From 431490a384229d79c1a5398ad9f96ecb335dc9b9 Mon Sep 17 00:00:00 2001 From: Maria Doyle Date: Thu, 2 Jul 2020 19:25:58 +1000 Subject: [PATCH] Update README --- README.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index d9d8b45..a847fcb 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ Docker image: https://hub.docker.com/repository/docker/stemangiola/bioc_2020_tid Dr. Maria Doyle (Maria.Doyle@petermac.org) and Dr. Stefano Mangiola (mangiola.s@wehi.edu.au) -# 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. @@ -27,11 +27,11 @@ 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 @@ -39,16 +39,17 @@ Students will be expected to participate in the workshop in a hands-on way, foll * 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 \ No newline at end of file