.rds") # you need to navigate to the 'shinyngs_app' directory
+app <- prepareApp("rnaseq", esel)
+shiny::shinyApp(app$ui, app$server)
+```
+
+# Interractive RNAseq data analysis
+This previous step is worth the hastle, because it gives you access to the R Shiny App with all your results available.
+
+![](RshinyHomepage.png)
+
+From here, you will see all the parameters you set up in your *nextflow run* kickoff script. And it will give you access to the data analysis.
+
+Once you've made it to this point take some time to navigate around. You will see interactive versions of many key differential expression analysis tools. In the background, it is being run in your Rstudio session.
+
+Now that we have done the combined work of what would take days writing R code for DEseq2 and ggplot2, there is a fair bit to unpack and understand \
+We'll walk through some of the key analysis, results.
+
+![](Rshiny_volcano.png)
+
+
+
+![](RshinyExperimentalData.png)
+You can see from the 'Experimental data' page, all of the information came from the sample sheet provided to nf-core/DifferentialAbudance.
+
+```bash
+cd home/workshop/workshop/nfDifferentialAbundance/
+cat SampleSheet.csv 
+```
+sample,fastq_1,fastq_2,treatment,cellline,condition
+	Acontrol1,,,control,A,Acontrol
+	Acontrol2,,,control,A,Acontrol
+	Acontrol3,,,control,A,Acontrol
+	Acontrol4,,,control,A,Acontrol
+	Atreated1,,,treated,A,Atreated
+	Atreated2,,,treated,A,Atreated
+	Atreated3,,,treated,A,Atreated
+	Atreated4,,,treated,A,Atreated
+	Bcontrol1,,,control,B,Bcontrol
+	Bcontrol2,,,control,B,Bcontrol
+	Bcontrol3,,,control,B,Bcontrol
+	Bcontrol4,,,control,B,Bcontrol
+	Btreated1,,,treated,B,Btreated
+	Btreated2,,,treated,B,Btreated
+	Btreated3,,,treated,B,Btreated
+	Btreated4,,,treated,B,Btreated
+
+```bash
+cat contrasts.csv 
+```
+	id,variable,reference,target
+	cellline,cellline,A,B
+	treatedVScontrol1,condition,Acontrol,Atreated
+	treatedVScontrol2,condition,Bcontrol,Btreated
+
+There is the opportunity to add as many extra columns to this table, which will all provide extra possible comparisons to the app.
+
+![](RowMetadata.png)
+Looking at the 'row metadata' page, you can see that all the gene information comes from the ***gtf*** file which we used.\
+For well annotated genomes like Hg38 (human) there is alot of extra information that can be pulled out.
+```bash
+head -n 10 genome.gtf
+```
+
+```bash
+head -n 10 genome.gtf
+```
+

public/agenda/index.html

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public/categories/feed.xml

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+  <link rel="canonical" href="http://localhost:1313/workshop_nfRNAseq/categories/">
 
 
 
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