Summaries of Affymetrix GeneChip probe level data

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Summaries of Affymetrix GeneChip probe level data

• By Rafael A. Irizarry
• PH 296 Project, Fall 2003
• GroupKelly Moore, Amanda Shieh, Xin Zhao

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Microarrays Many Probes for One Gene
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Affymetrix GeneChip Arrays

• High density oligonucleotide array technology is
  widely used in many areas of biomedical research
  for quantitative and highly parallel measurements
  of gene expression
• Most popular technology for quantitative and
  highly parallel measurements of gene expression
  is Affymetrix GeneChip arrays
• Used to obtain gene expression measures by
  summarizing probe level data

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Affymetrix Chips

• Each gene or portion of a gene is represented by
  16 to 20 oligonucleotides of 25 base-pairs, i.e.,
  25-mers. A mRNA molecule of interest (usually
  related to a gene) is represented by a probe set
  composed of 11-20 probe pairs of these
  oligonucleotides.
• Probe a 25-mer.
• Perfect match (PM) A 25-mer complementary to a
  reference sequence of interest (e.g., part of a
  gene).
• Mismatch (MM) same as PM but with a single
  homomeric base change for the middle (13th) base
  (transversion purine lt-gtpyrimidine, G lt-gtC, A
  lt-gtT) .
• Probe-pair a (PM,MM) pair.
• Probe-pair set a collection of probe-pairs (16
  to 20) related to a common gene or fraction of a
  gene.
• AffyID an identifier for a probe-pair set.
• The purpose of the MM probe design is to
  measure non-specific binding and background
  noise.
• After scanning the arrays hybridized to labeled
  RNA samples, intensity values PMij and
• Mmij are recorded for arrays i 1,., I and
  probe pairs j1,, J, for any given probe set.

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Affymetrix GeneChips

• After scanning the arrays hybridized to labeled
  RNA samples, intensity values PMij and MMij are
  recorded for arrays i1,,I and probe pairs
  j1,,J for any given probe set
• Probe intensities summarized for each probe set
  to define a measure of expression

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Combining Measurements across Arrays

• Data on G genes x n arrays G x n genes-by-arrays
  data matrix
• Expression measure M log2( Red intensity /
  Green intensity)
• Array1 Array2 Array3
  Array4 Array5
• Gene1 0.46 0.30 0.80 1.51
  0.90 ...
• Gene2 -0.10 0.49 0.24 0.06 0.46 ...
• Gene3 0.15 0.74 0.04 0.10 0.20 ...
• Gene4 -0.45 -1.03 -0.79 -0.56
  -0.32 ...
• Gene5 -0.06 1.06 1.35 1.09
  -1.09 ...
• ..

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Three Competing Models

• Affymetrix MicroArray Suite (MAS)
• MAS versions 4, and 5
• dChip
• Li and Wong, HSPH
• The log scale robust multi-array analysis (RMA)
• Bioconductor affy package.
• by Bolstad, Irizarry, Speed, et al

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1st Version of Affymetrix Analysis Software

• Used an average over probe pairs of differences
  PMij-MMij, j1,J for each
  array i
• A model for this Average Distance (AD) is
  PMij - MMij ?ieij, j1,,J
  where ?i is the expression quantity on array I
• AD is an appropriate estimate of ?i if the error
  term eij has equal variance for j1,J
• This assumption does not hold for GeneChip
  probe level data since probes with larger mean
  intensities have larger variances

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Model 1 MicroArray Suite Version 5
MAS 5

• MicroArray Suite version 5 uses
• where
• MM is an adjusted MM that is never bigger than
  PM
• Tukey biweight is a robust average procedure with
  weights f(x)c2/61-(1-x2/s2) 3 xltc

PM-MM values for probe pairs
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Model 2 Robust Multi-chip Analysis
dChip

• Each probe responds roughly linearly
• over a moderate range
• some probes are outliers
• Variation of a specific probe across multiple
  arrays could be considerably smaller than the
  variance across probes within a probe set. To
  account for this strong probe affinity effect,
  the following model was proposed.
• Multiplicative Model
• The probe affinity effect is represented by j.
  
• When multiple arrays are available, the
  expression index is defined as the maximum
  likelihood estimate of the expression parameters
  ?i.
• Robust Fit
• identify outliers by heuristic remove
• standard robust method iteratively re-weighted
  least squares
• The software package dChip can be used to fit
  this model and obtain what we refer to as the
  dChip expression measure.

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Model 3 A log scale linear additive model
RMA

• Appropriately removing background and normalizing
  probe level data across arrays results in an
  improved expression measures motivated by a log
  scale linear additive model
• T represents the transformation that background
  corrects, normalizes, and logs the PM
  intensities.
• represents the log2 scale expression value
  found on array i.
• represents the log scale affinity effects
  for probes j.
• represents error.
• A robust linear fitting procedure, such as median
  polish, was used to estimate the log scale
  expression values .
• The resulting summary statistic is referred to as
  RMA.
• Recent results suggest that subtracting MM as a
  way of correcting for non-specific binding is not
  always appropriate. Until a better solution is
  proposed, simply ignoring these values is
  preferable.

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Assessment Criteria

• Data from spike-in and dilution experiments to
  conduct various assessments on the MAS 5.0, dChip
  and RMA expression measures.
• The measures of expression are assessed according
  to three criteria
• (i) the precision of the measures of expression,
  as estimated by standard deviations across
  replicate chips
• (ii) the consistency of fold change estimates
  based on widely differing concentrations of
  target mRNA hybridized to the chip
• (iii) the specificity and sensitivity of the
  measures ability to detect differential
  expression, presented in terms of receiver
  operating characteristic (ROC) curves.

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Study Design

• Dilution Study
• Two sources of cRNA, human liver tissue and a
  central nervous system cell line (CNS), were
  hybridized to human arrays (HG-U95A) in a range
  of dilutions and proportions.
• Data from six groups of arrays that had
  hybridized liver and CNS cRNA at concentrations
  of 1.25, 2.5, 5.0, 7.5, 10.0 and 20.0 µg were
  studied.
• Five replicate arrays were available for each
  generated cRNA (n60 total).

• Spike-in Studies
• Different cRNA fragments were added to the
  hybridization mixture of the arrays at different
  pM concentrations.
• The cRNAs were spike-in at a different
  concentration on each array arranged in a cyclic
  Latin square design with each concentration
  appearing once in each row and column.
• Two different data sets from
• (i) Affymetric
• (ii) GeneLogic

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Study DesignAffymetrix spike-in experiment
This data set consists of 3 technical replicates
of 14 separate hybridizations of 42 spiked
transcripts in a complex human background at
concentrations ranging from 0.125pM to 512pM.
Thirty of the spikes are isolated from a human
cell line, four spikes are bacterial controls,
and eight spikes are artificially engineered
sequences believed to be unique in the human
genome.
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Resultsmeasure of precision R2

• A common measure of precision to compare
  replicate arrays is the squared correlation
  coefficient, R2.
• For the dilution data, average R2 is computed
  over all 120 pairs of replicates (2 tissues 6
  concentrations 10 different pairs in each group
  of 5 replicates).
• MAS5.0 0.990 dChip 0.993 RMA 0.995
• The differences between the R2 averages are
  statistically significant. RMA outperformed
  dChip, which in turn outperformed MAS5.0.
• However, because of the strong probe affinity
  effect, GeneCHip arrays will in generall have R2
  values close to 1. The gene-specific log
  expression SD across replicates is a more
  informative assessment.

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Resultsmeasure of precision gene-specific SD

• The SD of the expression values (log2 scale)
  across the five replicated in each of the 6
  concentration groups were computed.
• Smooth curves were then fitted to scatter plots
  of these SD values versus average expression
  value (log2 scale).

The above plot showed that RMA had a smaller SD
at all levels of expression.
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Results signal detection

• To insure that signal detection was not
  sacrificed for the gains in noise reduction, the
  ability of the expression measures to detect the
  increase in cRNA across the concentration groups
  was examined.
• The average slope, over all genes, of the
  expression versus concentration lines on the
  log-log scale was computed as a summary of signal
  detection.
• Liver cells MAS5.0 0.65 dChip 0.59 RMA 0.67
• CNS cells MAS5.0 0.63 dChip 0.58 RMA 0.67
• Since every fold increase in concentration of the
  target sample should give rise to the same fold
  increase in an expression measure, a line fitted
  on the log-log scale should have slope 1. For
  reasons we dont understand, all three measures
  lead to slopes well below 1, but on the
  criterion, RMA and MAs5.0 performed similarly,
  while dChip had a slightly smaller signal.
• RMA has similar accuracy but better precision
  than the other two summaries.

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Resultsmeasure of consistency fold change
across concentrations

• Observed fold change in expression measures is
  used to assess differential expression.
• While the Affymetrix protocal calls for 15 µg of
  RNA, in practice the amount of target mRNA
  available for the hybridization reactions can
  differ greatly depending on the cells or tissue
  type under study.
• Because fold change is a relative measure,
  estimates should be independent of the amount of
  RNA that is hybridized to the arrays. It is
  desirable to have estimated fold changes in
  expression largely independent of the amount of
  target mRNA used.
• The correlation of fold change estimates from the
  different concentrations was computed for each of
  the three expression measures. MAS5.0
  0.85 dChip 0.95 RMA 0.97
• RMA provides more consistent estimates of fold
  change.

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Resultsmeasure of consistency fold change
across concentrations
Log (base 2) fold change estimates of gene
expression between liver and CNS samples computed
from arrays hybridized to 1.25 µg of cRNA were
plotted against the same estimates obtained from
arrays hybridized to 20 µg for all three measures.
RMA provides more consistent estimates of fold
change.
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Resultsspecificity and sensitivity

• Successful fold change analysis will detect all
  and only genes that are differently expressed due
  to biological variation.
• In the spike-in experiments arrays were
  hybridized to the same background, successful
  differential expression analyses should identify
  only the spiked-in genes as being differentially
  expressed.
• 10 pairs of arrays were chosen at random from
  both Affymetrix and GeneLogic spike-in studies.
  For each of these pairs , estimates of fold
  change were computed using the three expression
  measures. Then, for a large range of cut-off
  values, the number of false positives and the
  number of true positives were computed.
• ROC curves were created by plotting the true
  positive rates (sensitivity) versus false
  positive rates (1-specificity).

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Resultsspecificity and sensitivity

• Area under ROC curves can be used to compare
  specificity and sensitivity of competing tests.
• The ROC curves below showed that the RMA curves
  dominated the dCHip and MAS5.0 curves. Thus the
  differential expression calls obtained with RMA
  have higher sensitivity and specificity then
  those obtained with the other two measures.

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Resultsspecificity and sensitivity

• To understand why fold change analysis using RMA
  has better sensitivity and specificity, we looked
  at
• versus
• plot for expression Xg and Yg from two arrays
  being compared for all genes, g1,, G.
• M vs. A plots are useful in the way that log
  fold change (the quantity of most interest) is
  represented on the y-axis and average absolute
  log expression (another quantity of interest) on
  the x-axis.
• The plots on next slides are produced by
  selecting one array from one of the Affymetrix
  spike-in experiments to use as a reference and
  then computing Mg and Ag for the comparisons of
  that array with all other arrays in the
  experiment using MAS5.0, dChip, and RMA.

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Resultsspecificity and sensitivity
In these plots, the colored numbers represent the
log2 fold change in concentrations of spiked-in
genes. The red points represent non-spiked-in
genes with a fold change larger than 2. Using
RMA, the plot has fewer red points, showing
smaller variance, especially for genes with lower
absolute expression. This resulted in better
detection capability of genes spiked-in at
different concentrations.
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Resultsspecificity and sensitivity

• The color box plots of fold change estimates
  demonstrated that RMA produces fold changes
  closer to 1 for genes that are not changing than
  those for MAS5.0 , with those for dCHip being in
  between.
• The interquartile ranges of log2 fold change for
  equivalently expressed genes were 0.92, 0.22 and
  0.19 for MAS5.0, dChip and RMA, respectively.

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Conclusions

• Through the analyses of dilution and spike-in
  data sets it was shown that RMA performs better
  than MAS 5.0 and dChip, specifically
• RMA has better precision
• RMA provided more consistent estimates
• RMA provided higher specificity and sensitivity
  when using fold change analysis to detect
  differential expression
• This greater sensitivity and specificity of RMA
  in detection of differential expression provides
  a useful improvement for researchers using the
  Affymetrix GeneChip technology

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Improvement in Models

• Affymetrix Suite gets better every year
• MAS 7 is expected to be a multi-chip model
• MAS 5.0 estimation does a reasonable job on probe
  sets that are bright
• Metabolic and structural genes
• These are most often reported in papers
• dChip and RMA do better on genes that are less
  abundant
• Signalling proteins
• transcription factors

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Introduction for practice project

• Goalspractice our data set using RMA and MAS 5
  normalization methods and compare the expression
  results to test the conclusion of this paper.
• Gene chipsHG-U133A/B Affymetrix GeneChip set
• Study design case-control study
• Exposedbenzene-exposed shoe workers ,6
  samples
• Controls clothing factories workers, 6
  samples
• Matched on gender ,age and smoking
• Samples 6 pairs matched people gene
  lymphocyte RNA
• Output 2,129 genes was significantly different
  in people exposed to high levels of benzene
  compared to matched unexposed subjects.
  Expression of 964 of these genes was decreased
  and 1165 were increased.(RMA method)

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Figure1 Measure of precision gene-specific SD

• We compared the exposed group(x1) vs unexposed
  group(x2) expression value Ag1/2(log(x1)log(x2))
  in genechipA to its Standard deviation here.
• Smooth curves were then fitted to scatter plots
  of these SD values versus average expression
  value (log2 scale).
• It is showed that RMA had a smaller SD than MAS 5
  that means the precision is better.

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Figure 2 M vs A plot
The plots are produced by unexposed(x1)/exposed(x2
) arrays in both chip A and B computing Mg and
Ag. Using RMA, shows smaller variance as compared
to MAS 5.0 which also supports results discussed
in the paper.
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Figure 3 Boxplot of log fold change (M) in
RMA(1) and MAS5(2)

• RMA produces fold changes closer to 1 for genes
  that are not changing than those for MAS5.0.
• The interquartile ranges of log2 fold change for
  equivalently expressed genes were 0.37 and 0.19
  for MAS5.0 and RMA, respectively.

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Remarks

• We were able to support the results,according to
  the criteria outlined in the paper by using the
  RMA and MAS 5.0 techniques on our own data.
• We also found that as compared to MAS 5.0,
• RMA has better precision
• RMA provided more consistent estimates
• RMA provided higher specificity and sensitivity