Affymetrix Expression Data

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Affymetrix Expression Data

• Comics Group
• 12-05-2003 Nijmegen
• Tim Hulsen

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General Information (1)

• Affymetrix oligo microarrays HG-U133 A and B
  (human) and MG-U74v2 A, B and C (mouse)
• Updated every two months releases used here
  november 2002 and january 2003
• UniGene-based
• Probes 25mer oligos complementary to the
  sequences of interest
• Probe pairs perfect match (PM) probe and
  mismatch (MM) probe, MM is different from PM in
  the 13th position

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General Information (2)

• Human chips 3269 samples, 44792 fragments, 115
  tissue categories (114 for nov. 2002 release), 15
  SNOMED tissue categories
• Mouse chips 859 samples, 36701 fragments, 25
  tissue categories, 12 SNOMED tissue categories
• Results from all samples within a tissue category
  are combined by generating electronic Northerns
• For each tissue fragment and each tissue category
  is determined
• Median expression value
• Present call (percentage)

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Median expression value

• Expression value intensity
• All expression values that have a present call
  are used to determine the median expression value
• Varies from 0 to 65,000 in human and from 0 to
  97,000 in mouse

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Present Call (Percentage)

• Normalization/scaling procedures (MAS 5.0) are
  used to determine an expression intensity value
  with an associated confidence level to each
  fragment
• When confidence level p for expression is smaller
  than 0.05, the expression intensity for this
  specific fragment in this particular sample is
  called present (P)
• Call values are used to calculate a present call
  percentage (P calls / total calls)

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Snomed category definitions (1)

• SNOMED Systematised Nomenclature of Medicine
• Combines specific categories into more global
  categories, i.e. organ systems
• In human far more useful than in mouse
  (115-gt15,25-gt12)
• Categories like cardiovascular system, digestive
  organs, endocrine gland, female genital system,
  male genital system, musculoskeletal system,
  nervous system, respiratory system, etc.

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Snomed category definitions (2)

• Example cat. 7 hematopoietic system

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Annotation provided
For each fragment, if available

• title
• geneSymbol
• geneAlias
• exemplarAcc
• omimId
• snpId
• refseqId
• refseqprotId
• ncbiNuclId
• ncbiProtId

• unigeneAcc
• unigeneId
• interproId
• pfamId
• swissprotId
• goId
• goFunction
• goProcess
• goComponent
• comment

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Goals Problems

• Goal use data set to see if co-expression
  between orthologous/paralogous gene pairs is
  higher than between unrelated gene pairs, in
  human mouse
• Problem 1 limited annotation
• Problem 2 empty expression profiles
• Problem 3 size of data set

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Limited annotation (1)

• For example for three of the most used protein
  ids
• ncbiProtId (in red), refseqProtId (in green),
  swissprotId (in blue)
• Human Mouse

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Limited annotation (2)

• Solutions
• Smith-Waterman of (SWX) of all Affymetrix
  sequences to the human mouse IPI sets, for
  which orthologs and paralogs were already defined
  -gt IPI id added to database
• Smith-Waterman (SWN) of all Affymetrix sequences
  to each other for better orthology/paralogy
  prediction

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Empty expression profiles

• Lots of genes have no expression at all in any
  tissue category
• Useless for correlation calculation two genes
  with no expression will have a top correlation!
• For human 4114 out of 44792 fragments completely
  no expression in all tissue categories -gt 40678
  left
• For mouse 6791 out of 36701 fragments completely
  no expression in all tissue categories -gt 29910
  left

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Size of data set

• Correlation between gene pairs is calculated the
  number of pairs is (x2-x)/2 for x genes -gt
  millions of data points
• Number of gene pairs is already brought down by
  the no expression gene removal in human from
  1,003,139,236 to 827,329,503, in mouse from
  673,463,350 to 447,289,095
• For some quick analyses, sets of e.g. 1000
  randomly selected genes were used -gt 499,500 gene
  pairs

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Uncentered Correlation

• Uncentered from 0 to 1
• UC(X,Y) S( X / ( sqrt ( S( X2 / N ) ) ) ( Y /
  ( sqrt ( S( Y2 / N ) ) ) ) / N
• Calculated correlations between gene pairs were
  used to see if the co-expression for orthologous
  pairs and/or paralogous pairs is higher than for
  unrelated pairs
• This was measured by using the KEGG Pathway map
  (release 25)
• The best, however not completely convincing,
  result was found using PCP and not ME

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Correlation KEGG Pathway Check

• Data points above a correlation threshold of 0,9
  and 1,0 were left out because of very low numbers
  (irreliability)
• Only orthologous conserved gene pairs have a
  higher accuracy when increasing the correlation
  threshold
• May be a combination of PCP and ME should be
  used
• Another measure could be used same GO category
  instead of KEGG, GO is already annotated by
  Affymetrix
• Lots of genes have only an expression value in
  one tissue this correlation method is not really
  suitable -gt mutual information analysis

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Mutual Information

• For each tissue category 0 or 1 (ME/PCP value
  below/above a specified threshold)
• x0 of 0s, x1 of 1s
• x00 of 0-0 pairs, x01 of 0-1 pairs, x10
  of 1-0 pairs, x11 of 1-1 pairs
• Entropy per gene -(x0ln(x0)x1ln(x1))
• Entropy per gene pair -(x00ln(x00)x01ln(x01)x
  10ln(x10)x11ln(x11))
• MI Entropy(1) Entropy(2) Entropy(1,2)
• 0ltMIlt0,693147

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MI GO Category Check

• Mutual information check using GO Biological
  Process 3rd level of specification
• Horizontal axis shows log(MI)
• Different lines different thresholds for
  defining as a 0 or a 1
• Accuracy indeed seems to be higher for pairs
  with much mutual information, but there is also a
  peak at -9ltlog(MI)lt-8
• Orthologous/paralogous pairs not checked yet

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Future plans

• Complete mutual information analysis, using both
  KEGG Pathway and GO databases look at
  orthologous and paralogous gene pairs too
• Check alternative splicing
• Speed license ends at the end of June