Download used for MS Thesis Defense - Bioinformatics Tools for Cerebellar

Finding Functional Gene
Relationships Using the
Semantic Gene Organizer (SGO)
Kevin Heinrich
Master’s Defense
July 16, 2004
Outline
• Problem / Goals
• Related Work
• Information Retrieval
– Vector Space Model
– Latent Semantic Indexing (LSI)
• Biological Databases
• SGO Use & Results
Problem
• Biological tools are creating vast amounts
of data.
• Current techniques are time-consuming
and expensive.
• Want to know phenotype (function) from
genotype (structure/sequence).
Goals
• Develop a tool to aid researchers in finding
and understanding functional gene
relationships.
• Use information that covers whole
genome, e.g. literature.
Related Work
• Jenssen et al. (2001) developed PubGene.
– Literature network
– Assigns functional association if there is a cooccurrence of gene symbols
• Wilkinson and Huberman (2004) expanded this
idea to find communities of related genes.
• Yandell and Majoros (2002) use natural
language processing techniques to identify
nature of relationships.
Related Work
• Most all literature-based techniques rely
on term co-occurrence.
• What about gene aliases?
• Solution: Apply a more robust technique.
Information Retrieval
Vector Space Model
• Documents are parsed into tokens.
• Tokens are assigned a weight of, wij, of ith token
in jth document.
• An m x n term-by-document matrix, A, is
created where
 
A  wij
– Documents are m-dimensional vectors.
– Tokens are n-dimensional vectors.
Information Retrieval
Term Weights
• Term weights are the product of a local and global
component
wij  lij g i d j
• tf
lij  f ij
f

 f 
ij
• idf
gi
j
ij
j
• idf2
gi  log 2
n
 f 
ij
j
1
Information Retrieval
Term Weights (cont’d)
• log-entropy
lij  log 1  f ij 
  pij log 2 pij 
 j

gi  1  

log 2 n




pij 
f ij
f
ij
j
• Goal is to give distinguishing terms more weight.
Information Retrieval
Query & Similarity
• Queries are represented by a pseudo-document
vector
q0  g1 , g 2 ,, g m 
• Similarity is the cosine of the angle between
document vectors.
 
qdj
sim q, d j   cos  j    
q  dj
m
g w
k
k 1
m
w
k 1
2
kj
kj
m
2
g
 k
k 1
Information Retrieval
Latent Semantic Indexing (LSI)
LSI performs a truncated SVD on
A = UΣVT
• U is the m x n matrix of eigenvectors of AAT
• VT is the r x n matrix of eigenvectors of ATA
• Σ is the r x r diagonal matrix containing the r nonnegative
singular values of A
• r is the rank of A
A rank-k approximation is given by Ak = UkΣkVkT
Information Retrieval
LSI (cont’d)
• Document-to-document similarity is
A A  Vk k Vk k 
T
T
k
• Queries are projected into low-rank
approximation space
q  q Uk
T
0
1
k
Information Retrieval
LSI (cont’d)
• Scaled document vectors can be computed once
and stored for quick retrieval.
• The lower-dimensional space forces queries and
documents to be compared in a more
conceptual manner and saves storage.
• Choice of number of factors is an open question.
• End Effect: LSI can find similarities between
documents that have no term co-occurrence.
Information Retrieval
Evaluation Measures
• Precision – ratio of relevant returned documents to the
total number of returned documents.
• Recall – ratio of relevant returned documents to the total
number of relevant documents.
• Goal is to have high precision at all levels of recall.
• Systems are often evaluated by average precision (AP),
which is the average of 11 interpolated precision values
at the decile ranges.
Biological Databases
MEDLINE
• MEDLINE (NLM)
– Contains 14+ million references to journal
articles with a concentration in medicine
– Span over 4,600 journals worldwide
– 1966 to present
– ~500,000 citations added annually
– Each citation is manually indexed with MeSH
terms.
Biological Databases
PubMed
• PubMed
– Retrieves articles from MEDLINE and other
journals.
– Can be queried via any combination of
attributes.
Biological Databases
LocusLink
• NCBI human-curated database
• Single query interface to a comprehensive
directory for genes and gene reference
sequences for key genomes.
• Provides links to related records in PubMed and
other citations when applicable.
• Provides RefSeq Summary of gene function and
links to key MEDLINE citations relevant to each
gene.
Biological Databases
Overview
• MEDLINE has lots information
– Not all articles relate to genes
– Gene terminology problem
• LocusLink does not cover all relevant
citations, but a representative few.
Biological Databases
Gene Document Construction
• Concatenate titles and abstracts of MEDLINE
citations cross-referenced in Human, Rat, and
Mouse LocusLink entries.
• Sequencing abstracts included – noise
• LocusLink references are not comprehensive, so
recall of all relevant abstracts is not guaranteed.
SGO
• Primarily uses LSI to rank genes.
• Enables user to specify query method
– Gene query
– Keyword query
– Number of factors
– Show latent matches
• Saves previous query sessions.
SGO
Interface
SGO
Interface (cont’d)
SGO
Trees
• Unfortunately, ranked lists mean little to
biologists.
• Pairwise distances can be formed into a matrix
 
D  d ij
d ij  1 cos  ij
where cos  ij is the similarity between
documents i and j
SGO
Trees (cont’d)
• Fitch-Margoliash (1967) method in
PHYLIP is applied to D to generate
hierarchical trees.
• Thresholds can be applied to self-similarity
matrix to produce graphs.
SGO
Hierarchical Tree
SGO
Graph or Nodal Tree
SGO
Coding Issues
• Web interface – must be interactive
– Queries are processed on click
– Document collections are parsed offline
– Trees are constructed offline
• Storage will eventually become an issue.
Results
Test Data Set
• 50 gene test data set was
constructed.
– Alzheimer’s Disease
– Cancer
– Development
• Reelin signaling pathway
used as basis for evaluation
– 5 primary genes (directly
associated)
– 7 secondary genes (indirectly
associated)
Results
Primary AP
• AP for 5 primary
genes
– 61% for 5 factors
– 84% for 25 factors
– 84% for 50 factors
Results
Secondary AP
• AP for 12
secondary genes
– 53% for 5 factors
– 59% for 25 factors
– 61% for 50 factors
Results
Comparison
• LSI comparable to tf-idf for 5 primary genes
• Far superior to tf-idf for 12 second genes
– PubMed co-citation identifies 2 of the 7 indirectly
related genes
– Abstract overlap of LocusLink citations fails to identify
any indirectly related genes
• tf-idf fails on many keyword queries
• Tested on Gene Ontology classifications (not
shown)
– Similar tendencies are observed
Results
Abstract Representation
• To simulate scaling
up, decrease
representation of
reelin-related genes
• AP of 47% on
20,856 Human
LocusLink abstracts
Results
Hierarchical Tree
Results
Hierarchical Tree
Results
Hierarchical Tree
Conclusions
• SGO allows genes to be compared to
each other and to keyword (function).
• SGO identifies latent relationships with
promising accuracy.
• SGO is not meant to replace existing
technologies, but to assist researchers
– Verify current results
– Direct future exploration
Future Work
• Scale up to entire genome
• Document construction
• Incorporate structural or other information
for multi-modal similarity
• Test other models e.g. NMF, QR, etc.
• Interactive tree building
• Keep collections current