Microarray Data Analysis Roy Williams PhD; Burnham Institute for Medical Research.

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Presentation transcript:

Microarray Data Analysis Roy Williams PhD; Burnham Institute for Medical Research

Aims Load normalised illumina data into GeneSpring (undiff verses diff stem cells) Cancel GeneSpring normalisation Define biological replicates Discover significantly differentially regulated genes (undiff verses diff stem cells) Compare this list to Gene Ontologies Attempt to make a conclusion

Essential Tools GeneSpring: Download Demo version Quantiles Normalised Stem Cell Dataset Select data from either: –StemCellCommunity.org database –NIH GeneExpressionOmnibus database –

Stem Cell Microarray Database Automatically exports normalised data table!!! GCT table format is widespread!!!

After QC for low confidence genes (P<0.99) Note: ~50 replicate beads per array Median Outliers 25% quartile 75% quartile BAD CHIP BOXPLOT REPRESENTATION OF DATA SPREAD CHIP NUMBER SIGNAL INTENSITY

The effect of quantiles Normalisation on the filtered 36 data sets IMPORTANT: use non-linear normalisation >library(affy) >Qdata <- normalize.quantiles(Rawdata) All same range

Normalised Tutorial Dataset Using this tool generated the dataset: outputTueJul gct (.gct is portable!) AES_derived Neural stem cells AES_derived Neural stem cells AES_derived Neural stem cells EES cells_ undifferentiated FES cells_ undifferentiated DES cells_ undifferentiated

Genome Import: File ->import genome Load the illumina chip information into GeneSpring

Drag and Drop loads datafile

Define columns using drop down boxes

Define sample attributes (or not)

Give new experiment a name and save

Define new experiment normalisations and parameters New data set loaded appears here

Delete the default normalisations

ALL GONE!

IMPORTANT! DEFINE REPLICATES (PARAMETERS)

Define the custom parameter “group”

Groups define the replicates (use exactly the same text!)

Change the interpretation to look at the “group” replicates

Filter on expression level: Remove the genes which are not expressed (ie absent)

Filtering leaves 25,923 of 49,009 genes: save list as GenesPresent

Reset colour bar range Right click on Colour bar

Using the now default interpretation the data is in 2 groups

To find differentially expressed gene filter on Volcano Plot

With these filters gives a list of ~1800 genes: save list

Export the list with the averaged data: copy annotated GeneList

Select annotations to export

Paste average data into an Excel worksheet

Export all the data and see it is highly reproducible

Check list against GeneOntologies: Development is major group

Look for Pathways significantly differentially expressed Load into Nextbio and Ingenuity GSEA is a good free alternative for pathways ( GeneSet enrichment analysis ) GenePattern is a good free alternative to GeneSpring ( )