Merge pull request #19 from stemangiola/some_edits

Some edits

CONTRIBUTING.md

@@ -0,0 +1,51 @@
+Contributing to TidyTranscriptomics Workshop
+===
+
+:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
+
+The following is a set of guidelines for contributing to this training material on GitHub.
+
+# Table of contents
+
+- [What should I know before I get started?](#what-should-i-know-before-i-get-started)
+- [How can I contribute?](#how-can-i-contribute)
+- [How do I add new content?](#how-do-i-add-new-content)
+- [How is the training material maintained?](#how-is-the-training-material-maintained)
+
+# What should I know before I get started?
+
+This repository contains the files for the TidyTranscriptomics workshop.
+
+By contributing, you agree that we may redistribute your work under [this repository's license](LICENSE.md).
+
+We will address your issues and/or assess your change proposal as promptly as we can.
+
+If you have any questions, you can reach us using the repository Issues.
+
+# How can I contribute?
+
+You can report mistakes or errors, add suggestions, additions, updates or improvements for content. Whatever is your background, there is probably a way to do it: via the GitHub website, via command-line. If you feel it is too much, you can even write it with any text editor and contact us: we will work together to integrate it.
+
+# How is the training material maintained?
+
+## Maintainers
+
+The maintainers are the workshop authors.
+
+They are responsible for making sure issues and change requests are looked at. They have the final say over what is included in the training material.
+
+## Labels
+
+This repository is using the following labels for issues, pull requests and project management:
+
+- Type
+    - `bug`: errors to be fixed
+    - `improvement`: enhancement to an existing functionality
+    - `feature`: new functionality
+    - `discussion`: discussion threads
+    - `question`: often turn into discussion threads
+- Status
+    - `help-wanted`: requests for assistance
+    - `newcomer-friendly`: suitable for people who want to start contributing
+    - `work-in-progress`: someone is working on this
+    - `review-needed`: requests for review

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tidytranscriptomics
 Title: A Tidy Transcriptomics introduction to RNA-Seq analyses
-Version: 0.0.0.9000
+Version: 0.1.0
 Author: Maria Doyle, Stefano Mangiola
 Maintainer: Maria Doyle <[email protected]>
 Description: This workshop will present how to perform analysis of RNA sequencing data following the tidy data paradigm, using      the tidybulk, tidyHeatmap and tidyverse packages.

README.md

@@ -8,7 +8,7 @@ Docker image: https://hub.docker.com/repository/docker/stemangiola/bioc_2020_tid
 
 Dr. Maria Doyle ([email protected]) and Dr. Stefano Mangiola ([email protected])
 
-# Workshop Description
+## Workshop Description
 
 This workshop will present how to perform analysis of RNA sequencing data following the tidy data paradigm. The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions. 
 
@@ -27,28 +27,29 @@ The topics presented in this workshop will be
 * Familiarity with tidyverse syntax
 
 Recommended Background Reading 
-[Introduction to R for Biologists](https://mblue9.github.io/r-intro-biologists/intro_r_biologists.html)
+[Introduction to R for Biologists](https://melbournebioinformatics.github.io/r-intro-biologists/intro_r_biologists.html)
 
 ## Workshop Participation
 
-Students will be expected to participate in the workshop in a hands-on way, following along with the code provided and performing  exercises.
+Students will be expected to participate in the workshop in a hands-on way, following along with the code provided and performing exercises.
 
 ## _R_ / _Bioconductor_ packages used
 
 * tidyverse
 * tidybulk
 * tidyHeatmap
 * edgeR
-* devtools
+* ggrepel
+* airway
 
 ## Time outline
 
 | Activity                                     | Time |
 |----------------------------------------------|------|
-| Data exploration                             | 30m  |
-| Data dimensionality reduction and clustering | 30m  |
-| Differential gene expression                 | 30m  |
-| Data visualisation                           | 30m  |
+| Data preprocessing                           | 15m  |
+| Data dimensionality reduction and clustering | 15m  |
+| Differential gene expression                 | 10m  |
+| Data visualisation                           | 20m  |
 
 # Workshop goals and objectives
 
@@ -65,4 +66,4 @@ The tidytranscriptomics approach to RNA sequencing data analysis abstracts out t
 
 * Recall the key concepts of RNA sequencing data analysis
 * Apply the concepts to publicly available data
-* Create plots that summarise the information content of the data and analysis results
+* Create plots that summarise the information content of the data and analysis results

vignettes/tidytranscriptomics.Rmd

@@ -1,8 +1,8 @@
 ---
 title: "A Tidy Transcriptomics introduction to RNA-Seq analyses"
 author:
-  - Maria Doyle^[Peter Mac, [email protected]]
-  - Stefano Mangiola^[WEHI, [email protected]]
+  - Maria Doyle, Peter MacCallum Cancer Centre^[[email protected]]
+  - Stefano Mangiola, Walter and Eliza Hall Institute^[[email protected]]
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{TidyTranscriptomics}
@@ -90,7 +90,7 @@ knitr::include_graphics(system.file(package='tidytranscriptomics',  'vignettes',
 * Setting up the data 
 * Filtering lowly expressed genes
 * Scaling of counts
-* Quality control plots
+* Exploratory analyses
 * Differential expression analysis
 * Visualisation (MA plot, Volcano plot)
 
@@ -520,19 +520,19 @@ strip_chart
 
 ### Interactive Plots
 
-A really nice feature of using tidyverse and ggplot2 is that we can make interactive plots quite easily using the plotly package. This can be very useful for exploring what genes or samples are in the plots. We can make interactive plots directly from our ggplot2 object (strip_cart). Having label= into the `aes` is useful to visualise the identified of the data point or other variables to be visible when we hover over the plot.
+A really nice feature of using tidyverse and ggplot2 is that we can make interactive plots quite easily using the plotly package. This can be very useful for exploring what genes or samples are in the plots. We can make interactive plots directly from our ggplot2 object (strip_chart). Having `label` into the `aes` is useful to visualise the identified of the data point or other variables to be visible when we hover over the plot.
 
-We can also specify which parameters from the aes() we want to show up when we hover over the plot with `tooltip`.
+We can also specify which parameters from the `aes` we want to show up when we hover over the plot with `tooltip`.
 
-```{r, echo=FALSE}
+```{r, warning=FALSE}
 strip_chart %>% ggplotly(tooltip = c("label", "y"))
 ```
 
 # Key Points
 - RNA-Seq data can be analysed in a 'tidy' way using the packages tidyverse, tidybulk and tidyHeatmap
 - Key steps in an RNA-Seq analysis are filtering lowly expressed genes, normalisation for sequencing depth and composition, and testing for differential expression
 - MDS plots are very important for examining the quality of the data
-- Other useful plots for assessing RNA-Seq data are bar plots, density plots, box plots, MA plots, volcano plots, stripcharts and heatmaps. These can all be made in a 'tidy' way.
+- Other useful plots for assessing RNA-Seq data are density plots, box plots, MA plots, volcano plots, stripcharts and heatmaps. These can all be made in a 'tidy' way.
 
 
 # Further Reading