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Introduction to Microarry and Related High Throughput Analysis BMI 705

Introduction to Microarry and Related High Throughput Analysis BMI 705 . Kun Huang Department of Biomedical Informatics Ohio State University. What is microarray? Affymetrix-like arrays – single channel (background-green, foreground-red) cDNA arrays – two channel (red, green, yellow)

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Introduction to Microarry and Related High Throughput Analysis BMI 705

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  1. Introduction to Microarry and Related High Throughput Analysis BMI 705 Kun Huang Department of Biomedical Informatics Ohio State University

  2. What is microarray? • Affymetrix-like arrays – single channel (background-green, foreground-red) • cDNA arrays – two channel (red, green, yellow) • CGH array, DNA methylation array, SNP array, etc. • CHIP-on-Chip • Tissue microarray • Future - Sequencing

  3. How is microarray manufactured?

  4. How does two-channel microarray work? • Printed microarrays • Long probe oligonucleotides (80-100) long are “printed” on the glass chip

  5. How does two-channel microarray work? • Printing process introduces errors and larger variance • Comparative hybridization experiment

  6. How does microarray work?

  7. How is microarray manufactured? • Affymetrix GeneChip • silicon chip • oligonucleiotide probes lithographically synthesized on the array • cRNA is used instead of cDNA

  8. How does Affymetrix microarray work?

  9. How does microarray work?

  10. How does microarray work?

  11. How does microarray work?

  12. How does microarray work?

  13. How does microarray work? • Fabrication expense and frequency of error increases with the length of probe, therefore 25 oligonucleotide probes are employed. • Problem: cross hybridization • Solution: introduce mismatched probe with one position (central) different with the matched probe. The difference gives a more accurate reading.

  14. How do we use microarray? • Profiling • Clustering

  15. Spatial Images of the Microarrays • Data for the same brain voxel but for the untreated control mouse • Background levels are much higher than those for the Parkinson’s disearse model mouse • There appears to be something non random affecting the background of the green channel of this slide

  16. How do we take readings from microarray (measurement)? cDNA array – ratio, log ratio Affymetrix array

  17. How do we process microarray data (McShane, NCI)

  18. How do we process microarray data • Normalization • Intensity imbalance between RNA samples • Affect all genes • Not due to biology of samples, but due to technical reasons • Reasons include difference in the settings of the photodetector voltage, imbalance in total amount of RNA in each sample, difference in uptaking of the dyes, etc. • The objective is to adjust the gene expression values of all genes so that the ones that are not really differentially expressed have similar values across the array(s).

  19. Normalization • Two major issues to consider • Which genes to use for normalization • Which normalization algorithm to use • Housekeeping genes • Genes involved in essential activities of cell maintenance and survival, but not in cell function and proliferation. These genes will be similarly expressed in all samples but they may be difficult to identify – need to be confirmed. Affymetrix GeneChip provides a set of house keeping genes (but still no guarantee). • Spiked controls • Genes that are not usually found in the samples (both control and test sample). E.g., yeast gene in human tissue samples. Note: Affy GeneChip protocol includes the spiking of control oligonucleotides into each sample. They are NOT for normalization. Instead, they are for other purposes such as gridding of slide by the image analysis software. • Using all genes • Simplest approach – use all adequately expressed genes for normalization The assumption is that the majority of genes on the array are housekeeping genes and the proportion of over expressed genes is similar to that of the under expressed genes. If the genes one the chip are specially selected, then this method will not work.

  20. Normalization • Which normalization algorithm to use • For two-color cDNA arrays - Intra-slide normalization Ratio-intensity (RI) or MA plot Scatter plot Slope = 1

  21. Normalization • Linear (global) normalization • Simplest but most consistent • Move the median to zero (slope 1 in scatter plot, this only changes the intersection) • No clear nonliearity or slope in MA plot

  22. Normalization • Intensity-based (Lowess) normalization • Overall magnitude of the spot intensity has an impact on the relative intensity between the channels. • “Straighten” the Lowess fit line in MA plot to horizontal line and move it to zero

  23. Normalization • Intensity-based (Lowess) normalization • Nonlinear • Gene-by-gene, could introduce bias • Use only when there is a compelling reason (McShane, NCI)

  24. Normalization • Other normalization method • Combination of location and intensity-based normalization • Location • Quantile • …

  25. Normalization • Which normalization algorithm to use • Inter-slide normalization • Not just for Affymetrix arrays

  26. Normalization • Box plot Upper quartile Median Low quartile

  27. Normalization • Linear (global) – the chips have equal median (or mean) intensity • Intensity-based (Lowess) – the chips have equal medians (means) at all intensity values • Quantile – the chips have identical intensity distribution • Quantile is the “best” in term of normalizing the data to desired distribution, however it also changes the gene expression level individually • Avoid overfitting • Avoid bias

  28. Gene Discovery and T-tests • Student’s t-test

  29. Gene Discovery and Multiple T-tests • Controlling False Positives • Statistical tests to control the false positives • Controlling for no false positives (very stringent, e.g., Bonferroni test) • Controlling the number of false positives • Controlling the proportion of false positives • Note that in the screening stage, false positive is better than false negative as the later means missing of possibly important discovery.

  30. Microarray Databases • Gene Expression Ominbus (GEO) database – NCBI • http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed • EMBL-EBI microarray database • http://www.ebi.ac.uk/Databases/microarray.html • ArrayExpress • Stanford Microarray Database (SMD) • http://genome-www5.stanford.edu/ • Other specialized, regional and aggregated databases • http://psi081.ba.ars.usda.gov/SGMD/ • http://www.oncomine.org/main/index.jsp • http://ihome.cuhk.edu.hk/~b400559/arraysoft_public.html • …

  31. Microarray Softwares • DChip • Open source R, Bioconductor • BRBArray tools (NCI biometric research branch) • Affymetrix • GeneSpring GX • GenePattern • …

  32. How do we use microarray (clustering)?

  33. How do we process microarray data (clustering)? • Unsupervised Learning – Hierarchical Clustering

  34. ChIP-on-chip, “also known as genome-wide location analysis, is a technique for isolation and identification of the DNA sequences occupied by specific DNA binding proteins in cells.” (http://www.chiponchip.org) • Identify protein binding sites on DNA • Study transcriptional factors – identify the genes that controlled by the specific TFs • Identify TFs • Identify regulatory regions such as promoters, enhancers, repressors, silencing elements, insulators, and boundary elements • Determine sequences controlling DNA replication (e.g., histone binding sites)

  35. ChIP-on-Chip ChIP – Chromatin immunoprecipitation Chip – Microarray

  36. ChIP-on-Chip – Example Simon I., Barnett J., Hannett N., Harbison C.T., Rinaldi N.J., Volkert T.L., Wyrick J.J., Zeitlinger J., Gifford D.K., Jaakkola T.S., et al. "Serial regulation of transcriptional regulators in the yeast cell cycle", Cell, Volume: 106, (2001), pp. 697-708. Figure 2. Genome-wide Location of the Nine Cell Cycle Transcription Factors(A) 213 of the 800 cell cycle genes whose promoter regions were bound by a myc-tagged version of at least one of the nine cell cycle transcription factors (p < 0.001) are represented as horizontal lines. The weight-averaged binding ratios are displayed using a blue and white color scheme (genes with p value < 0.001 are displayed in blue). The expression ratios of an α factor synchronization time course from Spellman et al. (1998) are displayed using a red (induced) and green (repressed) color scheme.(B) The circle represents a smoothed distribution of the transcription timing (phase) of the 800 cell cycle genes (Spellman et al., 1998). The intensity of the red color, normalized by the maximum intensity value for each factor, represents the fraction of genes expressed at that point that are bound by a specific activator. The similarity in the distribution of color for specific factors (with Swi4, Swi6, and Mbp1, for example) shows that these factors bind to genes that are expressed during the same time frame

  37. ChIP-on-Chip – Example Simon I., et al. "Serial regulation of transcriptional regulators in the yeast cell cycle", Cell, Volume: 106, (2001), pp. 697-708.

  38. ChIP-on-Chip • Problem : Probe design • Most TF binding sites are not in exon • Binding sequences are short • Cover entire genome? • Signal may be small • Tiling array – divide the sequence into chunks, called tiling path. The distance between the center of neighboring chunks is called resolution. A path can be overlapped or spaced. • Affymetrix tiling array for yeasts – 5bp resolution, 3.2 million probes • Affymetrix tiling array for human – 35bp spacing, 90 million probes

  39. Sequencing • Solexa http://www.illumina.com/pages.ilmn?ID=203 • SOLiD Mikkelsen et al

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