diff --git a/docs/articles/Visualization.html b/docs/articles/Visualization.html index 327bb07..ba9ac6c 100644 --- a/docs/articles/Visualization.html +++ b/docs/articles/Visualization.html @@ -71,39 +71,40 @@

Leafcutter Shiny App

Written by Jack Humphrey, David Knowles & Yang Li

-

To see an example of the leafcutter shiny app in action without installing anything take a look at https://leafvis.shinyapps.io/leafvis/. This shows leafcutter differential splicing results for a comparison of 10 brain vs. 10 heart samples (5 male, 5 female in each group) from GTEx.

-

The LeafCutter Shiny app has been tested on macOS 10.12 and Ubuntu 14.04.

+

To see an example of the leafcutter shiny app in action without installing anything take a look at https://leafvis.shinyapps.io/leafvis/. This shows leafcutter differential splicing results for a comparison of 10 brain vs. 10 heart samples (5 male, 5 female in each group) from GTEx.

+

The leafcutter shiny app has been tested on macOS 10.12 and Ubuntu 14.04.

Example

-

If you’ve already installed leafcutter then you can easily fire up a local shiny app on the same GTEx example data. Navigate into the leafvis directory:

+

If you’ve already installed leafcutter then you can easily fire up a local shiny app on the same GTEx example data. Navigate into the leafvis directory:

cd leafvis

and then download the example data using

./download_example.sh
-

This will download and unzip a folder called ‘example’. Now starting the shiny app should be as easy as

+

This will download and unzip a folder called example. Now starting the shiny app should be as easy as

./run_leafvis.R example/Brain_vs_Heart_results.Rdata
-

You can even leave out the ‘example/Brain_vs_Heart_results.Rdata’ part as this is the default dataset ‘run_leafvis.R’ will try to use.

+

You can even leave out the example/Brain_vs_Heart_results.Rdata part as this is the default dataset run_leafvis.R will try to use.

Annotation code

The Shiny app includes functionality to label detected introns as annotated or cryptic. To do for a new dataset this an “annotation code” is needed, built from a “Gene Transfer Format” file appropriate for your genome. We provide pre-built annotation codes for hg19 and hg38 which can be downloaded by running

./download_human_annotation_codes.sh
-

from the ‘leafvis’ directory. To build a new annotation code you need:

+

from the leafvis directory. To build a new annotation code you need:

-

On Mac you can install bedtools and GNU Awk using homebrew:

+

On Mac you can install bedtools and GNU Awk using homebrew:

brew install gawk; brew install bedtools

-

Most Linuxes will have ‘gawk’ already. On Ubuntu ‘bedtools’ can be installed using

+

Most Linuxes will have gawk already. On Ubuntu bedtools can be installed using

sudo apt-get install bedtools

Required R packages

-

These should all have been installed when you installed LeafCutter:

+

These should all have been installed when you installed leafcutter:

For details please see our bioRxiv preprint

+ +

Check out a demo leafcutter shiny app here: 10 brain vs. 10 heart samples from GTEx.

diff --git a/docs/reference/index.html b/docs/reference/index.html index 00288c6..a97a97c 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -88,7 +88,7 @@

Reference - version 1.0 + version 0.2

diff --git a/leafcutter/DESCRIPTION b/leafcutter/DESCRIPTION index 43b341b..c76bc5b 100644 --- a/leafcutter/DESCRIPTION +++ b/leafcutter/DESCRIPTION @@ -2,7 +2,7 @@ Package: leafcutter Type: Package Title: Alternative splicing quantification, differential splicing and splicing QTL mapping -Version: 1.0 +Version: 0.2 Date: 2016-11-19 Authors@R: c(person("David", "Knowles", email = "knowles84@gmail.com", role = c("aut", "cre")), person("Yang", "Li", role = c("aut", "cre")), diff --git a/leafcutter/README.md b/leafcutter/README.md index 1fd895a..cb24739 100644 --- a/leafcutter/README.md +++ b/leafcutter/README.md @@ -11,3 +11,10 @@ Leafcutter quantifies RNA splicing variation using short-read RNA-seq data. The * simple, computationally efficient algorithms scaling to 100s or even 1000s of samples For details please see our [bioRxiv preprint](http://biorxiv.org/content/early/2016/03/16/044107) + +* [Installation](./articles/Installation.html) +* [Differential splicing](./articles/Installation.html) +* [Visualization](./articles/Visualization.html) +* [SplicingQTL](./articles/sQTL.html) + +Check out a demo leafcutter [shiny](https://shiny.rstudio.com/) app [here](https://leafvis.shinyapps.io/leafvis/): 10 brain vs. 10 heart samples from [GTEx](https://www.gtexportal.org/home/).