Bioinformatics Applications of Machine Learning

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Bioinformatics Applications of Machine Learning

• Brian Parker
• NICTA Life Sciences

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Outline

• Bioinformatics/computational biology data
  analysis of molecular biology datasets
• Aims of this lecture To introduce-
• Some background molecular biology and
  biotechnology e.g. microarrays, expressed
  sequence tags (ESTs)
• Some bioinformatics applications of the machine
  learning methods covered in the lectures so far,
  and some of the issues and caveats specific to
  such datasets.

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Overview cont

• Applications-
• Unsupervised and supervised classification of
  expression microarrays
• Clustering of EST data and EST sequence alignment
  and discussion of genomic distance measures

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Background molecular biology

• Central dogma of molecular biology
• DNA-gt transcribed-gt RNA -gt translated -gt
  protein
• Protein has certain tertiary structures to carry
  out function e.g. structural elements, enzymes
  for metabolic processes, gene regulation etc.

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Background molecular biology cont

• DNA is double-stranded polymer of 4 nucleotides
  (Adenine(A), Cytosine (C), Guanine (G), Thymine
  (T))
• A gene is a segment of DNA coding for a protein.
• mRNA is single-stranded.
• Protein is polymer of 20 amino acids
• The genetic code maps from the 4-letter alphabet
  of DNA to the 20letter alphabet of protein
• Note Recent extension of central dogma---
  noncoding RNAs not translated into protein and
  directly regulate expression of other genes

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Background molecular biology cont

• These stages lead to several higher-level
  networks
• Gene regulatory networks, pathways
• Protein-protein interaction networks
• Biochemical networks

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Videos

• http//www.wehi.edu.au/education/wehi-tv/dna/index
  .html

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High-throughput data analysis

• Omics high throughput datasets
• Following the central dogma, we have
• Genomics from high-throughput sequencing of DNA
  (genome)
• Transcriptomics from high-throughput sequencing
  of RNA and transcribed genome
• Proteomics from high-throughput analysis of
  protein
• Metabolomics from analysis of biochemical
  metabolites

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Microarray technology

• Simultaneously measure the expression of 10s of
  thousands of genes.
• Several technologies e.g. Spotted and
  oligonucleotide arrays (Affymetrix)
• Large array of probes designed as a complementary
  match to the transcript of interest.

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Microarray technology

• Relies on hybridization i.e. single-stranded
  nucleic acids bind to their complement.
• mRNA extracted-gt reverse transcriptase -gt cDNA
  (biotin-labelled)
• -gt hybridize to array -gt scan image (amount of
  fluorescence relates to amount of mRNA)
• -gt convert to expression levels.
• Important to normalize arrays to remove
  variations due to differing lab technique (not
  covered in this lecture).

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Spotted array image
Affymetrix array
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Microarrays

• large p, small n dataset, where n is the number
  of samples and p is the number of features e.g.
  50,000 genes, 100 patient samples is typical
• This is the opposite assumption of earlier
  statistical and machine learning techniques.

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Microarrays

• Can lead to novel problems
• (1) Many techniques assume n lt p e.g. LDA cannot
  be applied directly as covariance matrix is
  under-determined and can not be estimated, so
  feature selection is required.
• (Even where a method e.g. SVMs can handle the
  high dimensionality, feature selection is still
  useful to remove noise genes).

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Microarrays

• (2) Large opportunity for selection bias to occur
  in feature selection.
• (3) Large multiple hypothesis correction problem.
  How to do this without being too conservative?
• (Note we will be talking about expression
  arrays there are other array types such as SNP
  arrays that hybridize with genomic DNA to measure
  copy number, LOH etc)

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Microarray Analysis

• 3 broad problems in microarray analysis (Richard
  Simon)
• class discovery (unsupervised classification)
• (2) class comparison (differential gene
  expression)
• (3) class prediction (supervised classification)

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Hierarchical clustering heat map

• E.g. Sorlie et. al. (2001) reported several
  previously unidentified subtypes of breast cancer
  using clustering.
• (Sorlie et al, Gene expression patterns of
  breast carcinomas distinguish tumor subclasses
  with clinical implications, PNAS)

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Filter methods

• Specific versus non-specific filtering
• Non-specific filtering doesnt use the class
  labels but removes noise genes of low variance
  etc.
• N.B. in clustering, dont do specific filtering
  and then cluster!

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Specific Filtering

• Fold change simplest method ratio of expression
  levels
• (but as microarray data is typically log
  transformed, calculated as difference of means)

• t-statistic (one-way ANOVA F-statistic if gt 2
  samples) problem is that there often isnt
  enough data to estimate variances

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Specific Filtering cont

• Moderated t-statistic. Estimate variance across
  multiple genes.
• Many different versions of moderated variations
  on the t-test (e.g. regularized t-test of Smyth
  (2004) (Limma package in Bioconductor), SAM).
• They combine a gene-specific variance estimate
  with an overall predicted variance (e.g. the
  microarray average) i.e. roughly--

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Where is some measure of group
difference (e.g. difference of means)
is a predicted variance based on all genes, (may
be transformed) and is estimated
variance based on the particular gene. B is a
shrinkage factor that ranges from 0 to 1.
For B 1, denominator is effectively constant
and so we get the fold change. For B 0,
standard t-test without any shrinkage.
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Spike-in experiment results

• Experiment with very small spike-in set (6
  samples)
• (ref. Bioinformatics and Computational Biology
  Solutions Using R and Bioconductor)
• moderated-t better than fold-change better than
  t-statistic

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Embedded and wrapper methods

• Wrapper method uses an outer cross-validation
  select gene set with smallest loss.
• Full combinatorial search is too slow need to do
  forward or backward feature selection
• Embedded e.g. Recursive feature elimination (RFE)
  (Guyon and Vapnik). Uses SVM internal weights to
  rank features removes worst feature and then
  iterate. (original paper had a severe selection
  bias).

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Differential gene expression multiple hypothesis
testing

• Setting a limit with p-value 0.05 is too lax
  due to multiple hypothesis testing.
• Doing a multiple hypothesis correction such as
  Bonferroni correction (multiply p-value by number
  of genes) is too conservative. In practice, some
  in-between value may be chosen empirically.
• This is controlling family-wise error rate
  (FWER) sets the p-value threshold so whole study
  has a defined false positive rate. For an
  exploratory study such as differential gene
  expression, we are willing to accept a higher
  false positive rate.

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False Discovery Rate (FDR)

• In this case, what we really want is to specify
  the proportion of false positives we will accept
  amongst the gene set we have selected as
  significant-- the false discovery rate FDR.
• Several variants of FDR-- an example is the
  q-value of Storey and Tibshirani.

F false positives, T true positives, S
significant features
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Class Prediction

• Can be a classification problem e.g. cancer vs
  normal or a regression problem, e.g. survival
  time
• Simple methods work well in practice due to small
  patient numbers.
• Dudoit, Fridlyand and Speed compared K-nn,
  various linear discriminants and CART.
• Conclusion k-nn and DLDA performed best, and
  ignoring correlation between genes helped DLDA
  vs correlated LDA.

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Selection bias in microarray studies
Because of the high dimensionality and small
sample size of microarray data, it is very likely
that a random gene will by luck correlate with
the class labels. So selecting the best gene set
for classification will give an optimistic bias
if done outside of the cross-validation loop. It
is essential that when using cross-validation,
the test set is not used in any way in each fold
of the cross validation. This means that all
feature selection and (hyper) parameter selection
and model selection must be repeated for each
fold.
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Selection Bias cont
(From Amboise and McLauclan Selection bias in
gene extraction on the basis of microarray
gene-expression data)
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Gene set enrichment analysis (GSEA)

• Previous approaches discussed were univariate
  filter methods, essentially treating each gene
  independently.
• Looking at the overall difference in expression
  of sets of genes that are known, by other
  experiments, to be related ,e.g. part of the same
  pathway or similar gene ontology (GO) annotation,
  can be a more powerful test to find significant
  differences.

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GSEA

• Genes are ranked using a univariate metric
• An enrichment score for the gene set is
  calculated using a Kologorov-Smirnov-like
  statistic
• The significance level of the enrichment score is
  computed using a permutation test (where the
  shuffled labels keep the gene set together).
• A FDR is computed to correct for multiple
  hypothesis testing.

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EST analysis

• Expressed sequence tags (ESTs) are short,
  unedited, randomly selected single-pass sequence
  reads derived from cDNA libraries. Low cost, high
  throughput.
• (cDNA is generated by reverse transcriptase
  applied to RNA)

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EST analysis steps

• (1) They need to be clustered into longer
  consensus sequences (unsupervised classification)
• (2) They can then be sequence aligned against the
  genome for gene-finding etc.
• These two methods require different genomic
  sequence distance measures

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Similarity measures for genomic sequences

• Most data analysis methods use some underlying
  measure of similarity or distance between samples
  either explicity or implicitly and this is a
  major determinant of their performance
• e.g. the hierarchical clustering discussed in
  previous lectures typically has a (dis)similarity
  matrix passed into the function so that the
  particular similarity measure used is decoupled
  from the clustering algorithm

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Similarity measures for genomic sequences

• This idea can be generalized to supervised
  classification and other data analysis even when
  the similarity measure is implicit, it can often
  be algebraically manipulated to make it explicit
  
• (and in this case is the measure is typically a
  dot product--- generalized by kernel methods to
  be discussed in later lectures)

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Similarity measures for genomic sequences

• So, it is important to generate good similarity
  measures between genomic sequences.
• Two broad classes
• Alignment methods and
• Alignment-free methods

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Alignment methods

• Model insertions/deletions and substitutions a
  form of edit distance
• Needleman-Wunsch global alignment
• Based on dynamic programming
• Smith-Waterman local alignment (includes only
  best-matching high-scoring regions)
• BLAST uses a non-alignment-based heuristic to
  quickly rule out bad matches
• Used for sequence alignment and database
  searching.

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Alignment-free methods

• Alignment-based distance measures assume
  conservation of contiguity between homologous
  segments
• Not always the case e.g. ESTs from different
  splice variants or genome shuffling.

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Alignment-free methods

• Based on comparing word frequencies
• D2 statistic number of k-word matches between
  two sequences.
• Can be shown to be an inner product of word-count
  vectors.
• Useful for EST clustering

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Other areas of bioinformatics

• Several other areas of bioinformatics not covered
  here which also use machine learning techniques
• Protein secondary and tertiary structure and
  motif finding
• De novo gene prediction by matching known
  promoter and coding sequence features.