Image Quantitation in Microarray Analysis More tomorrow...

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Presentation transcript:

Image Quantitation in Microarray Analysis More tomorrow...

Microarray analysis n Array construction, hybridisation, scanning n Quantitation of fluorescence signals n Data visualisation n Meta-analysis (clustering) n More visualisation

Technical probe (on chip) sample (labelled) pseudo-colour image [image from Jeremy Buhler]

Experimental design n Track what’s on the chip which spot corresponds to which gene n Duplicate experimental spots reproducibility n Controls –DNAs spotted on glass positive probe (induced or repressed) negative probe (bacterial genes on human chip) –oligos on glass or synthesised on chip (Affymetrix) point mutants (hybridisation plus/minus)

Imagesfrom scanner n Resolution –standard 10  m [currently, max 5  m] –100  m spot on chip = 10 pixels in diameter n Image format –TIFF (tagged image file format) –can be compressed (eg. Lempel-Ziv-Welch: ~ 5x compression) –1cm x 1cm image at 16 bit = 2Mb (uncompressed) –other formats exist eg. SCN (used at Stanford University) n Separate image for each fluorescent sample –channel 1, channel 2, etc.

Images in analysis software n Typical experiment: –“normal” state, Cy3-labelled sample (green) –“perturbed” state, Cy5-labelled sample (red) –hybridisation, then scanning –overlay images  pseudo-colour image –qualitative representation of results Image spot colourSignal strengthGene expression yellow:normal = perturbedunchanged green:normal > perturbedrepressed red:normal < perturbedinduced

Quantitation process (1) Accurate representation of signal for each spot and determine ratio channel1:channel2 n Determine spot boundary –construct grid (dimensions of array / spot size) –iterative process to find spots n Measure signal –fluorescence 8 bit = 256 shades 16 bit = 65’536 shades –absolute output values vary from system to system

Quantitation process (2) n Measure background –local (usually best) –selected region –selected spots / probes from different species n Quality control –eg. fraction of pixels greater than background (ScanAlyze) –flag aberrant spots n Determine ratio of signal strengths for each spot Ch1/Ch2 = (Ch1 I -Ch1 B )/Ch2 I -Ch2 B )

Normalisation n Eliminate systematic variation –correct for dye incorporation print-tip effects hybridisation efficiencies etc. n How? n Use STATISTICS!

Normalisation references Normalization for cDNA microarray data Yang et al. (2001) In preparation Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments Dudoit et al. (2000) Technical report #578 Berkeley Statistics Dept. both available from ftp://ftp.ch.embnet.org/pub/MAcourse/material / [statistics]

Quantitation - problems n Reference signal is close zero –channels ratio (Ch1/Ch2) tends to infinity n Non-uniform background –“mean” background sometimes non-representative bright particles streaks on image –safer to use “median” (middle value) less contribution by extreme values

Quantitation à la ScanAlyze signal background

ScanAlyze output CH1Ich1 intensity CH2Ich2 intensity SPIXnumber of pixels in spot CH1Bmedian intensity of the local background (recommended) CH2Bmedian intensity of the local background (recommended) CH1BAmean intensity of the local background CH2BAmean intensity of the local background BGPIXnumber of background pixels Ch1 CH1I - CH1B Thus to calculate channel ratios:Ch1 CH1I - CH1B --- = Ch2 CH2I - CH2B Quality control: CH1GTB1fraction of pixels in spot greater than background (CH1B) CH2GTB1fraction of pixels in spot greater than background (CH2B) CH1GTB2fraction of pixels in spot greater than 1.5 X background (CH1B) CH2GTB2fraction of pixels in spot greater than 1.5 X background (CH2B) CH1EDGEAmean magnitude of the horizontal and vertical Sobel edge vectors within spot 1 CH2EDGEAmean magnitude of the horizontal and vertical Sobel edge vectors within spot 2

Software packages - quantitation n ScanAlyze –by Michael Eisen (Stanford University) –quantitation of images –no data visualisation –free from n ImaGene –BioDiscovery Inc. –quantitation and some data visualisation –demo from n plus many others - explore!

Making sense of raw data n Difficult to see results in tabulated data n Represent in graphical form Data visualisation examples from ImaGene...

Data visualisation - scatter plot

Data visualisation - pie chart

ScanAlyze quick demo