Bioconductor in R with a expectation free dataset

Transcriptomics - practical 2012

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

• open the pptx

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?

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> UoN software> Statistical & Mathematical> R

- At home – follow the notes below.

Expression Probes on a GeneChip

Probes

Sequence

Perfect MatchMismatch

Chip

5’ 3’

Procedures for Target Preparation

cDNA

Wash & Stain

Scan

Hybridise

(16 hours)

RNAAAAA

B B B B

Biotin-labeled transcripts

Fragment(heat, Mg2+)

Fragmented cRNA

B BB

B

IVT(Biotin-UTPBiotin-CTP)

GeneChip® Expression AnalysisHybridization and Staining

Array

cRNA Target

Hybridized Array

Ab detection

Installing Bioconductor / oneChannelGUI normally

WAIT FOR THE DEMONSTRATIONDON’T DO THIS NOW

Experimental design and RNA tables

Biological replicatesfrom separate tissue samples

Box plots & normalisation

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

1.33 2.33 3.33 4.66 7

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

PCA – does my data look good in that?

Contrasts, top tables & differentials

If time permits: Venn diagrams