The microarray block. Outline. Microarray experiments. Microarray Technologies. Outline

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1 The microarray block Bioinformatics March 006 Microarray data analysis John Gustafsson Mathematical statistics Chalmers Lectures DNA microarray technology overview (KS) of microarray data (JG) How to use microarray technology and what to do with the results (BG) Computere exercise A case study using Bioconductor (JG) Microarray experiments Outline Biological Question or Hypothesis Labwork Microarray experiments Data analysis steps Introduction to the Computer Exercise Biological verification and interpretation Statistics and ranking To consider all these parts is extremely important for a satisfying result. Outline Microarray experiments Technologies design acquisition and analysis Data analysis steps Introduction to the Computer Exercise Microarray Technologies Spotted two-channel cdna Up to spots on a single glass-slides Two sources of RNA on each array labeled with red (Cy5) and green (Cy3) Can be bought or produced One slide costs from 100 euro 1

2 Microarray Technologies Affymetrix Each gene has seveal PM and MM One source on each array Can only be bought One chip costs from 00 euro design Depends heavily on the biological question Different types of designs are: directs comparison, common reference and time course Differs between cdna and Affymetrix type of microarrays design design Direct Comparison Time course with direct comparisons A B T 1 T T 3 T 4 Common Reference Time course with common reference A B T 1 T T 3 T 4 C. Ref. C. Ref. acquisition and analysis acquisition and analysis Print-tip area cdna Microarray

3 Outline Microarray experiments Data analysis R and Bioconductor Visualization and exploration of data Introduction to the Computer Exercise R and Bioconductor R is a powerful statistical software that is open source and free R is available for many platforms (Windows, Linux, Solaris, ), see Bioconductor is a collection of R packages for analysis of biological data of various kind, see LIMMA Linear Models for Microarray An R-package for analysis of microarray data Originally intended for spotted cdna microarrays but it is possible to analyze Affymetrix data as well Relatively easy to use and can handle almost any experimental design Contains most of the functions needed for a basic microarray analysis More information of LIMMA is available at Data Representation (cdna) To increase visualization, the following transformation is usually done R M = log R logg = log G 1 A = ( log R + logg) M reflects the fold change and A the average spot intensity MA-plot Data Representation (Affy) A model that takes into account the different affinity of the probes is fitted to the PM s The MM s is usually discarded The result is a log value for each gene (signal) MA-values can be created by comparing two Affymetrix arrays 3

4 Visualization Very important for quality control, and understanding the data Visualization of spatial patterns Visualization of intensity dependent bias: MA-plots Visualization of variance: box-plots swirl.1 Box-plot M M A swirl.1 swirl. swirl.3 swirl.4 loess Necessary to remove red or green bias Which subset to normalize with? Can be intensity-dependent. Ad-hoc solution: The loess function. Can be spatially dependent. A solution: print-tip loess normalization Normalizing between arrays: Adjusting the distribution Labwork A robust way to remove bias and intensity dependent trends. A curve is fitted using a robust weighted least square to the MA-plot and is then subtracted from the M-value. Can be done both locally (print-tip loess) and globally (global loess). Computationally intensive for large datasets (i.e. many genes). 4

5 loess To decide which genes that are regulated we need a statistic or ranking function The most common hypothesis are H 0 : gene g is not regulated H A : gene g is regulated, but other hypothesis might also be interesting Example of statistics used are: M-value threshold, t- statistic and moderated t-statistic M-value threshold M-value threshold Reject H 0 if M g > T Reject H 0 if M g > T Pros Easy to use and implement Fast and works with any number of arrays Easy to understand Easy to calculate significance under the right assumptions Cons Does not take the variance of a gene into account t-statistic Labwork M g Reject H 0 if > T. S / n Pros Takes the variance into account Easy to use and implement Easy to calculate significance under right assumptions Cons Very sensitive to genes with small variance Need many arrays to work satisfactory t-statistic M g Reject H 0 if > T. S / n 5

6 moderated t- statistic Labwork moderated t- statistic M g Reject H 0 if > T. S / n + a M g Reject H 0 if > T. S / n + a Pros Takes the variance into account Robust Works well with few arrays (>) Cons Numerical methods is needed to calculate significance. Outline Microarray experiments Data analysis steps Introduction to the Computer Exercise Introduction to Computer Exercise Aims To understand a complete microarray analysis To understand the need for normalization To understand the difference between different statistics To use the LIMMA-package and understand some its structure The SWIRL dataset ~8000 genes on four arrays from zebrafish. Direct comparison of WT vs BMP mutant. Dye swaps - two arrays with WT Cy3, mutant Cy5 and two arrays with WT Cy5, mutant Cy3. Questions at issue What happens in the mutant? (Exploative approach) What happens with the BMP gene? What should happen in a ideal experiment? Acknowledgements The lecture is partially based on slides by Erik Kristiansson and Petter Mostad 6

7 Extra slides Array Layout cdna microarrays are printed using sets of printtips This creates blocks containing rows and columns In order to visualize correctly each spot, its spatial position on the chip must be inferred An object containing the number of rows and columns of blocks, and the number of rows and columns of spots within blocks must be created Multiple testing adjustments The raw p-values are valid only individually Several ways to deal with this, for example controlling family wise error rate, or controlling the false discovery rate The Holm method will control the family wise error rate: gives few significant genes The FDR method controls the false discovery rate: how many false positives (genes that only appear to be diff. expr.) one can expect On normalization of trends Attention: Trends might be confounded with interesting information might conceal this interesting information Solution: Randomize! Example: Spatial trends in microarrays (can randomize gene position), however not possible for D-gels (cannot randomize protein position) 7

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