Analysis of SAGE Data: An Introduction

1
Analysis of SAGE DataAn Introduction

• Kevin R. Coombes
• Section of Bioinformatics

2
Outline

• Description of SAGE method
• Preliminary bioinformatics issues
• Description of analysis methods introduced in
  early paper
• Review of literature statistics and SAGE

3
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 270484-487
4
How does SAGE work?
3.(c) Discard loose fragments.
9. Sequence and record the tags and frequencies.
5
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
6
What does the data look like?
7
From tags to genes

• Collect sequence records from GenBank that are
  represented in UniGene
• Assign sequence orientation (by finding poly-A
  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
8
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)

9
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

10
Reliable tag-gene assignments
11
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 2761268-1272
12
Variability in SAGE libraries
13
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 2761268-1272
14
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

15
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 2761268-1272
16
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 2761268-1272
17
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.

18
Alternative Analytic Methods

• Audic and Claverie, Genome Res 1997 7986-995
• Chen et al., J Exp Med 1998 91657-1668
• Kal et al., Mol Biol of Cell 1999 101859-1872
• Michiels et al., Physiol Genomics 1999 183-91
• Stollberg et al., Genome Res 2000 101241-1248
• Man et al., Bioinformatics 2000 16953-959

19
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

20
Chen et al.

• Assume
• equal sample sizes
• tag has concentration X, Y in two samples
• Look at W X/(XY)
• Use a symmetric Beta prior distribution with a
  peak near 0.5 (since most genes dont change)
• Use Bayes theorem to compute posterior
  probability of threefold difference in expression

21
Unequal sample sizes

• This analysis generalizes easily to the case of
  unequal size SAGE libraries
• Lal et al., Cancer Res 1999 595403-5407
• This method is used at the NCBI SAGEmap web site
  for online differential expression queries
• http//www.ncbi.nlm.nih.gov/SAGE

22
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 table

23
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

24
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

25
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

26
Stollberg et al.
27
Man et al.

• Compares specificity and sensitivity of different
  tests for differential expression
• Audic and Claverie
• Kal
• Fishers exact test
• Monte Carlo simulation of experiments
• Findings
• Similar power at high abundance
• Kal has highest power at low abundance

28
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?

29
Additional SAGE references

• Review
• Madden et al., Drug Disc Today 2000 5415-425
• Online Tools
• Lash et al., Genome Res 2000 101051-1060
• van Kampen et al., Bioinformatics 2000
  16899-905
• Comparison of SAGE and Affymetrix
• Ishii et al., Genomics 2000 68136-143
• Combine SAGE and custom microarrays
• Nacht et al., Cancer Res 1999 595464-5470
• Mapping SAGE data onto genome
• Caron et al., Science 2001 2911289-1292
• Data mining the public SAGE libraries
• Argani et al., Cancer Res 2001 614320-4324