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Bioconductor in R with a expectation free dataset

Transcriptomics - practical 2012. Bioconductor in R with a expectation free dataset. Please close unnecessary programs. On http:// plantsci.arabidopsis.info/pg/2013/ Choose the ‘Introduction to R/ Bioconductor Practical 5’ link

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Bioconductor in R with a expectation free dataset

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

  2. Please close unnecessary programs. • On 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 • openthepptx

  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 RStart> All Program's> UoNsoftware> Statistical & Mathematical> R- At home – follow the notes below.

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

  6. Biotin-labeled transcripts B B B B B B B B Fragmented cRNA Procedures for Target Preparation Fragment (heat, Mg2+) IVT (Biotin-UTP Biotin-CTP) AAAA RNA Wash & Stain Scan cDNA Hybridise (16 hours)

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

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

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

  13. Contrasts, top tables & differentials

  14. If time permits: Venn diagrams

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