microarrays lecture slides courtesy of dr. tim hughes t.hughes@utoronto outline:
DESCRIPTION
Microarrays Lecture Slides Courtesy of Dr. Tim Hughes [email protected] Outline: Microarray experiments Different types of microarrays Clustering and interpretation. Nucleic Acid Hybridization. www.accessexcellence.org/AB/GG/nucleic.html. Typical use of cDNA microarrays: - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/1.jpg)
Microarrays
Lecture Slides Courtesy ofDr. Tim [email protected]
Outline:• Microarray experiments• Different types of microarrays•Clustering and interpretation
![Page 2: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/2.jpg)
www.accessexcellence.org/AB/GG/nucleic.html
Nucleic Acid Hybridization
![Page 3: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/3.jpg)
controltreatment
(drug, mutation)
updownunchangednot present
x y z
xx
x
xx
yy
yy
zz z
cDNA pools
Typical use of cDNA microarrays:“Internal” normalization using two colors
![Page 4: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/4.jpg)
“cDNA microarrays” are essentially dot-blots on glass slides
http://arrayit.com/Products/Printing/Stealth/stealth.html
• This slide was made with 16 pins• 4.5 mm pin spacing matches 384-well plates (16 x 24)• Done with robotics• Slides usually coated with poly-lysine• Spots are usually 100-150 microns• Spot spacing is usually 200-300 microns.• Slides are 25 x 75 mm• Easy to deposit 20K spots/slide
0.45 mm
![Page 5: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/5.jpg)
Microarray expression profiling by 2-color assay (“cDNA arrays”)
Array: PCR products6250 yeast ORFs
hybridized cDNAs:green = controlred = experiment
*Schena et al., 1995
![Page 6: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/6.jpg)
Image processing and normalization: what is microarray data?Microarray data is summary information from image files that come out of the scanner.Image processing: line up grids, flag bad spots, quantitate.
![Page 7: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/7.jpg)
![Page 8: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/8.jpg)
Looking at data from a single experiment
3-AT vs.No drug
wild-type vs.wild-type
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Log10(Intensity)
Log1
0(Expression Ratio)
Slides: 11120c01 -11121c01
P-value < 0.01
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
P-value < 0.01
Log10(Intensity)
Log1
0(Expression Ratio)
Slides: 11857c01 -11858c01
log10(average intensity)
-2 -1 0 1 2
log 1
0(r
atio
)lo
g 10(r
atio
)
2
1
0
-1
-2
-2 -1 0 1 2
2
1
0
-1
-2
![Page 9: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/9.jpg)
Other types of arrays
![Page 10: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/10.jpg)
Photolithographic arrays (Affymetrix)
Building up oligonucleotides on a surface:
http://www.affymetrix.com/technology/manufacturing/index.affx
![Page 11: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/11.jpg)
Photolithographic arrays (Affymetrix)
aka “GeneChip”
Arrays are typically 25-mers, with “mismatch” control for specificity
![Page 12: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/12.jpg)
Photolithographic arrays (Affymetrix)Advantages:
Density is limited essentially by the 5 micron resolution of scanners (solution: larger arrays).
Well-developed protocols.
“Industry standard” (largely self-driven).
Disadvantages:
Not all probes work well. Affymetrix has evolved a complicated system to compensate for this, but even “believers” use at least four probes per gene, and usually more.
Single color.
Sample preparation typically requires amplification.
Single supplier; historically intellectual property issues. (i.e. comparisons)
![Page 13: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/13.jpg)
• 25,000 oligos / 1 x 3 inches
• Sequence completely flexible
• 60-mers
G
AGTC
A
CGGG
C
TGAA
Ink-jet arrays (Agilent)
Hughes TR et al. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001 Apr;19(4):342-7.
![Page 14: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/14.jpg)
Ink-jet arrays generally agree with spotted cDNA arrays
Yeast IJS array: ~8 oligos per gene Spo vs. SC
cDNA array
mu
ltip
le o
ligos
cDNA array
sin
gle
olig
o
r = 0.96
HXT3 HXT1
HXT4
r = 0.97
![Page 15: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/15.jpg)
Ink-jet arrays (Agilent)Advantages:
User-specified sequences; “no questions asked”
Sensitivity and specificity are defined and exceed requirement for most expression profiling applications; no amplification required
Virtually every 60-mer is functional
Data correlates well with spotted cDNA arrays
Disadvantages:
Density currently limited to ~45,000 spots per array.
Single supplier (although a protocol is in press for making your own synthesizer!)
![Page 16: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/16.jpg)
2-D clusteringStep 1: cut experiments and transcripts
falling below P-value and ratio thresholds
-10 -5 -2 1 2 5 10
fold repression fold induction
transcript response index
exp
erim
ent
ind
ex
44 experimentsx
407 genes
![Page 17: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/17.jpg)
2-D clustering
-10 -5 -2 1 2 5 10
fold repression fold induction
Step 2: cluster experiments and transcriptstranscript response index
exp
erim
ent
ind
ex
RHO O/XPKC O/X
ste mutants
treatment withalpha-factor
Data from Roberts et al., Science (2000)
![Page 18: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/18.jpg)
K = 10 #1 #2 #3
There are many types of clustering. One example: K-means (must choose K)
See: Sherlock G. Analysis of large-scale gene expression data.Curr Opin Immunol. 2000 Apr;12(2):201-5.
![Page 19: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/19.jpg)
Basics of clustering freeware: Eisen’s “Cluster” and “Treeview”
Mike Eisen's web site: rana.lbl.gov/EisenSoftware.htm
“Cluster” loads an Excel file (save as tab-delimited text) in the following format:
Cluster
Treeview
(also: “TreeArrange” - http://monod.uwaterloo.ca/downloads/treearrange/)There are also many commercial programs available.
![Page 20: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/20.jpg)
mRNA
protein
nucleus
cell
![Page 21: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/21.jpg)
Microarray expression data
Co-regulated groups of genes
Functional categories
Predict functions of new genes
cis, trans regulators
![Page 22: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/22.jpg)
GO-Biological Process categories
Broad
Mid-level
Narrow eye pigment metabolism
eye morphogenesis
pigment metabolism
striated muscle contraction
ATP biosynthesis
vision
CNS development
insulin secretion
Very Broadmetabolism
163
137
21
36
25
33
34
1548
# annotated genes(mouse)
development 2341
![Page 23: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/23.jpg)
GO-Biological Process hierarchy
eye pigment metabolism
eye morphogenesis
pigment metabolism
CNS development
metabolism
development
![Page 24: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/24.jpg)
Other types of categorical annotations:
KEGG, EC numbers (describe biochemical “pathways”)
MIPS, YPD (yeast databases – older than GO)
Results of individual studies (localization, 2-hybrid screens, protein complexes, etc.
Sequence motifs, structural domains (pfam, SMART)
![Page 25: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/25.jpg)
Cluster labelamino acid metabolismarginine biosynthesisarginine catabolismaromatic AA metabolismasparagine biosynthesisbranched chain AA synthlysine biosynthesismethionine biosynthesissulfur AA tnsprt, metabadenine biosynthesisaldehyde metabolismbiotin biosynthesiscitrate metabolismergosterol biosynthesisfatty acid biosynthesisgluconeogenesisNAD biosynthesisone-carbon metabolismpyridoxine metabolismthiamin biosynthesis 1thiamin biosynthesis 2hexose transportsodium ion transportpolyamine transportnucleocytoplasmic transportribosome/RNA biogenesisribosomal proteinstranslational elongationprotein foldingsecretionprotein glycosylationvesicle-mediated transportproteasomevacuole fusionmitoribosome/respirationMitochond. electron trans.iron transport/TCA cycleChromatin/transcriptionhistonesMCM2/3/6/CDC47DNA replicationmitotic cell cycleCLB1/CLB6/BBP1cytokinesisdevelopmentpheromone responseconjugationsporulation/meiosisresponse to oxidative stressstress/heat shock
Sample genesTRP4, HIS3ARG1, ARG3CAR1, CAR2ARO9, ARO10ASN1, ASN2ILV1,2,3,6LYS2, LYS9MET3,16,28MUP1, MHT1ADE1,4,8AAD4,14,16BIO3,4CIT1,2ERG1,5,11FAS1,FAS2PGK1, TDH1,2,3BNA4,6GCV1,2,3SNO1, SNZ1THI5,12THI2,20HXT4,GSY1ENA1,2,5TPO2,3KAP123,NUP100MAK16,CBF5RPS1A,RPL28TEF1,2SSA1,HSP60VTH1,KRE11ALG6,CAX4VPS5,IMH1RPN6,RPT5VTC1,3,4,PHO84MRPL1,MRPS5ATP1,COX4FRE1,FET3SNF2,CHD1,DOT6HTA1,HHF1MCM2,3,6RFA1,POL12SPC110,CIN8CLB1,6CTS1,EGT2PAM1,GIC2FUS3,FAR1CIK1,KAR3SPO11,SPO19GDH3,HYR1 HSP104,SSA4
Candidate regulatorGCN4ARG80/81ARG80/81/UME6/RPD3ARO80GCN4/HAP1/HAP2LEU3, GCN4LYS14CBF1, MET28, MET32MET31,MET32BAS1, BAS2, GCN4
RTG3ECM22/UPC2INO4GCR1
THI2/THI3THI2/THI3GCR1NRG1,MIG1HAA1RRPE-binding factorPAC/RRPE-binding factors
HAC1,ROX1RLM1XBP1
RPN4PHO4
HAP2/3/4/5MAC1/RCS1/AFT1/PDR1/3
HIR1,HIR2ECBMCBHCM1FKH1ACE2,SWI4
MATALPHA2,STE12KAR4NDT80ROX1,MSN2,MSN4MSN2,MSN4
249
gen
es1,
226
gen
esNon-overlapping yeast gene expression
clusters424 experiments
Chua et al., 2004
![Page 26: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/26.jpg)
Analyzing clusters:
amino acid biosynthesis (p<10-
14)**amino acid metabolism (p<10-
14)**
methionine metabolism (p=1.07×10-7)
**When testing clusters against many different types of categorical annotations, should consider correcting for multiple-testing, and also consider
that categories are often not independent
![Page 27: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/27.jpg)
**http://area51.med.utoronto.ca/FUNSPEC.html
![Page 28: Microarrays Lecture Slides Courtesy of Dr. Tim Hughes t.hughes@utoronto Outline:](https://reader036.vdocuments.us/reader036/viewer/2022062315/56814f6f550346895dbd249b/html5/thumbnails/28.jpg)