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DTSTAMP:20260618T114830Z
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DTSTART:20220218T130000Z
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DESCRIPTION:This tutorial will present how to perform analysis of single-ce
 ll RNA sequencing data following the tidy data paradigm. The tidy data par
 adigm provides a standard way to organise data values within a dataset\, w
 here each variable is a column\, each observation is a row\, and data is m
 anipulated using an easy-to-understand vocabulary. Most importantly\, the 
 data structure remains consistent across manipulation and analysis functio
 ns. This can be achieved with the integration of packages present in the R
  CRAN and Bioconductor ecosystem\, including tidyseurat\, tidySingleCellEx
 periment\, and tidyverse. These packages are part of the tidytranscriptomi
 cs suite that introduces a tidy approach to RNA sequencing data representa
 tion and analysis.\n\n**Instructors:**\nDr. Stefano Mangiola is a Postdoct
 oral researcher in the laboratory of Prof. Tony Papenfuss. His background 
 spans from biotechnology to bioinformatics and biostatistics. His research
  focuses on prostate and breast tumour microenvironment\, the development 
 of statistical model for the analysis of RNA sequencing data\, and data an
 alysis and visualisation interfaces.\nDr. Maria Doyle is the Application a
 nd Training Specialist for Research Computing at the Peter MacCallum Cance
 r Centre in Melbourne\, Australia. She has a PhD in Molecular Biology and 
 currently works in bioinformatics and data science education and training.
  She is passionate about supporting researchers\, reproducible research\, 
 open source and tidy data.\n\n**Recommended Prerequisites:**\nBasic knowle
 dge of single cell transcriptomic analyses\nBasic knowledge of tidyverse\n
SUMMARY:Tidy Trannscriptomics for Single-cel RNA Sequencing Analyses
URL;VALUE=URI:https://www.iscb.org/cms_addon/academy/events#5
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