Introduction to Microarray analysis

Overview of experimental process

File:Expt2.tiff

• Competitive hybridization to spotted oligo/cDNA transcripts
• Interested in genes that change between treatments

→ differential expression versus equivalent expression

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Reproducibility or results

• Ideally would like unambiguous interpretation of results
• Large amounts of data to analyse can be overwhelming and make interpretation subjective
• Independent reproducibility of results by another collegue
• Keep a record (log) of what was done

Statistical analysis process

File:Overview2.tiff

• Raw data (GPR file format)

http://www.moleculardevices.com/pages/software/gn_gpr_format_history.html

• Each GPR intensity file is typically >8 megabytes in size
• Each TIFF image file is typically >30 megabytes in size
• A microarray experiment consists of several → many slides

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Statistical issues

• In the past statistics was developed for n >>p

n observations, p variables

• Gene expression data n<<p

Thousands of measured genes (p)

Small number of biological replicate slides (n)

• Gene expression data can be highly correlated

groups of genes are regulated in the same way

• Data not normally distributed

log transform highly skewed intensity data

File:Graph channels.tiff

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Scratch pad

• A flow diagram for analysis
• Recap of cDNA microarrays (slide 3)
• Microarray data issues (slide 4)
• Microarray data issues (continued)
  • Large amount of data (GPR/JPEG file size)
  • Subjective
  • Need a log of what was done so someone else can quickly reroduce the results
  • → Reproducible research (someone else can understand/reproduce the results) (McGintys talk)
• Analysis process

• R resources/contributed guides (including downloading)
• R tutorial of basics (objects/indexing/functions)

http://www.moleculardevices.com/pages/software/gn_genepix_file_formats.html#gpr