We are developing software applications to perform meta-analysis of microarray experiments based on standardized experiment annotations aiming to identify similar experiments and cluster experiments. The applications were tested on files obtained from the ArrayExpress public repository. Annotation terms were used to compute experiment dissimilarities to find experiments related to a query experiment. These applications may motivate efforts of bench biologists to better annotate experiments

Christian J. Stoeckert, Jr.

Elisabetta Manduchi

Jie Zheng

Junmin Liu

English

Christian Stoeckert, Jr.

Crossref

Annotation-based meta-analysis of microarray experiments

Nature Precedings

Annotation-based Meta-analysis of Microarray Experiments 
Jie Zheng, Junmin Liu, Elisabetta Manduchi, Christian J. Stoeckert, Jr. 
Center for Bioinformatics, Department of Genetics, University of Pennsylvania School of Medicine, Philadelphia, USA
Methods
1. Parkinson et al., Nucleic Acids Res. 2007;35 (Database issue):D747.
2. Rayner et al., BMC Bioinformatics 7:489. 
3. Whetzel et al., Bioinformatics. 2006; 22(7): 866.
Two sets of keywords searches were performed
- keywords: insulin or glucose found in 76 experiments
- keywords: aging, longevity, or lifespan found in 88 experiments
References:
ABSTRACT
Annotation Information Of Experiments 
 Source Of Experiments
Experiments in ArrayExpress1 which are in MAGE-TAB format2 and typically contain 
some annotations using the MGED ontology (MO)3 
 Annotation Components Used And Scores
— StudyName (free-text)
— ExperimentDesignType (MO terms) (scored 1 if annotated, otherwise scored 0)
— ExperimentFactorType (MO terms) (scored 1 if annotated, otherwise scored 0)
— ExperimentFactorValue (free-text or measurement or ontology terms)
— Organism (ontology terms) (scored 1 if annotated, otherwise scored 0)
— BiomaterialCharacteristics (types are ontology terms, values are free-text or   
ontology terms) (scored as # of characteristic types annotated)
— ProtocolType (MO terms) (score 1 if annotated, otherwise scored 0)
How well an experiment annotated was evaluated by the sum of the scores of the 
various annotation components. The higher scores indicate richer annotation.
 Dissimilarity Measures Using Jaccard Or Kulczynski Distance
For each annotation component, let A and B be the sets of annotation terms 
for the two experiments, respectively.
Jaccard Distance = 1 - |A ∩ B| / |A U B|   
Kulczynski Distance =  1-1/2 (|A ∩ B| /A + |A ∩ B| /B)
The dissimilarity between two experiments was defined as the weighted average of 
their component-wise distances.
 Experiments Clustering
Based on dissimilarity matrix, automatically generate a hierarchical tree.
— Generate clusters of varying sizes n
— Assess clusters using silhouette scores and select the best cluster size
 Gold Standards For Meta-analysis
— A list of experiments about glucose responsive genes and insulin secretion in 
islets
— A list of experiments about genes involved in organismal lifespan alteration
generated based on keyword searches in ArrayExpress
 Term Harmonization
Annotation terms were harmonized using Unified Medical Language System (UMLS)
Experimental Annotation-based Clusters (1) Conclusion and Future Directions
• Meta-analyses based on annotation can help to identify closely related 
experiments. The richly annotated experiments gave better results.
• To further improve the comparison of annotation terms, we will apply 
ontological relationships to refine the dissimilarity measures.
• In the future, we will expand keyword search results based on 
dissimilarity matrix if necessary. 
We are developing software applications to perform meta-analysis of 
microarray experiments based on standardized experiment annotations 
aiming to identify similar experiments and cluster experiments. The 
applications were tested on files obtained from the ArrayExpress public 
repository. Annotation terms covering the biological intent and context of 
experiments were used to compute dissimilarity measures between 
experiments. Our applications will categorize the experiments resulting 
from keyword search based on experimental annotation information. These 
applications may motivate efforts of bench biologists to better annotate 
experiments.
Meta-analysis software applications contain three components. The first 
software component extracts annotations from appropriate fields in MAGE-
TAB files. The second module computes dissimilarity measures between 
pairs of experiments using extracted annotations. Given a set of keywords, 
the third software module will categorize the experiments containing given 
keywords using the dissimilarity matrix generated by the second software 
module. 
Funding: NHGRI grant R21 HG004521
Our goal is to develop software applications that can identify similar experiments to a query experiment and group the 
experiments resulting from keyword search into various categories based on experimental annotations. 
The first software module to retrieve annotations from MAGE-ML or MAGE-TAB files 
has been developed. Experimental annotations were successfully extracted from 5632 
experiments. According to the scores of annotation components, around 50% of the 
experiments were not well annotated (scores ≤ 2), most coming from GEOD. We 
focused on a total of 2435 experiments with all considered components annotated in 
the following meta-analyses.  
Experimental Annotation-based Clusters (2)
Experiments:
E-CBIL-13 Beta cell specific ablation of Foxa2 (HNF-3b) in mice
(glucose homeostasis)
E-CBIL-6 HNF4alpha in beta cell function
(insulin secretion and glucose tolerance)
E-CBIL-37 foxA1 and beta cell function
(glucose homeostasis)
E-CBIL-39 Foxa2 controls vesicle docking and insulin secretion 
in mature beta-cells
E-CBIL-21  Beta Cell Growth in Tcf-1 Deficient Mice
(pancreatic insulin content)
E-CBIL-23  Fat and Normal adipocytes from insulin receptor knockout 
mice sorted into small and large cells (glucose intolerance)
E-CBIL-19  Skeletal Muscle Insulin Receptor Knockout -
Control, Streptozotocin Diabetic and Insulin Treated
E-CBIL-35 GSIS Study of Rat INS1 cell lines 
(glucose responsiveness)
• Most of the experiments in the gold standard list were categorized in one 
group for two different sets of keyword searches.
• We obtained similar results by using either the Jaccard or the Kulczynski
distance to compute dissimilarity measures between pairs of experiments.
*: Experimental IDs in red are the experiments in the list of gold standard (total 8 experiments).
ExperimentDesign
genetic_modification
_design
ExperimentFactor
genetic_modification
BioCharacteristics
GeneticModification
Age, OrganismPart
Taxon
Mus musculus
ProtocolType
labeling, purify
nucleic_acid_extraction
linear_amplification
8 Experiments
Cluster
Database 
(eg. ArrayExpress, RAD)
MAGE-TAB
files Annotations Of Experiments
retrieve compute
Dissimilarity Measures
Between Experiments
Keywords
Cluster experiments containing given keywords
Clustering of 76 Experiments (containing “insulin” or “glucose”)
ExperimentDesign
co-expression_design
ExperimentFactor
genotype
BioCharacteristics
Genotype
Taxon
Mus musculus
S. cerevisiae
ProtocolType
labeling, purify
nucleic_acid_extraction
16 Experiments
ExperimentDesign
compound_treatment
_design 
ExperimentFactor
compound
BioCharacteristics
CellType, CellLine
OrganismPart
Taxon
Mus musculus
Homo sapiens
ProtocolType
labeling, purify
nucleic_acid_extraction
26 Experiments
ExperimentDesign
genetic_modification
_design
ExperimentFactor
genetic_modification
BioCharacteristics
GeneticModification
Age, OrganismPart
Taxon
Mus musculus
ProtocolType
labeling, purify
nucleic_acid_extraction
linear_amplification
8 Experiments 5 Experiments
ExperimentDesign
strain_or_line_design
ExperimentFactor
strain_or_line
BioCharacteristics:
StrainOrLine
Taxon
no common term
ProtocolType
labeling, purify
nucleic_acid_extraction
ExperimentDesign
co-expression_design
growth_condition_design
disease_state_design
ExperimentFactor
growth_condition
disease_state
BioCharacteristics
StrainOrLine,DiseaseState
Age, OrganismPart
Taxon
Mus musculus
Homo sapiens
ProtocolType
labeling, purify
nucleic_acid_extraction
21 Experiments
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Christian Stoeckert,

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Annotation-based meta-analysis of microarray experiments

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