1. Bioconductor in R with a expectation free dataset Transcriptomics - practical 2012

2. Please close unnecessary programs. On http://plantsci.arabidopsis.info/pg/2013/http://plantsci.arabidopsis.info/pg/2013/ Choose the ‘Introduction to R/Bioconductor Practical 5’ link Open the Free Transcriptomic Practical link and download the pptx to your DESKTOP **we will fill out this pptx TOGETHER** Download the data files zip folder Unzip to your DESKTOP Download the All Packages zip folder Unzip to your DESKTOP open the pptx

3. Experimental setup Equivalency? - fair representatives? (G/E) Replicates? - ease, cost Suitability of samples? -which tissue? Degradation? - is the tissue normal? - how has it been stored? All determine the TYPE of experiment you are doing While you are doing this analysis – think.. What am I finding out? Why?

4. Installing R / bioconductor This is easy from home, but can be a little tricky from UoN – WAIT FOR THE DEMONSTRATION To save time we are using pre-installed R Start> All Program's> UoN software> Statistical & Mathematical> R - At home – follow the notes below.

5. Expression Probes on a GeneChip Probes Sequence Perfect Match Mismatch Chip 5’ 3’

6. Procedures for Target Preparation cDNA Wash & Stain Scan Hybridise (16 hours) RNA AAAA BBBB Biotin-labeled transcripts Fragment (heat, Mg 2+ ) Fragmented cRNA B B B B IVT (Biotin-UTP Biotin-CTP)

7. GeneChip ® Expression Analysis Hybridization and Staining Array cRNA Target Hybridized Array Ab detection

8. Installing Bioconductor / oneChannelGUI normally WAIT FOR THE DEMONSTRATION DON’T DO THIS NOW

9. Experimental design and RNA tables Biological replicates from separate tissue samples

10. Box plots & normalisation

11. RMA uses Quantile normalisation at the probe level Chip 1 Chip 2 Chip 3 1 2 3 4 5 1 2 3 5 7 2 3 4 5 9 Order by ranks PA PB PC PD PE Chip 1 Chip 2 Chip 3 1 2 4 3 5 7 2 5 3 1 5 3 4 2 9 Average the intensities at each rank Chip 1 Chip 2 Chip 3 1.33 2.33 3.33 4.66 7 PA PB PC PD PE Chip 1 Chip 2 Chip 3 1.33 2.33 4.66 3.33 7 7 2.33 4.66 3.33 1.33 4.66 2.33 3.33 1.33 7 Reorder by probe

12. PCA – does my data look good in that?

13. Contrasts, top tables & differentials

14. If time permits: Venn diagrams