Transcript Analysis of SAGE Data: An Introduction

Analysis of SAGE Data:
An Introduction
Kevin R. Coombes
Section of Bioinformatics
Outline
• Description of SAGE method
• Preliminary bioinformatics issues
• Description of analysis methods
introduced in early paper
• Review of literature: statistics and SAGE
What is SAGE?
• Serial Analysis of Gene Expression
• Method to quantify gene expression
levels in samples of cells
• Open system
– Can potentially reveal expression levels of
all genes: “unbiased” and “comprehensive”
– Microarrays are closed, since they only tell
you about the genes spotted on the array
Ref: Velculescu et al., Science 1995; 270:484-487
How does SAGE work?
1. Isolate mRNA.
2.(a) Add biotin-labeled dT primer:
2.(b) Synthesize ds cDNA.
3.(a) Bind to streptavidin-coated beads.
3.(b) Cleave with “anchoring enzyme”.
3.(c) Discard loose fragments.
4.(a) Divide into two pools and add linker sequences:
4.(b) Ligate.
5. Cleave with “tagging enzyme”.
6. Combine pools and ligate.
7. Amplify ditags, then cleave with anchoring enzyme.
8. Ligate ditags.
9. Sequence and record the tags and frequencies.
From ditags to counts
• Locate the punctuation “CATG”
• Extract ditags of length 20-26 between the
punctuation
• Discard duplicate ditags (including in reverse
direction) -- probably PCR artifacts
• Take extreme 10 bases as the two tags,
reversing right-hand tag
• Discard linker sequences
• Count occurrences of each tag
SAGE software available at http://www.sagenet.org
What does the data look like?
TAG
CCCATCGTCC
CCTCCAGCTA
CTAAGACTTC
GCCCAGGTCA
CACCTAATTG
CCTGTAATCC
TTCATACACC
ACATTGGGTG
GTGAAACCCC
CCACTGCACT
TGATTTCACT
ACCCTTGGCC
ATTTGAGAAG
GTGACCACGG
COUNT
1286
715
559
519
469
448
400
377
359
359
358
344
320
294
TAG
CACTACTCAC
ACTAACACCC
AGCCCTACAA
ACTTTTTCAA
GCCGGGTGGG
GACATCAAGT
ATCGTGGCGG
GACCCAAGAT
GTGAAACCCT
CTGGCCCTCG
GCTTTATTTG
CTAGCCTCAC
GCGAAACCCT
AAAACATTCT
COUNT
245
229
222
217
207
198
193
190
188
186
185
172
167
161
TAG
TTCACTGTGA
ACGCAGGGAG
TGCTCCTACC
CAAACCATCC
CCCCCTGGAT
ATTGGAGTGC
GCAGGGCCTC
CCGCTGCACT
GGAAAACAGA
TCACCGGTCA
GTGCACTGAG
CCTCAGGATA
CTCATAAGGA
ATCATGGGGA
COUNT
150
142
140
140
136
136
128
127
119
118
118
114
113
110
From tags to genes
• Collect sequence records from GenBank that
are represented in UniGene
• Assign sequence orientation (by finding polyA tail or poly-A signal or from annotations)
• Extract 10-bases 3’-adjacent to 3’-most CATG
• Assign UniGene identifier to each sequence
with a SAGE tag
• Record (for each tag-gene pair)
– #sequences with this tag
– #sequences in gene cluster with this tag
Maps available at http://www.ncbi.nlm.nih.gov/SAGE
From tags to genes
• Ideal situation:
– one gene = one tag
• True situation
– one gene = many tags (alternative
splicing; alternative polyadenylation)
– one tag = many genes (conserved 3’
regions)
Sequencing Errors
• Estimated sequencing error rate:
– 0.7% per base (range 0.2% - 1%)
• Affect
– ditags in a SAGE experiment
• can improve by using phred scores and
discarding ambiguous sequences
– tag-gene mappings from GenBank
• RNA better than EST
Reliable tag-gene assignments
Sequence Type
Poly-A signal Annotation
mRNA/cDNA
NA
NA
EST
Yes
3’
EST
Yes
None
EST
Yes
5’
EST
No
3’
SAGE and cancer
• Ten SAGE libraries, two each from
– normal colon
– colon tumors
– colon cancer cell lines
– pancreatic tumors
– pancreatic cell lines
• Pooled each pair
Ref: Zhang et al., Science 1997; 276:1268-1272
N
C
2
0 2 4 6 8 10
Variability in SAGE libraries
0
2
4
6
8
10
NC1
Distribution of tags
• 303,706 total tags
• 48,471 distinct tags
• Distribution
– 85.9% seen up to 5 times (25% of mass)
– 12.7% between 5 and 50 times (30%)
– 0.1% between 50 and 500 times (26%)
– 0.1% more than 500 times (19%)
Ref: Zhang et al., Science 1997; 276:1268-1272
How many tags were missed?
• They simulated to find 92% chance of
detecting tags at 3 copies/cell
• Using binomial approximation
– Get 95% chance for 3 copies/cell
– Only get 63% chance for 1 copy/cell
• Most of what they saw occurred at 1-5
copies per cell
Differential Expression
• Found 289 tags differentially expressed
between normal colon and colon cancer
(181 decreased; 108 increased)
• Method: Monte Carlo simulation.
– 100000 sims per transcript for relative
likelihood of seeing observed difference
– Used observed distribution of transcripts to
simulate 40 experiments.
Ref: Zhang et al., Science 1997; 276:1268-1272
Sensitivity
• Claim: 95% chance of detecting 6-fold
difference
• Method: Monte Carlo
– 200 simulations, assuming abundance of
0.0001 in first sample and 0.0006 in
second sample
Ref: Zhang et al., Science 1997; 276:1268-1272
Weaknesses in Analysis
• Failed to account for intrinsic variability
in samples (which changes depending
on abundance) in assessing significance
• Monte Carlo used observed distribution,
which is definitely not true distribution.
• Sensitivity only measured at one
abundance level.
Alternative Analytic Methods
• Audic and Claverie, Genome Res 1997; 7:986995
• Chen et al., J Exp Med 1998; 9:1657-1668
• Kal et al., Mol Biol of Cell 1999; 10:1859-1872
• Michiels et al., Physiol Genomics 1999; 1:8391
• Stollberg et al., Genome Res 2000; 10:12411248
• Man et al., Bioinformatics 2000; 16:953-959
Audic and Claverie
• Main goal: confidence limits for
differential expression
• Use Poisson approximation for number
of times x you see the same tag.
• Put a uniform prior on the Poisson
parameter; get posterior probability of
see tag y times in new experiment
p(y | x) = (x + y)! / [x! y! 2^(x + y +1)]
• Generalizes to unequal sample sizes
Chen et al.
• Assume
– equal sample sizes
– tag has concentration X, Y in two samples
• Look at W = X/(X+Y)
• Use a symmetric Beta prior distribution with a
peak near 0.5 (since most genes don’t
change)
• Use Bayes theorem to compute posterior
probability of threefold difference in
expression
Unequal sample sizes
• This analysis generalizes easily to the
case of unequal size SAGE libraries
– Lal et al., Cancer Res 1999; 59:5403-5407
• This method is used at the NCBI
SAGEmap web site for online differential
expression queries
– http://www.ncbi.nlm.nih.gov/SAGE
Kal et al.
• Assume the proportion of times you see
a tag has binomial distribution
• Replace with a normal approximation to
compute confidence limits
• Used at http://www.cmbi.kun.nl/usage
• Equivalent to chi-square test on 2x2
Library 1 Library 2
table:
Gene of
Interest
All Other
Genes
x1
x2
N1
N2
Michiels et al.
• First perform overall chi-square test to
decide if the two SAGE libraries being
compared are different.
• Get significance by Monte Carlo
simulation
• Perform gene-by-gene chi-square tests
and use them to rank genes in order of
“most likely to be different”
Stollberg et al.
• Assume binomial distributions
• Model the binomial parameters as a
sum of two exponentials
– fit to the Zhang step function data
• Simulate from this model, adding
– sequencing errors
– nonuniqueness of tags
– nonrandomness of DNA sequences
Stollberg et al.
• Key finding:
– Naively using observed data to fit model
parameters cannot recover the observed
data by simulation
– Maximum likelihood estimate of
parameters that recover the observed data
give very different looking parameters
Stollberg et al.
Model
Observed
Data
Inferred
Predicted Data
Parameters
Unique Tags 15720
25336
15651 (58)
% 1-5
64.16
80.56
63.64 (0.31)
% 6-50
31.04
16.51
31.74 (0.40)
% 51-100
4.38
2.72
4.33 (0.10)
0.215
0.29 (0.01)
% 501-5000 0.42
Man et al.
• Compares specificity and sensitivity of
different tests for differential expression
– Audic and Claverie
– Kal
– Fisher’s exact test
• Monte Carlo simulation of experiments
• Findings
– Similar power at high abundance
– Kal has highest power at low abundance
Questions
• Sample size computations:
– How many tags should we sequence if we
want to see tags of a given frequency?
– How many tags should we sequence if we
want to see a given percentage of tags?
• How many tags are expressed in a
sample?
• Best method for identifying differential
expression?
Additional SAGE references
• Review
– Madden et al., Drug Disc Today 2000; 5:415-425
• Online Tools
– Lash et al., Genome Res 2000; 10:1051-1060
– van Kampen et al., Bioinformatics 2000; 16:899-905
• Comparison of SAGE and Affymetrix
– Ishii et al., Genomics 2000; 68:136-143
• Combine SAGE and custom microarrays
– Nacht et al., Cancer Res 1999; 59:5464-5470
• Mapping SAGE data onto genome
– Caron et al., Science 2001; 291:1289-1292
• Data mining the public SAGE libraries
– Argani et al., Cancer Res 2001; 61:4320-4324