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README.md

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 ## 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 tutorial will showcase 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/).