SPOTTED cdna MICROARRAYS

Transcription

1 SPOTTED cdna MICROARRAYS Spot size: 50um - 150um SPOTTED cdna MICROARRAYS Compare the genetic expression in two samples of cells PRINT cdna from one gene on each spot SAMPLES cdna labelled red/green e.g. treatment / control normal / tumor tissue

2 HYBRIDIZE Add equal amounts of labelled cdna samples to microarray. Laser SCAN Detector GENERAL APPROACH 1. Image Analysis: Extract intensities from the scanner images of both dyes (in each slide). The scanner produces green Cy3 and red Cy5 16-bit TIFF image files (for each pixel, intensity ranges from 0 to ) 2. Data Cleaning: Detect and filter poor quality genes on a slide using measurement from multiple spots. 3. Normalization: Perform slide-dependent nonlinear normalization of the log-ratios of the two channels. 4. Analysis: Use hierarchical model-based analysis on normalized log-ratio scale to perform significance testing or clustering.

3 GENE EXPRESSION DATA On p genes for n slides: p is O(10,000), n is O(10-100), but growing. Slides Genes slide 1 slide 2 slide 3 slide 4 slide Gene expression level of gene 5 in slide 4 = Log 2 ( Red intensity / Green intensity) These values are conventionally displayed on a red (>0) yellow (0) green (<0) scale.

4 IMAGE ANALYSIS Addressing, Segmenting, Quantifying Quality of images, spots, (log) ratios Comet tails PROBLEM IMAGES Smudges? PROBLEM IMAGES (CONT D) High background & weak signals Spot overlap

5 PROBLEM IMAGES (CONT D) Dust (Hair?) TIFF file representations of fluorescence activity Part of the image of one channel false-coloured: (very high) red (high) yellow (medium) green (medium-low) blue (low) black (very low)

6 STEPS IN IMAGES PROCESSING 1. Addressing: locate centers. 2. Segmentation: classification of pixels either as signal or background (using seeded region growing). 3. Information extraction: for each spot of the array, calculates signal intensity pairs, background and quality measures. Some image analysis software: ArrayWorx, Dapple, GenePix, ImaGene, ScanAlyse, Spot, UCSF Spot, etc..

7 ADDRESSING This is the process of assigning coordinates to each of the spots. Automating this part of the procedure permits high throughput analysis. 4 by 4 grids 19 by 21 spots per grid ADDRESSING Registration

8 SEGMENTATION METHODS Fixed circles Adaptive Circle Adaptive Shape Edge detection. Seeded Region Growing (R. Adams and L. Bishof (1994): Regions grow outwards from the seed points preferentially according to the difference between a pixel s value and the running mean of values in an adjoining region. Histogram Methods Adaptive threshold. Clustering algorithms (Bozinov and Rahnenfuhrer, 2002) Robust to sickle-cell, donut-shaped spots. SEGMENTATION: LIMITATION OF THE FIXED CIRCLE METHOD Seeded Region Growing (Yang et al., 2002) Fixed Circle Inside the boundary is spot (foreground), outside is not.

9 INFORMATION EXTRACTION Spot Intensities mean (pixel intensities) median (pixel intensities) Pixel variation (IQR of log (pixel intensities) Background values Local Morphological opening Constant (global) None Quality Information Background Signal QUALITY FILTERING 1. Suppose each gene (clone) is spotted p times on the slide. 2. For each spot, calculate m s = Cy5/Cy3 (s = 1, 2,,p) and CV (standard deviation / mean). slide with better quality genes that should be filtered out Quality index (CV) vs average intensity in 125-gene project, for two slides (C1S2 and C2S1). The curve indicates the 10 th upper percentile in the moving windows contaning 50 nearest genes. (Source: Li et al., 2003)

10 DATA CHECKS Quality Area (not too small, not too large Ø look for outliers) Circularity (area/perimeter 2 as small as possible) Signal to Noise ratio (foreground/background intensities) Interpixel coefficient of variation (std dev/ mean). Use array-specific box plots, normal probability plots and spatial plots to diagnose potential problems with area, foreground, background, foreground/background, etc. Outlier detection As many as 15% of data are outliers (Lee et al., 2000). Not easy to pursue for test probes outlier detection is difficult when # of reps/probe is small. QUALITY OF ARRAY Distribution of areas - Judge by eye - Look at variation. (e.g, SD) Cy3 area mean 57 median 56 SD Cy5 area mean 59 median 57 SD 24.34

11 SOME LOCAL BACKGROUNDS GenePix QuantArray ScanAnalyze GeneTAC LS IV Background adjustment method more important than segmentation (Yang et al., 2002) QUANTIFICATION OF EXPRESSION For each spot on the slide we calculate: Red intensity = Rfg - Rbg Green intensity = Gfg - Gbg (fg = foreground, bg = background) And combine them in the log (base 2) ratio: Log 2 ( Red intensity / Green intensity)

12 WHAT IS THE DATA? Example data from the CAFG (Center for Animal Functional Genomics - MSU) FluorN ame Grid Spot Addres s Grid Spot Name Area Volume AvgPix Int Back ground Back ground Avg Back ground StdDev Back ground Volume Target Avg Target Median Target StdDev Target Volume Cy3 A1.c3 GAPDH Cy5 A1.c3 GAPDH Number of pixels x Average pixel = Volume intensity (after background) Number of background pixels = 176 (after background) Number of target pixels = 553 > Area WHAT HAS BEEN DONE? Typically total (background-adjusted) volume has been analyzed Certainly appropriate as measure of total mrna hybridized. But how about average pixel intensity? Particularly if Cy3 and Cy5 areas for a spot are different and area is a function of print quality?

13 Average pixel intensity vs. area Cy3 (one example microarray) Theoretical maximum Should not be saturated at maximum! Should spots from larger areas receive greater statistical weightings? Do the pixel areas match up? (and should they?) Cy3 If pixel areas on an array don t match up, should we use average pixel intensity? Cy5

14 Spatial patterns in average pixel area? (GAPDH Cy3 example) Spatial patterns in average pixel area? (LAMBDAQ Cy3 example)

15 The red/green ratios can be spatially biased. Top 2.5% of ratios red, bottom 2.5% of ratios green The red/green ratios can be intensity-biased (M-A Plot) M = log 2 R/G = log 2 R - log 2 G A = log 2 (R µ G ) = (log 2 R + log 2 G )/2 Values should scatter about zero.

16 REFERENCES Bozinov, D. and J. Rahnenfuhrer (2002) Unsupervised technique for robust target separation and analysis of cdna microarray spots through adaptive pixel clustering. Bioinformatics 18(5): Lee, M.L.T et al. (2000) Importance of replication in cdna microarray gene expression studies: statistical methods and evidence from repetitive cdna hybridizations. PNAS 97, Nadon, R. and J. Shoemaker Statistical issues with microarrays: processing and analysis. Trends in Genetics 18(5): Tseng, G. C. et al (2001) Issues in cdna microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res. 29(12), Yang, Y.H., Buckley, M.J., Dudoit, S., and Speed, T.P. (2002) Comparison of methods for image analysis on cdna microarray data. Journal of Computational and Graphical Statistics 11, 1-29.