Merge pull request #7 from tidytranscriptomics-workshops/stemangiola-…

…patch-1

rearrange info on README

README.md

@@ -9,16 +9,44 @@
 <img style="height:100px;" alt="tidybulk" src="https://github.com/Bioconductor/BiocStickers/blob/master/tidybulk/tidybulk.png?raw=true"/>
 </p>
 
-## Instructor names and contact information
+## Workshop Description
 
-* Maria Doyle <Maria.Doyle at petermac.org>  
-* Stefano Mangiola <mangiola.s at wehi.edu.au>
+This tutorial will present how to perform analysis of single-cell 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.
 
-## Syllabus
+This can be achieved with the integration of packages present in the R CRAN and Bioconductor ecosystem, including [tidySingleCellExperiment](https://stemangiola.github.io/tidySingleCellExperiment/) and [tidyverse](https://www.tidyverse.org/). These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis. For more information see the [tidy transcriptomics blog](https://stemangiola.github.io/tidytranscriptomics/).
+
+### Pre-requisites
+
+* Basic familiarity with single-cell transcriptomic analyses
+* Basic familiarity with tidyverse
+
+## Workshop goals
 
-Material [web page](https://tidytranscriptomics-workshops.github.io/bioc2022_tidytranscriptomics/articles/tidytranscriptomics_case_study.html).
+* To approach single-cell data representation and analysis though a tidy data paradigm, integrating tidyverse with tidySingleCellExperiment.
+* Compare SingleCellExperiment and tidy representation  
+* Apply tidy functions to SingleCellExperiment objects  
+* Reproduce a real-world case study that showcases the power of tidy single-cell methods
 
-More details on the workshop are below.
+### What you will learn
+
+* Basic tidy operations possible with tidySingleCellExperiment
+* The differences between SingleCellExperiment representation and tidy representation
+* How to interface SingleCellExperiment with tidy manipulation and visualisation
+* A real-world case study that will showcase the power of tidy single-cell methods compared with base/ad-hoc methods
+
+### What you will not learn
+
+* The molecular technology of single-cell sequencing
+* The fundamentals of single-cell data analysis
+* The fundamentals of tidy data analysis
+
+### Workshop Participation
+
+The workshop format is a 1.5 hour session consisting of hands-on demos, exercises and Q&A.
+
+## Syllabus
+
+Material [web page](https://tidytranscriptomics-workshops.github.io/bioc2022_tidytranscriptomics/articles/tidytranscriptomics_case_study.html). More details on the workshop are below.
 
 ## Workshop package installation 
 
@@ -63,46 +91,8 @@ browseVignettes("bioc2022tidytranscriptomics")
 
 To run the code, you could then copy and paste the code from the workshop vignette or [R markdown file](https://raw.githubusercontent.com/tidytranscriptomics-workshops/bioc2022_tidytranscriptomics/master/vignettes/tidytranscriptomics.Rmd) into a new R Markdown file on your computer.
 
-## Workshop Description
-
-This tutorial will present how to perform analysis of single-cell 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.
-
-This can be achieved with the integration of packages present in the R CRAN and Bioconductor ecosystem, including [tidySingleCellExperiment](https://stemangiola.github.io/tidySingleCellExperiment/) and [tidyverse](https://www.tidyverse.org/). These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis. For more information see the [tidy transcriptomics blog](https://stemangiola.github.io/tidytranscriptomics/).
-
-### Pre-requisites
-
-* Basic familiarity with single-cell transcriptomic analyses
-* Basic familiarity with tidyverse
-
-
-### Workshop Participation
-
-The workshop format is a 1.5 hour session consisting of hands-on demos, exercises and Q&A.
-
-
-## Workshop goals and objectives
-
-### Learning goals
-
-* To approach single-cell data representation and analysis though a tidy data paradigm, integrating tidyverse with tidySingleCellExperiment.
-
-
-### Learning objectives
-
-* Compare SingleCellExperiment and tidy representation  
-* Apply tidy functions to SingleCellExperiment objects  
-* Reproduce a real-world case study that showcases the power of tidy single-cell methods
-
-
-### What you will learn
-
-* Basic tidy operations possible with tidySingleCellExperiment
-* The differences between SingleCellExperiment representation and tidy representation
-* How to interface SingleCellExperiment with tidy manipulation and visualisation
-* A real-world case study that will showcase the power of tidy single-cell methods compared with base/ad-hoc methods
+## Instructor names and contact information
 
-### What you will not learn
+* Maria Doyle <Maria.Doyle at petermac.org>  
+* Stefano Mangiola <mangiola.s at wehi.edu.au>
 
-* The molecular technology of single-cell sequencing
-* The fundamentals of single-cell data analysis
-* The fundamentals of tidy data analysis