Abstract
Background
In a complex disease, the expression of many genes can be significantly
altered, leading to the appearance of a differentially expressed
"disease module". Some of these genes directly correspond to the
disease phenotype, (i.e. "driver" genes), while others represent
closely-related first-degree neighbours in gene interaction space. The
remaining genes consist of further removed "passenger" genes, which are
often not directly related to the original cause of the disease. For
prognostic and diagnostic purposes, it is crucial to be able to
separate the group of "driver" genes and their first-degree neighbours,
(i.e. "core module") from the general "disease module".
Results
We have developed COMBINER: COre Module Biomarker Identification with
Network ExploRation. COMBINER is a novel pathway-based approach for
selecting highly reproducible discriminative biomarkers. We applied
COMBINER to three benchmark breast cancer datasets for identifying
prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold
higher reproducibility than other methods, with up to 30-fold greater
enrichment for known cancer-related genes, and 4-fold enrichment for
known breast cancer susceptible genes. More than 50% and 40% of the
resulting biomarkers were cancer and breast cancer specific,
respectively. The identified modules were overlaid onto a map of
intracellular pathways that comprehensively highlighted the hallmarks
of cancer. Furthermore, we constructed a global regulatory network
intertwining several functional clusters and uncovered 13 confident
"driver" genes of breast cancer metastasis.
Conclusions
COMBINER can efficiently and robustly identify disease core module
genes and construct their associated regulatory network. In the same
way, it is potentially applicable in the characterization of any
disease that can be probed with microarrays.
Background
In recent years, gene expression signatures based on DNA microarray
technology have proven useful for predicting the risk of breast cancer.
Agendia's MammaPrint has become the first FDA-cleared breast cancer
prognosis marker chip containing 70 gene signatures [[30]1]. Many other
microarray-based biomarkers, such as 76 gene signatures [[31]2] have
been derived using independent data sources. However, there are only
three overlaps between MammaPrint's 70-gene and Wang's 76-gene
signatures. Furthermore, many of these markers are functionally
unrelated to breast cancer. In order to identify robust, functionally
relevant disease biomarkers, it is crucial to find gene signatures that
are consistent in various data sources.
A complex disease such as breast cancer results in many differentially
expressed genes (DEGs), which together can be used to construct a
"disease module" network [[32]3]. Some of these DEGs directly
correspond to the disease phenotype (i.e. "driver" genes). The
expression changes enacted on the driver genes lead to a cascade of
changes of other genes: initially to their first-degree interaction
neighbors [[33]4], followed by downstream effects to so-called
"passenger" genes. Due to their direct relevance to the biology of the
disease in question, the expression changes of the driver genes and
their first-degree neighbours (i.e. members of the "core module"),
should be more consistent than those of the passenger genes when
compared across independent cohorts. However, it is often difficult to
separate the core module from the passenger genes for a given disease
[[34]5,[35]6]. In this paper, we aim to isolate the core module from
the more general disease module and further identify the driver genes
using network analysis.
The most intuitive way of finding the disease core module is to
identify the Differential Expressed Genes (DEGs) over various cohorts.
Unfortunately, the typically larger number of passenger genes in each
cohort will contribute to the majority of gene overlaps, due to
statistical chance. A more biologically-motivated technique for
identifying the core module is to find overlapping differentially
expressed pathways. However, a pathway may also contain hundreds of
genes with respect to the disease in question, while only a functional
submodule (a small group of genes) is differentially expressed. These
submodules are often overlooked in pathway enrichment analysis.
In light of the aforementioned challenges, we propose to identify
Pathway Activities (PAs) from cohorts of data and use supervised
classification to isolate a consistent core module. Each PA is a vector
aggregating the information of a few genes expressed in a pathway
[[36]7,[37]8]. The use of PAs for biomarker identification has been
shown improve reproducibility and disease-related functional enrichment
of the resulting biomarkers [[38]7]. The main idea behind our method is
to infer the most significant PAs in each data cohort, and validate
these PAs using classification methods in other cohorts. If a PA also
scores highly in all the other cohorts, we consider it to be
consistently differentially expressed in the disease of interest.
Furthermore, we would consider the genes that make up the PA to belong
to the disease core module.
In this work, we develop a novel biomarker identification framework
entitled COre Module Biomarker Identification with Network ExploRation
(COMBINER). COMBINER identifies "core module" (Figure [39]1) that are
consistently differentially expressed as a whole in the data cohorts of
interest. COMBINER uses a Core Module Inference (CMI) component to
infer candidate PAs from pathway database, a Consensus Feature
Elimination (CFE) component to filter out irreproducible PAs, and a
multi-level reproducibility validation framework to find the consistent
PAs, which in turn make up the complete core module. In its final step,
COMBINER uses known pathways and protein networks to identify the
driver genes within this core module.
Figure 1.
[40]Figure 1
[41]Open in a new tab
Schematic overview of COMBINER. COMBINER uses Core Module Inference
(CMI) to infer candidate pathway activities from each pathway in an
inference dataset, Consensus Feature Elimination (CFE) to filter out
irreproducible activities in validation datasets, and a multi-level
reproducibility validation framework to conduct pair-wise validations
to find common reproducible activities which make up the "core module".
To identify the driver genes, we reassemble the resulting core module
markers in both intracellular signalling pathways and a large overall
regulatory network reflecting interactions between pathways.
To illustrate its utility, we apply COMBINER to three benchmark breast
cancer datasets. We evaluate the resulting core module for accuracy,
reproducibility, and enrichment for known cancer-related genes. We then
explore the roles of the COMBINER-identified core module in the
hallmarks of cancer, and we reconstruct a breast cancer-specific
interaction network composed of functionally coherent modules. Finally,
we summarize our analyses by identifying 13 high confidence driver
genes from COMBINER markers.
Results and Discussion
Overview
COMBINER is a multi-level optimization framework for identifying core
module markers (Figure [42]1 and Methods). Briefly, COMBINER infers
candidate submodules from known pathways, identifies the reproducible
"core module" using independent cohorts, and uses intracellular
signaling pathways and protein networks to identify the "driver" genes
from the "core module".
We applied COMBINER to three independent breast cancer datasets to
evaluate its effectiveness: Netherlands [[43]9], USA [[44]2], and
Belgium [[45]10]. We obtained pathway information from the MsigDB v3.0
Canonical Pathways subset [[46]11]. To decrease redundancy, we applied
pathway filtering to remove bulky pathways such as KEGG Pathways of
Cancer. This resulted in a pathway dataset containing 624 pathways with
5,155 genes assayed in all three benchmark datasets.
Core Module Inference improves reproducibility and classification accuracy
A primary challenge of pathway inference is to find pathway subsets
that are reproducible between independent datasets. We compared Core
Module Inference (CMI) with five other inference methods as well as
individual genes (see Methods). When compared to a range of numbers of
inferred Pathway Activities (PAs), CMI showed two-fold increased
reproducibility over the related CORG method and about a 10-fold
improvement over other methods (Figure [47]2).
Figure 2.
[48]Figure 2
[49]Open in a new tab
Reproducible power of pathway inference methods. The reproducibility
power of a pathway inference method in an inference-validation pair
datasets is measured by
[MATH:
Csco
re(N)=1N
∑i=1Ntscore(PIi)⋅tscore(PVi) :MATH]
, where
[MATH:
PIi
:MATH]
is the i^th PA in descending order in the inference dataset,
[MATH:
PVi
:MATH]
is its corresponding PA in the validation dataset, and N is the number
of selected inferred pathways. The overall reproducibility is then
defined as the average Cscore of selected top inferred pathway
activities over all six inference-validation pairs. We compared CMI
with five inference methods, including the CORG, mean, median, first
component score of PCA, as well as no-inferring gene method. Comparing
by different ranges of top inferred activities, the CMI showed
significant better overall reproducibility over other methods.
We then compared the classification accuracy of CMI and the other
inference methods using Linear Discriminant Analysis-Consensus Feature
Elimination (LDA-CFE) classifiers focused on the top 100 inferred PAs
(Methods). As shown in Figure [50]3, COMBINER run using PA vectors
identified by CMI (CMI-COMBINER) exhibits better overall accuracy than
the other methods coupled with COMBINER. Similarly, CMI also shows good
overall accuracy using the SVM classifier (Additional file [51]1,
Figure S1).
Figure 3.
[52]Figure 3
[53]Open in a new tab
Comparison of CMI and other inference methods-based COMBINER using
LDA-CFE classifiers focused on the top 100 inferred pathways. Seven
methods were compared here, including CMI, CORG, Mean, Median, PCA, LLR
and Individual Gene. (a) Classification accuracy for best feature set:
pair-wise comparisons. Starting from all 100 inferred pathway
activities, we recursively removed the activity with the lowest average
weight from 500 LDA classifiers, until the maximum average AUC was
reached. The process was repeated 100 times and the most frequently
occurring marker set was regarded as the ultimate marker. We measured
classification accuracy of each method by computing AUC mean ± standard
error for the final feature set. (b) Classification accuracy overall.
The overall classification accuracy was measured by computing the
average maximum mean AUC of all six inference-validation pairs. On
average, CMI was superior to the other methods, even though its
activity vector consisted of expression values from only a few genes in
each pathway.
Core module markers enrich cancer-related genes
We compared the enrichment of known cancer genes in the biomarkers
discovered by CMI-COMBINER, (93 genes); CORG-COMBINER, (i.e. COMBINER
run using CORG activity vectors), (123 genes); Subnetwork markers (1162
genes) ( [[54]7], [55]http://www.cellcircuits.com); MammaPrint's
70-gene signature (G70) (70 genes) [[56]1]; and Wang's 76-gene
signature (G76) (76 genes) [[57]2]. Seven known cancer gene datasets
were compared (see Materials and methods). Both CMI-COMBINER and
CORG-COMBINER showed much higher enrichment of cancer-related genes in
their biomarker signatures (Table [58]1). Specifically, CMI- and
CORG-COMBINER showed up to 4-fold increased enrichment over subnetwork
markers and up to 30-fold enrichment over other gene signatures. In
particular for known breast cancer genes in Census, they exhibited up
to 4 fold enrichment over others. More than 50% and 40% of the
resulting biomarkers are cancer and breast cancer specific,
respectively. Additionally, CMI-COMBINER showed greater enrichment than
CORG-COMBINER with respect to the Atlas of Cancer Genes, which is the
largest cancer gene collection. Consistent to Chuang et al's results
[[59]7],. we also found insignificant enrichment in CANgene dataset
including 122 mutative genes from 11 breast cancer cell lines. A
possible explanation is that "the cancer cell lines capture a different
disease state than that found in the population of patients surveyed by
microarray profiling." [[60]7] The COMBINER core module markers with
associated pathways are summarized in Additional file [61]2, Table S1
and Additional file [62]3, Table S2. Additional file [63]4, Table S3
lists the overlaps between CMI-/CORG-COMBINER and KEGG pathways of
cancer, along with up-/down-regulation information.
Table 1.
Cancer Gene Enrichment rate of various breast cancer gene signatures
CMI-COMBINER CORG-COMBINER Subnetwork G70 G76
NetPath 54.17%* 50.41%* 26.33%* 10.00% 10.53%
Atlas 60.42%* 46.34% 32.87% 15.71% 18.42%
Census 11.46%* 13.82%* 5.42%* 2.86% 0.00%
CANgene 1.04% 1.63% 0.52% 0.00% 0.00%
G2SBC 43.75%* 46.34%* 19.02% 21.43% 10.53%
COSMIC 16.67% 17.89%* 7.06% 4.29% 1.32%
KEGG 35.42%* 29.27%* 9.90%* 8.57% 1.32%
[64]Open in a new tab
* p-value < 0.05 for hypergeometric tests
Core module markers highlight the hallmarks of cancer
As shown in Figure [65]4, the COMBINER-discovered biomarkers are
overlaid on the hallmarks of cancer [[66]12,[67]13], which integrate
the common intracellular signalling pathways of all subtypes of cancer.
The components of the core module markers from CMI and CORG along with
eighteen common markers are listed in different fonts. The remaining
proteins (most were not differentially expressed) in the pathways are
consolidated into unlabeled nodes. Figure [68]4 shows that the
identified core module genes comprehensively highlight the hallmarks,
demonstrating the high specificity of COMBINER. In particular, 18
common markers, which we regard as the most reliable predictors,
describe well-characterized processes involving growth factors,
survival factors, the cell cycle, and the ExtraCellular Matrix (ECM).
The modules unique to CMI-COMBINER include anti-apoptosis and JAK-STAT
cascades, while pathways describing anti-growth factors and death
factors were unique to CORG-COMBINER. A few well-known mutant proteins,
including cyclin D1 and p53, may play an important role in connecting
other signatures [[69]7], but they showed only limited predictive
ability in the three breast cancer datasets.
Figure 4.
[70]Figure 4
[71]Open in a new tab
COMBINER biomarkers overlap with well-known cancer-related signalling
pathways. The core module markers from CMI and CORG are listed in
normal and italic fonts, respectively, while the common markers are in
bold. Red/green color denotes up-/down-regulation. The remaining
proteins in the circuit are abstracted as unlabeled nodes. The common
core module markers of CMI- and CORG-COMBINER describe growth factors,
survival factors, the cell cycle, and the extracellular matrix. Unique
pathways to CMI-COMBINER include the anti-apoptosis and JAK-STAT
cascade, while anti-growth factor and death factor pathways were
discovered uniquely by CORG-COMBINER.
Core module markers in predicted protein-protein interaction networks
underpin functional modules
Figure [72]5 shows how a regulatory network was constructed using the
interactome of the core module markers. The regulatory network was
divided into a few functional modules, including cell cycle and ECM.
These functional modules were interconnected by 20 "hub" genes (larger
pink/green nodes), 13 of which overlapped with the common marker genes
(Additional file [73]2, Table S1). Our results imply that these 13
"hub" markers are the essential "driver" genes of breast cancer
metastasis (Table [74]2). For example, BRCA1 is among the most
well-characterized genes whose mutation gives rise to breast cancer. In
addition, low E2F1 transcript levels strongly predicted good prognosis
based on quantitative RT-PCR in 317 primary breast cancer patients
[[75]14]. We further enlarged the nodes of three standard breast cancer
indicators TP53, BRCA1, and ERBB2, which connect many of the
surrounding hub genes. Although TP53 and ERBB2 are useful for a
mechanistic understanding of breast cancer, they were not identified as
discriminative gene markers. A regulatory network was also created
representing CORG-COMBINER (Additional file [76]5, Figure S2), but no
additional "hub" markers were found.
Figure 5.
[77]Figure 5
[78]Open in a new tab
Regulatory networks of CMI-COMBINER biomarkers The pink/green nodes
denote up-/down-regulation of gene expression. The orange nodes
indicate contradictory regulation in different datasets. Larger nodes
are highly connected in the network; most are overlaps between CMI- and
CORG-COMBINER. The three well-known oncogenes for breast cancer
metastasis-TP53, BRCA1, and ERBB2-were enlarged further. The core
module markers were reassembled into an overall interaction network.
Known functional modules neatly overlay well-connected clusters. Many
of the highly connected genes are known "driver" genes playing an
important role in breast cancer metastasis.
Table 2.
Confident "driver" genes for breast cancer metastasis
Symbol Entrez Description
MAP2K1 [[79]32] 5604 mitogen-activated protein kinase kinase 1
E2F1 [[80]14] 1869 E2F transcription factor 1
GRB2 [[81]33] 2885 growth factor receptor-bound protein 2
NFKB1 [[82]34] 4790 nuclear factor of kappa light polypeptide gene
enhancer in B-cells 1
RB1 [[83]35] 5925 retinoblastoma 1
BRCA1 [[84]36] 672 breast cancer 1, early onset
FOS [[85]37] 2353 v-fos FBJ murine osteosarcoma viral oncogene homolog
SOS1 [[86]38] 6654 son of sevenless homolog 1 (Drosophila)
PIK3CA [[87]39] 5290 phosphoinositide-3-kinase, catalytic, alpha
polypeptide
JAK1 [[88]40] 3716 Janus kinase 1
SHC1 [[89]41] 6464 SHC (Src homology 2 domain containing) transforming
protein 1
MYC [[90]42] 4609 v-myc myelocytomatosis viral oncogene homolog (avian)
CCNA2 [[91]37] 890 cyclin A2
[92]Open in a new tab
Conclusions
Identifying accurate and reproducible disease biomarkers is an
important challenge for gene expression analysis. To facilitate this
task, we developed COMBINER, a novel pathway-based biomarker
identification method that extracts the essential "core module" of
disease from known biological networks. Compared to existing methods,
COMBINER substantially improves the reproducibility and cancer-specific
enrichment of its resulting biomarkers. We examined the identified
markers in intracellular signalling networks highlighting the hallmarks
of cancer. Reassembling the core module genes into a regulatory
network, we found 13 "driver" genes connecting eight functional
modules. We anticipate such molecular descriptions to prove even more
useful when applied to diseases that are less well-characterized; our
current work focuses on several such applications.
Methods
Gene expression, pathways, cancer gene databases, and interactome
We used three breast cancer datasets from different countries of origin
to evaluate our method: Netherlands [[93]9], USA [[94]2], and Belgium
[[95]10]. Each dataset recorded whether the assayed patients developed
metastasis within 5 years after surgery. The Netherlands, USA, and
Belgium datasets contain expression profiles for 295, 286, and 198
patients, respectively, with 78, 107, and 35 patients experiencing
metastasis. All of the patients in the USA and Belgium datasets had
lymph-node-negative disease, although their estrogen receptor (ER)
types differed. The Netherlands data contained both lymph-node positive
and negative disease patients with differing ER types, 130 of which
received adjuvant systemic therapy including chemotherapy and hormonal
therapy. We performed a two-tailed t-test on the gene expression values
of each dataset to distinguish between metastatic and non-metastatic
patients, considering genes with p-value ≤.05 as differentially
expressed (DE).
The reference cancer genes for enrichment analysis were collected from
datasets including NetPath [[96]15] (all cancers,
[97]http://www.netpath.org/), Atlas of Cancer Genes [[98]16] (all
cancers, [99]http://atlasgeneticsoncology.org/), Census Genes [[100]17]
(all cancers), CANgenes [[101]18] (breast cancer), G2SBC [[102]19]
(breast cancer, [103]http://www.itb.cnr.it/breastcancer/), and KEGG
Pathways of Cancer [[104]20] (all cancers, KEGG hsa05200
[105]http://www.genome.jp/kegg/pathway/hsa/hsa05200.html).
Pathway information was obtained from the MsigDB v3.0 Canonical
Pathways subset [[106]11,[107]21]. This collection contains 880
pathways collected from seven hand-curated pathway databases including
KEGG, Reactome, and Biocarta.
Predicted protein protein interaction information was obtained from
STRING 9 [[108]22].
Core Module Inference
The CMI method adopts the strategy of the CORG method [[109]8] of
finding the genes with the most discriminative power, differing in
three ways: first, the CORG method collects CORGs only from the up- or
downregulated subset of genes in a pathway, and some key genes can thus
be discarded. In contrast, CMI considers both up- and downregulation
together. Second, CMI improves the greedy search for the discriminative
set of genes. Third, CMI considers only differentially expressed genes.
As illustrated in Figure [110]1, given a pathway consisting of genes
{g[1],... g[i], ..., g[n]} ranking by a descending order of their
absolute t-scores, with their normalized expression values
{z(g[1]),..., z(g[n])}, determining a core module {g[1],..., g[K]} is
equivalent to finding the K^th component, such that
[MATH: K=argmax<
mo
class="MathClass-open">(t<
mi>score(P<
mi>j)), :MATH]
(1)
where
[MATH:
Pj={∑i=1jz
(gi)sign(tsco<
/mi>re(gi))j,1≤j≤mi<
/mi>n(|gi∈D
EGs|,20),|gi∈DEG<
/mi>s|>0,0 <
/mtext>
<
mtext>
<
/mtext>
, |g
i∈DEGs|=0.
:MATH]
(2)
g[i ]is the i^th DEG in descending order and Pj is the PA containing
from g[1 ]to g[j]. | g[i ]∈ DEGs | denotes number of DEGs in the
pathway. The DEGs by default are the genes with p-value ≤ 0.05 in a
two-tailed t-test. We limit the largest marker size to 20 DEGs. In
fact, all marker sets have fewer than 20 components.
Reproducibility power
We consider an inference-validation pair datasets to be reproducible if
their pathway activities provide similar discriminative power. First,
we rank the PAs inferred from the inference dataset in descending order
by their tscores. Then, we define reproducibility by
[MATH:
Csco
re(N)=1N
∑i=1Ntscore(PIi)⋅tscore(PVi), :MATH]
(3)
where
[MATH:
PIi
:MATH]
is the i^th PA in descending order in the inference dataset, and
[MATH:
PVi
:MATH]
is its corresponding PA in the validation dataset. For the breast
cancer datasets, the overall reproducibility is then given by the
average Cscore of the inferred pathways over all six
inference-validation pairs.
Six methods were compared in this work, including CMI, CORG [[111]8],
Mean [[112]23], Median [[113]23], PCA [[114]24], and Individual Gene.
LLR(Log likelihood Ratio, [[115]25]) was not compared here, because it
is not discussed in the same gene expression space.
Consensus Feature Elimination (CFE)
In this work, gene expression and activity vectors are generalized as
features for classification. Given a set of features {x [1], x[2],...,
x[n]} with class labels {y[1], y[2],..., y[n]} ∈ {-1, +1}, the task of
binary classification is to find a decision function
[MATH: D(x)>0⇒x∈class(+)<0⇒x∈class(-)=0⇒x∈decis
ionboun<
mi>dary, :MATH]
(4)
We choose a linear decision function, which can be described as a
separating hyperplane:
[MATH: D(x)=w⋅x+b, :MATH]
(5)
with w the weight vector and b the bias value.
Linear classifiers such as Linear Discriminant Analysis (LDA) [[116]26]
and linear Support Vector Machines (SVM) [[117]27] use differing
optimization criteria to estimate the weight vector. Intuitively, the
weights indicate the importance of the associated features. Guyon et al
proposed Recursive Feature Elimination (RFE), which removes features
recursively based on their weights [[118]28]. However, classical RFE
exhibits lack of stability in feature selection [[119]29]. In contrast
to binary classification tasks that emphasize maximization of
classification accuracy, biomarker identification requires features
that are both accurate and reproducible across multiple experiments.
Thus, we propose a Consensus Feature Elimination (CFE) approach to
improve the stability of RFE. As illustrated in Figure [120]6, we first
generate 100 alternative 5-fold random splits of samples, upon which we
construct 500 classifiers and record their AUCs (Area Under Receiver
Operating Characteristic Curves) and weight vectors. Each feature was
then ranked by average square weight
[MATH: w¯=
∑j=1500<
/msubsup>(wj)2/500 :MATH]
. The lowest ranking feature was removed recursively until the maximum
average AUC was achieved. This process, which has also been called
Multiple RFE [[121]30] or ensemble feature selection [[122]31] is known
to increase biomarker reproducibility and accuracy by as much as 30%
and 15%, respectively. For the breast cancer datasets described in this
work, we found the maximum AUC to be very stable, while the
corresponding biomarker set was not always unique. Thus we chose to
repeat the above procedure 100 times, selecting the most frequently
occurring biomarkers as the final marker set.
Figure 6.
[123]Figure 6
[124]Open in a new tab
Diagram of Consensus Feature Elimination. We first generated 100
alternative 5-fold random splits of samples, upon which it constructs
500 classifiers with their AUCs as well as weight vectors. Each feature
is then ranked by its average square weight. The lowest ranking feature
was removed backward until the maximum average AUC was achieved. The
procedure is repeated for 100 times, and the most frequently occurring
marker set was regarded to be the ultimate marker.
Seven methods were compared in this work, including CMI, CORG [[125]8],
Mean [[126]23], Median [[127]23], PCA [[128]24], LLR [[129]25], and
Individual Gene.
Cancer gene enrichment analysis
The cancer gene enrichment analysis examines over-representation of
known cancer genes in a gene signature. Assuming the total number of
genes N, cancer genes M, and signature genes J, the probability of
having more than K cancer genes in a signature follows a hypergeometric
distribution:
[MATH: P(# of cancer genes
>K)=1−∑i=0<
/mn>K(iJ)<
/mrow>(M−
iN−J)(MN). :MATH]
(6)
Software
COMBINER was implemented in Matlab R2010a with Bioinformatics toolbox
v3.5. The source code is available on [130]http://www.ruotingyang.com.
Authors' contributions
RY, BJD, LRP, and FJD conceived and designed the research. RY, and BJD
performed the analysis, the statistical computations, and wrote the
paper. RY implemented the programs. All authors read and approved the
final manuscript.
Supplementary Material
Additional file 1
Figure S1: Comparison of CMI and other pathway inference methods using
SVM-CFE classifiers subject to top 100 inferred pathways.
[131]Click here for file^ (433.5KB, TIFF)
Additional file 2
Table S1: List of core module genes identified by CMI and CORG.
[132]Click here for file^ (19.9KB, XLSX)
Additional file 3
Table S2: Pathway markers identified by all methods.
[133]Click here for file^ (28.5KB, XLSX)
Additional file 4
Table S3: List of core module genes overlaid in KEGG pathway of
cancers.
[134]Click here for file^ (13.7KB, XLSX)
Additional file 5
Figure S2: Unique core module of cancer pathway identified by
CORG-COMBINER method.
[135]Click here for file^ (712.1KB, TIFF)
Contributor Information
Ruoting Yang, Email: ruoting@engineering.ucsb.edu.
Bernie J Daigle, Jr, Email: bdaigle@gmail.com.
Linda R Petzold, Email: petzold@engineering.ucsb.edu.
Francis J Doyle, III, Email: doyle@engineering.ucsb.edu.
Acknowledgements