Content
- Introduction on data analysis
- Quantitative proteomics data analysis - overview
- Visualisation
- Quantitative proteomics data analysis - examples
- Data analysis
- References and resources
Laurent Gatto
Laurent Gatto Computational Proteomics Unit
https://lgatto.github.io University of Cambridge
lg390@cam.ac.uk @lgatt0
Slides: http://bit.ly/qprotda – Vignette: http://bit.ly/qprotdavig
Acknowledgements BBSRC for funding; Sebastian Gibb and Lisa Breckels for coding.
(Last update Mon Jun 6 08:58:52 2016)
These slides are available under a creative common CC-BY license. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.
Data analysis is the process by which data becomes understanding, knowledge and insight. Hadley Wickham
The ability to prepare and explore data, identify patterns (good and pathological ones) and convincingly demonstrate that the patterns are genuine (rather than random).
It’s not analysing data, it’s investigating data - requires flexibility.
Programming
, but:
Graphics reveal data.
Visualization can surprise you, but it doesn’t scale well. Modeling scales well, but it can’t surprise you. Hadley Wickham
Inspection, visualisation and analysis of quantitative proteomics data
should enables you to manipulate your data, give some guarantees about the integrity of the data, support effective extract/subset components of the data, visualise them, enable transformation of the data, give access to infrastucture for statistical analysis, and enable annotation of the data.
MSnSet
class for quantitative data
Can be subsetted, transformed, visualised, annotated, statistics, …
RforProteomics
: http://bioconductor.org/packages/RforProteomicsThank you for your attention