Ontology-based meta-analysis of global collections of high-throughput public data.

Abstract

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.
We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.
Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

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Ontology-Based Meta-Analysis of Global Collections of
High-Throughput Public Data
Ilya Kupershmidt1,2*, Qiaojuan Jane Su1, Anoop Grewal1, Suman Sundaresh1, Inbal Halperin1, James
Flynn1, Mamatha Shekar1, Helen Wang1, Jenny Park1, Wenwu Cui1, Gregory D. Wall1, Robert Wisotzkey1,
Satnam Alag1, Saeid Akhtari1, Mostafa Ronaghi1,3
1NextBio, Cupertino, California, United States of America, 2Royal Institute of Technology (KTH), Stockholm, Sweden, 3Illumina, San Diego, California, United States of
America
Abstract
Background: The investigation of the interconnections between the molecular and genetic events that govern biological
systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray
and next-generation sequencing technologies have the potential to provide this information. However, taking full
advantage of these approaches requires that biological connections be made across large quantities of highly
heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is
fast becoming one of the key challenges in the research community today.
Methodology/Results: We have developed a novel data mining framework that enables researchers to use this growing
collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular
states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a
combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality
concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using
multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining
approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression
datasets.
Conclusions: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale
genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven
analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing
hypothesis.
Citation: Kupershmidt I, Su QJ, Grewal A, Sundaresh S, Halperin I, et al. (2010) Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public
Data. PLoS ONE 5(9): e13066. doi:10.1371/journal.pone.0013066
Editor: Ramy K. Aziz, Cairo University, Egypt
Received November 15, 2009; Accepted July 28, 2010; Published September 29, 2010
Copyright: ? 2010 Kupershmidt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: A major part of this work was funded by NextBio, which employs all authors (with the exception of Mostafa Ronaghi, employed by Illumina). This work
was also supported in part by NIH grant R43 GM078602. The funders provided resources necessary for the development of the presented methods and
technology and played a role in study design, data collection and analysis, decision to publish, and preparation of the manuscript.
Competing Interests: All of the authors are employed by a commercial company, NextBio (with the exception of the last author, Mostafa Ronaghi, who is
employed by Illumina). There are also a number of patents filed with respect to the technology and algorithms described in the article. NextBio also provides a
commercial software platform in both free and paid versions. These competing interests do not alter the authors’ adherence to all the PLoS ONE policies on
sharing data and materials.
* E-mail: ilya@nextbio.com
Introduction
High-throughput technologies have become essential tools for
biological researchers. The advent of ‘‘open biology’’ has led to
an exponential growth of high-throughput data in publicly
shared repositories, such as NCBI GEO, EBI Array Express,
and the Stanford Microarray Database (SMD) [1]. The billions
of data points collected within these repositories provide an
unprecedentedopportunityfor
molecular portraits of different biological states. However, the
complex and heterogeneous nature of this exponentially
growing amount of data has created a new and daunting
challenge for a community wishing to explore it in a systematic
and easy way.
exploringandcomparing
A number of meta-analysis studies across multiple sets of gene
expression data have led to important discoveries, such as: i) the
identification of consistently and significantly deregulated genes
in prostate cancer [2], ii) the derivation of candidate biological
pathways that underlie mechanisms of carcinogenesis [3], and
iii) the identification of lung adenocarcinoma genetic markers
that correlated with patient survival [4], among others [5–10].
These studies typically focused on a single phenotype and
identified significant differentially expressed sets of genes across
multiple datasets. Conversely, an investigator-generated gene
signature can be applied across large collections of high-
throughput data to look for associations with various diseases,
tissues, and treatments. In the landmark study by Lamb et al., the
connections between disease-associated gene expression ‘‘foot-
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prints’’ and gene expression profiles of different cell lines treated
with diverse compounds were explored through meta-analysis of
data generated on a highly standardized, single microarray
platform [11]. The authors created a map that linked disease to
relevant compounds by computing enrichment-based ‘‘connec-
tivity’’ scores between their corresponding gene expression
signatures. Public repositories, however, contain thousands of
independent studies with highly heterogeneous data from
different labs, platforms, and organisms. The high level of
complexity makes it difficult to use by the broader scientific
community.
Here we report the development of a novel strategy to
explore the biological properties of gene sets found in global
collections of public or proprietary large-scale experimental
data. The size of gene sets queried can range from the tens (e.g.,
the results of qPCR experiments) to hundreds or even
thousands (e.g., the gene signature results from microarray or
next generation sequencing experiments). Using a unique
combination of rank-based enrichment algorithms, ontologies,
and meta-analysis techniques, we compute correlation scores
between a given gene set and thousands of public studies. The
output provides a ranked set of signatures and ‘‘meta-concepts’’
representing diseases, normal tissues, compound treatments,
and genetic perturbations (gene mutations, knockouts, siRNA
knockdowns) that have strong association with a gene set of
interest. We applied our strategy to develop NextBio (www.
nextbio.com) – a data mining framework that integrates and
correlates global public datasets with the user’s own experi-
mental data. As a demonstration, we used NextBio to detect
and explore the biology of brown fat, to compare its expression
profile to those of other tissues and cell types, and to discover
connectivities with different disease states and chemical and
genetic perturbations.
Results
Data pre-processing and correlation overview
Our data mining strategy can be divided into two parts. In the
first part (Figure 1), semi-automated crawlers collected public data
from diverse sources, such as NCBI GEO [12], Array Express
[13], SMD [14], Broad Cancer Genomics [15], Cancer Biomed-
ical Informatics Grid (caBIG), and other repositories (Table S1). A
data analysis step produced sets of differentially expressed gene
signatures associated with each experimental or clinical compar-
ison, such as disease versus normal (Methods). In the final step of
part one, all signatures were tagged with relevant ontology terms
(Figure 1) that reflected associated tissue types, disease/phenotype,
compound treatment, or genetic perturbation (e.g., gene mutation,
knockout, siRNA knockdown). In the second part, rank-based
enrichment statistics were applied to compute pairwise correlation
scores between all signatures (Figures 2 and 3) followed by a meta-
analysis to compute individual signature-ontology concept corre-
lation scores (Figure 4).
To ensure that data were comparable across different
platforms and species, gene signature identifiers were translated
using both a universal gene dictionary to map them to a standard
NCBI gene reference and a cross-organism dictionary to assign
themtoprecomputedortholog
annotations and probe definitions were regularly revised to
ensure that they are up-to-date with manufacturer specifications.
While issues have been reported with microarray spot definitions
[16–18], we believe that our meta-analysis approach mitigated
the effects of errors that occur on a single platform since the
strength of an association was weighted by its consistency across
multiple studies and platforms.
We collected and analyzed over 6,000 individual experiments
from different public sources of large-scale experimental data.
clusters (Methods).Gene
Figure 1. Public data processing and analysis pipeline diagram. The steps for turning public datasets into processed gene signatures include:
raw data collection, sample annotation curation, data quality control, automated analysis, and manual tagging of resulting signatures with disease,
tissue, compound ontology, and gene perturbation terms (tags). Curation of sample annotation includes a systematic analysis of all sample attributes
that should be processed for differential expression. The data processing step converts original raw data into processed results – gene expression
signatures representative of a given biological condition. The final tagging step ensures that key biological conditions associated with each signature
are captured with standardized vocabulary terms, enabling downstream meta-analysis.
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Within this collection were more than 140,000 individual samples
profiled on gene expression microarrays. Out of 6,000 experi-
ments, only 4,000 passed our extensive quality control (QC)
criteria. Approximately 60% of the disqualified studies were
excluded from processing due to insufficient replicates, lack of
control samples, or unsupported platforms (e.g., platforms that do
not cover more than half the number of genes for an organism).
Approximately 25% of the disqualified studies were duplicated
(e.g. part of an already processed super-series), and 15% were
excluded for failing QC metrics during pre-processing and
differential expression analysis (Table 1, Methods).
After applying a statistical analysis to identify differentially
expressed genes in each experiment, we obtained a total of 25,000
gene signatures (a typical study produces multiple results). Each
signature was tagged with relevant ontology terms. This
annotation step identified a total of 120 unique normal tissue
concepts, 700 disease, 1,430 compound, and 135 genetic
perturbation concepts (including gene mutations, knockouts, and
Figure 2. Computing pairwise signature correlation scores. The algorithm represented by this schematic computes an enrichment score and
p-value between two ranked gene signatures. Dark red and blue colored boxes indicate genes present in both signatures; light red and blue colored
boxes represent genes present in only one of the signatures. Dark lines connecting genes in each signature represent connections between genes
with the same direction of regulation in both signatures. Light lines connect genes with opposite direction in two signatures.
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siRNA knockdowns). The final dataset contained a high-
dimensional space of gene signatures with ranked genes and
associated ontology concepts (tags) for diseases, tissues, com-
pounds, and genetic perturbations (Table 1).
Computing pairwise signature correlation (enrichment)
scores
A number of factors must be considered when performing a
comparative analysis of highly heterogeneous data from different
sources, platforms, and technologies. We applied statistical
methods to compensate for differences in platforms and their
probe content, in organisms studied, and in signature sizes that
could arise from choices of analysis stringency (e.g. p-value cutoffs).
In addition, directional information (up- or down-regulation) is
important for assessing connectivity between different gene sets
derived from gene expression data. Our rank-based directional
enrichment analysis enabled us to statistically assess pairwise
correlations between any two gene signatures and to use this
information to rank connectivity between different biological
states.
We applied our algorithm to compute pairwise correlation
scores between all signatures in our system (Figure 2). The
magnitude of the pairwise correlation score reflected the similarity
of the two signatures, which is measured by the extent that the
genes in one signature set are enriched at the top ranks of the other
signature set, and vice versa. Each signature consisted of a list of
Figure 3. Computing directionality and final correlation scores between two signatures. The directional subsets are formed for both b1
and b2, and subset-subset enrichment scores are Computed for b1+b2+, b1+b22, b12b2+, and b12b22. Pairwise correlation scores for the directional
subsets are positive where subsets are of the same direction and negative sign otherwise. The correlation scores of the subsets are summed up to
give the final score for full set b1 versus full set b2.
doi:10.1371/journal.pone.0013066.g003
Figure 4. Gene signature query against all other signatures within the system. First, pairwise gene signature correlation scores (using rank-
based enrichment statistics) are computed, followed by meta-analysis of individual score-tag pairs to compute overall tag scores. This two step
process results in computation of direct correlations between user’s defined signature and diverse biological conditions representing normal tissues
and cell types, diseases, and compounds. Furthermore, overall positive or negative correlation between a signature and a concept is computed based
on individual pairwise signature correlation scores. A positive correlation implies a similar up- and down-regulation of genes in each signature or
signature-tag pair, while a negative correlation implies the opposite trend.
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genes that passed a select fold change, p-value, or other test
statistic threshold. Thresholds might vary between different
researchers, types of studies, and analytical methods, and thereby
result in different signature sizes. To capture the key enrichment
signal, even among very short or very long signatures, we
developed a non-parametric rank-based statistical approach.
The general design of the algorithm, which we call ‘‘Running
Fisher’’ (see Methods), is analogous to the Gene Set Enrichment
Analysis (GSEA) method [11,19]. As with GSEA, Running Fisher
dynamically detects the most significant enrichment signal in a
ranked signature, allowing the signature to contain a relatively
more comprehensive collection of genes than would otherwise be
required when using a stringent statistical cutoff. This ‘‘dynamic’’
enrichment detection approach overcomes the limitations of a
more commonly used ‘‘selection’’ approach where a too stringent
cutoff might lead to potential loss of significant information, and a
too relaxed cutoff might include insignificant data into the
evaluation [20]. The Running Fisher algorithm differs from
GSEA in the assessment of the statistical significance, where p-
values are computed by a Fisher’s exact test rather than by
permutations (see Methods for details). Overall, this approach
provided us the flexibility to compute correlation scores for data of
different sizes and filter thresholds, as well as the ability to use
ranks in both query and target signatures.
The directional relationship between the two signatures was
captured by the sign of the correlation score. The up-regulated
genes and the down-regulated genes were separated into
directional subsets, and correlation scores were computed for
each directional subset from one signature against each subset
from the other signature (Figure 3). A positive sign was given to a
subset pair that changed expression in the same direction, and a
negative sign was given to a subset pair that changed in opposite
directions. The overall correlation score was the sum of directional
subset scores, and the sign of the sum determined whether the two
signatures were positively or negatively correlated (see Methods for
details). Using this strategy, we computed pairwise correlation
scores between all 25,000 signatures, resulting in over 625 million
pairwise scores.
Meta-analysis to compute signature-ontology
correlations
Currently, our system contains tens of thousands of datasets
representing diverse types of biological conditions. With the
development of new sequencing technologies we anticipate
hundreds of thousands of public datasets to be available for the
research community in the near future. To systematically
interrogate these huge quantities of data, we have to abstract
our analysis to the level of biological conditions those datasets
represent. Researchers can then look at the connections between
their own data and the potentially thousands of individual datasets
with matching tissues, diseases, compounds, or genetic perturba-
tions. Ontology-based meta-analysis is designed to accomplish that
goal by computing an overall correlation score between a given
gene set and an ontology concept (e.g. disease). The meta-analysis
algorithm statistically assesses ‘‘reproducibility’’ of significant
findings, thus minimizing the chance of random correlations and
poor-quality data affecting the final results.
The meta-analysis algorithm aggregated scores for various
ontology terms associated with the correlated signatures, weighted
by the strength of the correlation score (Figure 4, Methods). This
was computed separately for tissue, disease, compound, and
genetic perturbation categories. The algorithm considered any
available hierarchical relationships of ontology terms and
propagated enrichment scores to more general concepts accord-
ingly. Concepts that may have had lower scores than their parent
concepts were clustered under the parent. A ranked structure of
the most relevant tissues, diseases, and compounds was thus pre-
computed for each signature. The advantage of this strategy is that
related ontology tags could be associated with each signature
semantically. For example, ‘‘heart’’ and ‘‘left heart ventricle’’ can
both contribute to the ‘‘heart’’ concept meta-analysis score since
‘‘heart’’ is the parent concept of ‘‘heart ventricle’’.
As each new signature was added, the meta-analysis computa-
tions for existing signatures in the system were also updated. This
ongoing process ensured that at a given time the most up-to-date
results of the meta-analysis given the current state of the
knowledge base were produced. When a query with a given set
of genes was performed a total collection of meta-categories, as
well as individual signatures were scanned to identify top-ranking
normal tissues, diseases, and compounds (Table 1).
Use Case 1: Comparative Analysis across Normal Tissue
and Cell Type Data
Analysis of the molecular similarity between brown fat
and other normal tissues.
A large number of experiments in
the public domain provide a great resource for exploring normal
tissue biology. It is virtually impossible to create a single
comprehensive dataset with gene expression profiles of all tissues
and cell types of interest. However, normal tissue datasets from
hundreds of independent studies can be scanned using gene sets of
interest to identify similarities. This can further our understanding
of the relationships between different tissues, different stem cell
lineages, as well as mechanisms governing normal and aberrant
developmental pathways.
Table 1. Summary of all data associated with normal tissues, diseases, drug treatments, and genetic perturbations.
Concept Type Total StudiesTotal SignaturesTotal Samples Total Concepts
Normal Tissues 4502,120 14,500120
Diseases1,3905,880 54,600700
Compounds 990 10,830 45,4201,430
Genetic Perturbations1,2356,17016,400 135
Total 4,06525,000 130,9202,375
Total concepts count represents the number of specific ontology terms that are assigned as tags to signatures, or which represent ‘‘parent’’ ontology concepts. For
example, when a gene signature is tagged with ‘‘heart ventricle’’, it is automatically considered tagged with the parent term ‘‘heart’’ and both are considered in the
counts shown. ‘‘Total studies’’ refers to the number of studies that contain gene signatures that contribute to a given concept based on their associated tags. ‘‘Total
Signatures’’ and ‘‘Total Samples’’ refer to the number of gene signatures and individual samples contributing to a given concept type (e.g. disease).
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We applied our strategy to investigate molecular properties of
brown fat cells and to explore their similarity to a collection of
other normal tissues. To achieve this we derived a brown fat tissue
gene expression signature from the mouse tissue atlas dataset
containing genome-wide gene expression profiles of 61unique
tissues and organs (NCBI GEO Accession # GSE1133) [21]. Each
gene was ranked according to its fold change relative to the
median of all mouse tissues (see Methods). As a result we obtained
a tissue signature that consisted of 31,309 probe sets and their
associated ranks.
We then used our rank-based enrichment analysis to compute
pairwise correlation scores between brown fat and signatures
across all studies contained in NextBio (www.nextbio.com,
Table 1). A meta-analysis of the pairwise signature correlations
then computed the correlation of the brown fat signature with the
tags of all target signatures (Figure 5A). The majority of ontology
concepts were associated with multiple signatures derived from
different studies and organisms. As shown in Table 2, skeletal
muscle tissue produced the strongest positive correlation with the
brown fat tissue signature. A positive correlation indicates that
predominantly the same sets of genes are either up- or down-
regulated in the query and target set of signatures (in this case a
total of 4 signatures). Skeletal muscle had the top ranked
correlation score to brown fat out of 120 total tissue concepts
computed from over 2,000 signatures (Table 1). Skeletal muscle
data used in the meta-analysis was generated from mouse, rat, and
human tissues, providing evidence that the results are consistent
across species and platforms. Of interest, these data indicate that
brown fat cells are more closely related to muscle than to white
adipose tissue, which failed to produce significant correlation with
muscle tissue concepts for the same query (Table S2). In a recently
published study, Seale et al. demonstrated that brown fat cell
precursors can turn into muscle cells upon the loss of PRDM16
protein [22]. Other studies have also shown that brown fat and
muscle tissue share important molecular characteristics, thus
validating our approach [23,24].
Analysis of a brown preadipocyte signature
Seale et al. identified new progenitor cells that gave rise to brown
fat and muscle cells but not to white fat cells [22]. Currently,
however, there is no gene expression data available for these new
precursor cells. Given that, we decided to investigate the molecular
properties of another brown fat cell precursor and its molecular
similarity with normal tissues and cell types. We derived a brown
preadipocytes signature consisting of 2,302 probesets (mouse
MG_U74Av2 Affymetrix chip) by comparing gene expression of
Figure 5. Brown fat meta-analysis. Diagram representing analyses of two different brown fat related signatures: (a) Brown fat tissue signature
(relative to all other mouse tissues). (b) Signature of brown preadipocytes vs. white preadipocytes. After computing pairwise scores between query
and all target signatures the meta-analysis of pairwise scores and their associated tags (associated disease, tissue, and compound terms) is
performed. The final result produces a ranked set of tissues, diseases, and compounds with the most significant association to query signature.
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brown to white preadipocytes (GEO Accession # GSE7032,
Table S4) [23]. Using this signature, we performed a correlation
analysis across all normal tissues and cell types in NextBio
(Figure 5B), and found that muscle stem cells were among the top
five concepts with the strongest positive correlation scores to the
brown preadipocytes signature (Table 3). This result further
contrasts the differences between brown and white fat cells and
demonstrates the association of muscle to brown fat. We continued
our analysis by looking for patterns as brown and white adipocytes
undergo differentiation, and we continued to observe a pattern of
positive correlation between brown adipocyte and muscle cell
precursors. In accordance with results in Seale et al., our meta-
analysis approach suggests the possibility of the existence of a
common precursor cell for these two cell types [22].
Use Case 2: Comparative Analysis across Disease Related
Data
Analysis of brown fat tissue and correlation with disease
signatures.
Using public data, we generated thousands of
signatures representing 700 distinct disease states. This large
collection of disease profiles provides a rich contextual framework
with which to explore gene sets of interest. Analysis of tissue- and
cell type-specific gene sets against these disease-state profiles can
unveil abnormal cell- and tissue-specific programs that are
involved in disease development. Analysis of gene sets derived
from specific patient cohorts against specific disease signatures can
classify a disease more precisely and help drive patient
stratification and trial selection [25,26].
As an example, we investigated the relationship of brown fat
tissue to all disease tissue signatures. For the purposes of this study
we focused on disease concepts that had a negative correlation to
brown fat signature. Among the top ranking disease states, we
found obesity, quadriplegia, aging, Duchenne muscular dystrophy,
and myocardial infarction (Table 4). The negative correlation to
obesity provided a positive control, as brown fat functions in
energy expenditure and is associated with resistance to obesity in
diverse mouse strains [27]. Furthermore, the proportion of white
to brown fat is significantly increased in obese versus normal
subjects [28].
The strong negative correlation of the brown fat signature
with aging (Table 4) may be partly explained by the potential
age-related suppression of pathways leading to brown fat cell
production. This is supported by the fact that that brown tissue
deposits are more abundant in fetuses and newborns, but are
less prominent in adults [29]. The strong negative correlation
with quadriplegia, Duchenne muscular dystrophy, and myo-
cardial infarction is consistent with our earlier findings of
molecular similarities between brown fat and muscle tissues.
Also, normal tissue-specific gene expression is suppressed in the
atrophied muscles associated with these disease phenotypes
[30].
Use Case 3: Comparative Analysis across Chemical
Perturbations Data
Brown preadipocytes differentiation signature positively
correlates with reversine.
A comparative analysis of gene
signatures derived from a large collection of compound treatment
experiments can identify those compounds with similar biological
properties, pinpoint treatments with toxic side-effects, and discover
novel indications for existing compounds [11]. Furthermore, by
exploring a large collection of compound signatures, investigators
can identify chemical perturbations that can activate or deactivate
Table 2. Brown fat tissue signature query results.
RankNormal tissue
Correlation
DirectionCorrelation Score
## Correlated/
Total Studies
## Total Correlated
Signatures
1 Skeletal muscle tissue
+
+
+
+
+
1004/44/4
2 Tongue93.83/3 5/5
3 Epidermis93.31/13/3
4Cardiac atrium 88.92/2 2/2
5Duodenum85.9 2/22/2
The gene expression signature from brown fat tissue was queried against all studies corresponding to normal normal tissues from different microarray platforms and
organisms. The results for top five tissues with the biggest positive correlation to brown fat signature are shown. Additional results are shown in Tabs S2 of the
Supporting Information section.
doi:10.1371/journal.pone.0013066.t002
Table 3. Brown versus white preadipocytes signature correlation with normal tissues and cell types.
Normal tissueCorrelation DirectionCorrelation Score
## Correlated/
Total Studies
## Total Correlated
Signatures
Embryonic tissue
+
+
+
+
+
89.07/7 17/17
Muscle stem cell88.7 1/13/3
Hair follicle matrix 87.12/2 3/3
T-helper type 1 cells 82.21/11/1
Embryonic Stem cells82.115/1826/32
Query results for gene expression signature differentiating brown and white preadipocytes across normal tissue signatures. Positive correlation scores for the top four
tissues whose expression signatures correlate with brown vs. white preadipocytes signature (for additional results see Table S5, Supporting Information section).
doi:10.1371/journal.pone.0013066.t003
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cell type-specific differentiation programs and use them as
additional tools in future experiments.
To explore compounds that may affect differentiation of brown
preadipocytes, we first derived a differentiation signature of 2,000
probe sets by comparing mature brown adipocytes to brown
preadipocytes (Table S6) [23]. We then queried this signature against
all compound-related data. The strongest positive correlation
discovered was with the signature of the small molecule reversine
(Table 5). Interestingly, Kim et al. demonstrated that reversine
stimulates adipocyte differentiation in 3T3-L1 cells[31]. There is also
a strong positive correlation between the signatures of mature brown
fat and reversine (Table S7) [32]. This suggests that reversine may be
a useful compound in future studies of brown fat and may act to
stimulate brown preadipocytes differentiation into mature cells.
Use Case 4: Comparative Analysis across Genetic
Perturbations Data
Analysis ofbrown
signatures.
Genetic perturbation experiments represent animal
orcell linemodelsinwhich a genewasdeleted,modified,orsilenced
using transcript-specific siRNAs. Identifying genes whose per-
turbation causes similar gene expression changes as found in the
target condition might help reveal common, key mechanisms
versuswhitepreadipocytes
involved intheregulationofprocessesleading tonormaland disease
development.
To identify genetic perturbations that resulted in altered gene
expression patterns similar to the brown preadipocyte signature,
we again used the 2,302 probe set derived by comparing the gene
expression profiles of brown and white preadipocytes (Table S4)
[23]. A query against all genetic perturbation experiments in
NextBio (1,235 datasets containing 6,170 gene signatures for a
total of 135 perturbed gene products) revealed that perturbations
of SNF5 correlated most positively and those of MYC correlated
most negatively (Table 6). Positive correlation between brown vs.
white preadipocytes signature and SNF5 perturbation implies that
ablation of SNF5 function induces gene expression changes that
are positively correlated with white preadipocytes. Current
literature supports the notion that the SNF5 gene positively
regulates adipocyte differentiation during adipogenesis [33]. Our
results suggest that SNF5 expression may direct cell fate towards
white preadipocyte differentiation. The negative correlation with
MYC gene perturbations indicates that MYC regulated pathways
may positively regulate brown adipocyte differentiation as
compared to white adipocytes. A number of reports suggest that
overexpression of MYC suppresses adipogenesis and that its
deletion can stimulate accumulation of white fat in pancreas
[34,35]. Overall, we find that our system allows a deeper look at
Table 4. Correlation between brown fat and muscle tissue signatures with diseases.
A. Query: Brown FatRankDisease
Correlation
Direction
Correlation
Score
## Correlated/
Total Studies
## Total Correlated
Signatures
Brown fat1 Obesity-1008/1427/46
Brown fat2Quadriplegia- 78.21/1 1/1
Brown fat3 Aging-73.927/3036/48
Brown fat4 Duchenne Muscular Dystrophy
(DMD)
- 86.97/8 14/16
Brown fat5 Myocardial infarction- 84.15/525/26
B. Query: Skeletal
Muscle
Skeletal muscle1 Quadriplegia- 100 1/11/1
Skeletal muscle2Duchenne Muscular Dystrophy
(DMD)
- 99.97/814/16
Skeletal muscle3Oral cancer-89.5 2/2 3/3
Skeletal muscle4Nerve injury- 78.14/4 10/14
Skeletal muscle5Myocardial infarction-77.26/6 24/27
Brown fat and muscle normal tissue signatures queried against all disease-related signatures in different studies and organisms. Top diseases with negatively correlated
genes to brown fat and muscle are shown (for additional results see Table S3, Supporting Information section).
doi:10.1371/journal.pone.0013066.t004
Table 5. Brown mature adipocytes signature correlation with compounds.
Compound
Correlation
Direction
Correlation
Score
## Correlated/
Total Studies
## Total Correlated
Signatures
Reversine
+
+
+
+
1001/11/1
Dasatinib 79.81/19/9
Matrigel79.67/8 24/26
Gentamicin79.53/5 12/16
Top five query results for gene expression signature comparing mature brown adipocytes to differentiating brown preadipocytes across all signatures tagged with
‘‘Compounds’’ category (for additional results see Table S6, Supporting Information section).
doi:10.1371/journal.pone.0013066.t005
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the regulatory networks involved in regulating brown and white
preadipocyte differentiation into mature cell types.
Discussion
In this study we presented a novel strategy for mining global
collections of large-scale biological data using a combination of
ranked-based enrichment statistics and ontology-based meta-
analysis. We have implemented our approach within the NextBio
platform (www.nextbio.com) and demonstrated how researchers
can use their own gene sets of interest to perform queries in the
context of the globally available public data. In a series of case
studies, we showed how this strategy can be used to scan thousands
of microarray experiments in the public domain against brown fat-
related gene sets to glean insight into adipose tissue biology. These
adipose-related gene sets were analyzed within the context of gene
expression data from normal and disease tissue comparisons,
chemical compound studies, and genetic perturbation experi-
ments. Insights drawn from these case studies were consistent with
previously published results and also provided a novel foundation
for the formation of new hypotheses.
Two key factors driving the significance of our data-driven in
silico analysis are the sheer volume of data that we independently
correlated and ranked with brown fat-related gene sets (over 4,000
experiments comprising 25,000 signatures) and the replication of
observed correlations across multiple independent datasets. Using
brown fat-derived gene sets, we demonstrated the strategy of
exploring tissue development, cell-type specific expression, and
disease etiology. Furthermore, we demonstrated the discovery of
compounds and genetic perturbations that could potentially
influence gene expression programs involved in adipocyte
differentiation. The identification of compounds and genetic
perturbations also furthers our understanding of cell type-specific
expression and helps in designing new experiments to study white
and brown fat biology.
Our strategy also provides a method that addresses, at multiple
levels, the data quality concerns that are often raised with respect
to publicly available data. First, the data goes through rounds of
preprocessing, quality control, and curation. Second, all analysis
results are rank-ordered according to enrichment statistics. Finally,
the meta-analysis framework ensures that the majority of findings
are supported by multiple, independent datasets, which signifi-
cantly increases the overall confidence of our results.
The brown fat case study represents a hypothesis-generation
strategy that can be applied to a variety of biological questions. As
the amount of large-scale public data continues to grow, such data-
driven in silico analyses are becoming increasingly important and
provide a complementary methodology to traditional hypothesis-
driven research. Similar strategies can also be applied to study the
function of genes, pathways, and other biological entities of
interest. Additionally, within clinical research settings, it can be
used to study common and distinct genomic signatures of different
patient cohorts or to identify novel drug indications, among other
applications.
The ontology-based meta-analysis strategy presented here
enables a higher order view of biological connections within the
combined corpus of public and user-generated data. As thousands
of new datasets become available, such meta-level analyses can
provide a practical way to mine vast quantities of diverse large-
scale datasets. Microarray-based gene expression data is the
obvious starting point, given the large amount of public data that
has become available in the last several years. The next logical step
for our strategy would be to extend the current framework to
incorporate orthogonal data types generated by proteomics, SNP
genotyping, and next-generation sequencing platforms. The
combination of orthogonal data will ultimately provide a broader
view of biological systems and enable comprehensive in silico
investigations to take place.
Methods
Raw data pre-processing
A majority of studies currently processed within NextBio adhere
to certain criteria for inclusion:
N Comprehensive coverage of genes - The platform used should
contain over 12,000 probes for human, mouse, or rat studies.
For all other organisms, the array should contain at least half
the number of probes as there are estimated genes in the
genome.
N Presence of a baseline or control group.
N Access to raw or normalized expression values
N Sample annotations provided
To ensure standardization in the processing pipeline, studies
were processed from raw data whenever available and from pre-
processed data otherwise. For example, for Affymetrix-based
studies in which CEL files are available, RMA normalization was
applied [36]. Otherwise, expression summary intensities, such as
those processed generated by MAS5 (Affymetrix) or dChip were
processed [37]. All datasets went through appropriate processing
steps that depended on the data type, platform, and experimental
design used to generate the data and include:
N Background subtraction, if applicable
N Expression summarization, e.g. using RMA when CEL data is
available
Table 6. Brown versus white preadipocytes signature correlation with genetic perturbations.
Perturbed Gene
Correlation
DirectionCorrelation Score
## Correlated/
Total Studies
## Total Correlated
Signatures
SMARCB1 (SNF5)
+
+
100 1/1 1/1
Tcrb96 1/110/11
MYC
2
95 21/2344/65
MYOD1
2
955/520/28
RHO
2
953/3 8/9
Top five query results for gene expression signature comparing brown to white preadipocytes across all signatures tagged with ‘‘Genetic Perturbation’’ category (for
additional results see Table S8, Supporting Information section).
doi:10.1371/journal.pone.0013066.t006
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N Data transformation (log) and technical replicate averaging
and negative value correction
N Normalization – RMA, per-chip median or Lowess where
applicable
N Quality control assessment
N Statistical (differential expression) analysis
The vast majority of processed data in the system falls into the
category of case-control experimental design analyzed using
Welch or standard t-tests, paired or unpaired, as appropriate.
Quality assessment methods were employed to review sample-level
and dataset-level integrity – these included curator review of pre-
and post-normalization boxplots, missing value counts, and p-
value histograms (after statistical testing) with FDR analysis to
determine whether the number of significantly changing genes is
greater than expected by chance.
A p-value significance cutoff of 0.05 (without any multiple
testing correction) and a minimum absolute fold-change cutoff of
1.2 (typically the lowest sensitivity threshold of commercial
microarray platforms) was used to obtain the final set of signatures
of differentially-expressed genes. This double filtering procedure
serves to address different aspects of variability in the data [38–
40]. To address the potential unreliability of the fold-change
metric at low intensity levels [41], genes with signals lower than a
20thpercentile cutoff in both control and test groups were
discarded from the signature.
Expression profiles can vary considerably from study to study
and from platform to platform. Different platform technologies
can yield different dynamic ranges, distributions of fold-changes,
and p-values that reflect the technologies used. To allow inter-
study comparability, a non-parametric approach was established
so that ranks were assigned to each final gene signature based on
the magnitude of fold change. Fold-change, as a ranking metric,
had a better concordance across platforms than p-values from
statistical tests [42]. Ranks were then further normalized to
eliminate any bias due to varying platform sizes.
In the absence of a ‘‘gold standard’’ for processing microarray
data, these statistical threshold cutoffs serve to maintain a
reasonable and consistent level of data quality across all studies
analyzed within NextBio and are commonly adopted in the
literature [43–45]. The thresholds are intentionally permissive to
ensure that signatures contain all potentially interesting elements.
The potential for introducing noise, i.e., more false positives, is
balanced by (a) enforcing the basic quality control metrics
described above and (b) incorporating a normalized rank-based
scheme that captures the relative importance of each gene in a
signature. This key metric of the meta-analysis framework is
described below. In summary, this strategy results in the following
advantages:
(1) The normalized ranking approach enables comparability
across data from different studies, platforms, and analysis
methods by removing dependence on absolute values of fold-
change, minimizing some of the effects of normalization
methods used, and accounting for platform effects.
(2) During pair-wise comparison of signatures, the Running Fisher
algorithm (described below) dynamically determines the best
cutoffs corresponding to the maximal similarity score by
scanning all of the potentially interesting data. Most of the
time, low ranking genes do not contribute to the maximal
score, thus reducing the dependence on the minor variations
of the actual cutoffs used in generating biosets.
(3) A meta-analysis identifies genes with consistent signals across
several experiments. This rescues potentially interesting gene
signatures that might otherwise have fallen below the margin
of significance in an analysis based on a single study.
Cross-platform comparisons
An index of microarray platforms was compiled to aid in the
comparison of microarray data. The index provides a standard-
ized mapping of commonly used public- and vendor-specific
vendor gene identifiers to reference identifiers such as NCBI
Entrez Gene, UniGene, Ensembl, RefSeq, or GenBank accession
numbers.
Cross-species comparisons
To enable seamless comparison across different species, orthologs
were identified for each pair of organisms and were grouped into
ortholog clusters. Ortholog information was derived from Mouse
Genome Informatics (MGI) at Jackson Lab (http://www.informat-
ics.jax.org), HomoloGene at NCBI (http://www.ncbi.nlm.hih.gov),
and Ensembl (http://www.ensembl.org). Ortholog clusters were
generated as follows: 1) the manually curated pairwise ortholog data
among human, mouse, and rat from MGI were retrieved and
clustered to form initial ortholog clusters. 2) The homology group
data among human, mouse, rat, fly, and worm were analyzed to
remove those in conflict with MGI data. The filtered homology
group data were then entered into the ortholog clusters. 3) The
wholegenomepairwisesequencesimilaritydata fromEnsemblwere
processed to identify reciprocal best hits as candidate orthologs for
all pairwise organisms among human, mouse, rat, fly, worm, and
yeast. The candidate orthologs were prioritized based on the
percentage sequence identity and examined against the existing
ortholog cluster. Qualified ortholog candidates were then entered
into the ortholog cluster.
Computing pairwise correlation scores between gene
signatures
The directional relationship between the two signatures is
captured by the sign of the correlation score. The up-regulated
genes (b+) and the down-regulated genes (b2) are separated into
directional subsets, and correlation scores are computed for each
directional subset from one signature (b1+, b12) against each
subset from the other signature (b2+, b22). A positive sign is given
to a subset pair of the same direction (b1+b2+, b12b22), and a
negative sign is given to a subset pair of opposite directions
(b1+b22, b12b2+). The overall correlation score is the sum of
directional subset scores and the sign of the sum determines
whether the two signatures are positively or negatively correlated
(Figure 3). The matching genes between two typical gene
signatures are depicted in Figure 2. The directionality and ranks
for each gene is shown.
The detailed steps given two gene signature sets (b1, b2) are as
follows:
First, each gene signature set is rank-ordered according to fold
change, p-value or a particular score. If appropriate metrics are
not provided, then the gene signature set is unranked. The up-
regulated genes and down regulated genes are noted with positive
and negative signs to imply directionality, respectively. A
directional subset is generated for each direction, such as b1+,
b12, b2+, and b22(Figure 3). If no directional data are provided,
then the gene signature set is not directional and only one subset is
formed with the whole signature set, such as b1o, or b2o.
Second, all the subset pairs are identified: b1Di, b2Dj, where Di
and Dj are the available directions (+, 2, or o) in b1 and b2,
respectively. The Running Fisher algorithm is applied to each
subset pair. The top ranking genes in the first subset b1Di are
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collected as a group G, and the second subset b2Dj is scanned top
to bottom in the rank order to identify each rank with a gene
matching a member in the group G. If the subset is unranked, all
the genes in the subset are retrieved at the first scan.
At each matching rank K, the scanned portion of the second
subset b2Dj consists of N genes, and the overlap between group G
and N genes is M. A Fisher’s exact test is performed at rank K, to
evaluate the statistical significance of observing M overlaps
between a set of size G and a set of size N, where the set of size
G comes from platform P1 and the set of size N comes from
platform P2, given the sizes of P1 and P2 as well as the overlap
between P1 and P2.
At the end of the scan, the best p-value is retained, and a
multiple hypothesis testing correction factor is applied. The
multiple testing factor is the expected number of overlaps between
the two subsets of the given sizes, given the two platforms P1 and
P2. The negative log of the multiple testing corrected best p-value
is a score for the subset pair.
Next, the Running Fisher algorithm is performed in the reverse
direction: the top ranking genes in the second subset b2Dj are
collected as a group G, and the first subset b1Di is scanned in the
rank order. The same procedure in this reverse direction produces
another score for the same subset pair. The two scores are
averaged to represent the magnitude of the similarity between the
two subsets. A positive sign is given to the final subset pair-score if
Di and Dj are the same. A negative sign is given if Di and Dj are
opposite. The score is unsigned if any of Di and Dj is not
directional.
Finally, the overall score is computed by summing up all
directional subset pair scores (Figure 3). The sign of the sum
determines whether the two signatures are positively or negatively
correlated. If one of the two signature sets is directional and the
other is not directional, the overall score is represented by the
larger of the two subset pair scores, annotated with the
contributing direction from the directional signature. If both
signatures are not directional, then a single unsigned pair score is
calculated between the two biosets.
Ontology-driven Meta-Analysis
Given a gene signature representing the set of genes of interest
from a given experiment, a meta-analysis of the tens of thousands
of tagged gene signatures in the NextBio system can be used to
determine tissues, diseases, and compounds associated strongly
with the query set. Conceptually, the problem is that of ranking
ontology terms (concepts) based on how strongly enriched
signatures tagged with those concepts are with a set of genes of
interest. For a set of genes, ranked or unranked, the gene set
enrichment analysis described above is used to identify other
strongly associated signatures. Based on the strength of the
association, the aggregated scores for their associated semantic
concepts were computed.
Meta-analysis scores were computed separately for tissue,
disease, and compound categories. Given a query gene signature,
a list of contributing signatures was obtained for each category
based on two criteria – (1) They have enrichment scores with the
query signature above a pre-determined threshold and (2) They
pass an initial screening logic that ensures that they are tagged
with the appropriate combination of concepts to allow them to
contribute to that category.
Based on the list of contributing signatures, along with the
associated concepts and enrichment scores, the equation below
describes the various factors considered in determining a score for
a concept.
ScoreConcept!
NormalizedHitCounta
BackgroundCountb|AverageWeightedRankc
The normalized hit count for a concept is the sum of the ratio of
associated score of each signature tagged with that concept to the
overall best association score. The background count of a concept
is the number of signatures in the NextBio system tagged with that
concept. Inclusion of the background count reduces the bias
toward popular concepts that have more associated gene
signatures than others. Finally, the average weighted rank
represents the average rank of a tagged signature relative to all
other correlated signatures weighted by the associated normalized
score.
Given the dynamic nature of the NextBio system where the
distribution of data from various species, platforms, data types,
and semantic categories changes on a continuous basis, it is not
obvious at the outset what the relative contributions of each of
these factors should be toward determining an optimal overall
scoring function for determining top ranked concepts. These are
determined empirically by optimizing the model described above
using gold standard use cases and tuning parameters (a,b,c in the
equation above) for each of the factors.
This meta-analysis scoring process results in a ranked list of
ontological terms for each tissue, disease, and compound category.
It should be noted that some concepts are part of a hierarchical
ontological framework. When that was the case, enrichment scores
for signatures tagged with specialized concepts are accordingly
propagated to more general parent concepts in the hierarchy.
After scores for all concepts are computed, children concepts with
lower scores than parent concepts are clustered and presented to
the user.
Computing direction of signature-concept correlation
The overall direction of association (positive or negative)
between a query signature and a concept refers to the net
correlation of the query signature with a set of contributing
signatures (see previous section) tagged with that concept. Recall
that these contributing signatures may have positive or negative
pairwise correlation scores with the query signature. A cumulative
positive and negative score is obtained by aggregating the scores
for the positively and negatively associated signatures separately.
To minimize the effect of any spuriously strong signature
association, the cumulative scores are each weighted by the ratio
of the respective number of positively or negatively associated
signatures to the total number of contributing signatures. Finally,
the overall direction is called depending on which weighted
cumulative score is greater, as long as a minimum difference
threshold is met.
Supporting Information
Table S1
data available to the public. Only major databases were included
in this list.
Found at: doi:10.1371/journal.pone.0013066.s001 (0.02 MB
XLS)
The list of public databases containing raw microarray
Table S2
query against all other public datasets on normal tissue analysis.
Found at: doi:10.1371/journal.pone.0013066.s002 (0.02 MB
XLS)
Meta-analysis results for the brown fat tissue signature
Table S3
query against all other public datasets on normal tissue analysis.
Meta-analysis results for the white fat tissue signature
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Found at: doi:10.1371/journal.pone.0013066.s003 (0.38 MB
XLS)
Table S4
signature was identified by comparing cultured brown preadipo-
cytes to white preadipocytes at day 4.
Found at: doi:10.1371/journal.pone.0013066.s004 (0.01 MB
DOC)
Brown preadipocytes gene expression signature. The
Table S5
signature query against all other public datasets on normal tissue
analysis. Brown preadipocyte signature was determined by
comparing gene expression of cultured brown preadipocytes
versus white preadipocytes at 4 days.
Found at: doi:10.1371/journal.pone.0013066.s005 (0.01 MB
XLS)
Meta-analysis results for the brown preadipocyte
Table S6
mature brown adipocytes to differentiating brown preadipocytes
across all signatures tagged with ‘‘Compounds’’ ontology category.
Found at: doi:10.1371/journal.pone.0013066.s006 (0.01 MB
XLS)
Query results for gene expression signature comparing
Table S7
was identified by comparing cultured C2C12 mouse myoblasts
treated with reversine to non-treated control myoblasts.
Reversine gene expression signature. The signature
Found at: doi:10.1371/journal.pone.0013066.s007 (0.62 MB
XLS)
Table S8
signature query against all other public datasets on genetic
perturbations analysis. Brown preadipocyte signature was deter-
mined by comparing gene expression of cultured brown
preadipocytes versus white preadipocytes at 4 days
Found at: doi:10.1371/journal.pone.0013066.s008 (0.01 MB
XLS)
Meta-analysis results for the brown preadipocyte
Acknowledgements
The authors would like to thank members of the NextBio team, as well as
Dr. Kathleen M. Scully and Dr. Michael G. Rosenfeld. We would like to
thank Dr. Saeed Tavazoie and Dr. Joakim Lundeberg for useful comments
on this manuscript.
Author Contributions
Conceived and designed the experiments: IK. Performed the experiments:
MS. Analyzed the data: IK QJS AG JF MS WC GDW MR. Contributed
reagents/materials/analysis tools: IK QJS SS IH HW JP WC GDW RW
SA SA MR. Wrote the paper: IK.
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Public Data Meta-Analysis
PLoS ONE | www.plosone.org 13September 2010 | Volume 5 | Issue 9 | e13066