J. Biomedical Science and Engineering, 2009, 2, 106-116
Published Online April 2009 in SciRes. http://www.scirp.org/journal/jbise JBiSE
MicroPath-A pathway-based pipeline for the
comparison of multiple gene expression profiles to
identify common biological signatures
Mohsin Khan1, Chandrasekhar Babu Gorle1, Ping Wang3, Xiao-Hui Liu2, Su-Ling Li1
1Molecular Immunology & bioinformatics Group, Microarray Facility, Division of Bio-Sciences, Brunel University, Uxbridge, UB8 3PH, UK; 2Intelligent Data
Analysis Group, Department of Information Systems and Computing, Brunel University, Uxbridge, UB8 3PH, UK; 3Immunology Group, Institute of Cell and
Molecular Sciences, Barts and London School of Medicine, London, UK. Correspondence should be addressed to Su-Ling Li (su-ling.li@brunel.ac.uk)
Received Jan. 2nd, 2009; revised Feb. 15th, 2009; accepted Mar. 4th, 2009.
High throughput gene expression analysis is
swiftly becoming the focal point for deciphering
molecular mechanisms underlying various dif-
ferent biological questions. Testament to this is
the fact that vast volumes of expression profiles
are being generated rapidly by scientists
worldwide and subsequently stored in publicly
available data repositories such as ArrayEx-
press and the Gene Expression Omnibus (GEO).
Such wealth of biological data has motivated
biologists to compare expression profiles gen-
erated from biologically-related microarray ex-
periments in order to unravel biological mecha-
nisms underlying various states of diseases.
However, without the availability of appropriate
software and tools, they are compelled to use
manual or labour-intensive methods of com-
parisons. A scrutiny of current literature makes
it apparent that there is a soaring need for such
bioinformatics tools that cater for the multiple
analyses of expression profiles.
In order to contribute towards this need, we
have developed an efficient software pipeline for
the analysis of multiple gene expression data-
sets, called Micropath, which implements three
principal functions; 1) it searches for common
genes amongst n number of datasets using a
number crunching method of comparison as
well as applying the principle of permutations
and combinations in the form of a search strat-
egy, 2) it extracts gene expression patterns both
graphically and statistically, and 3) it streams
co-expressed genes to all molecular pathways
belonging to KEGG in a live fashion. We sub-
jected MicroPath to several expression datasets
generated from our tolerance-related in-house
microarray experiments as well as published
data and identified a set of 31 candidate genes
that were found to be co-expressed across all
interesting datasets. Pathway analysis revealed
their putative roles in regulating immune toler-
ance. MicroPath is freely available to download
from: www.1066technologies.co.uk/micropath.
Keywords: Co-Expression Analysis, Microarray,
Permutations and Combinations, Multiple Gene
Expression Analysis
There is a general consensus amongst scientists and re-
searchers that the fundamental asset of microarray tech-
nology lies in its inherent ability to produce a global
snapshot of the cellular state in the milieu of any given
biological question. It is therefore not surprising that
microarrays have revolutionised the field of molecular
biology by offering an efficient and cost effective me-
dium for biologists to quantify mRNA transcript levels
of several thousands of genes concurrently in order to
observe specific states of the transcriptome (in response
to a particular treatment or specific time point). Owing to
this innate faculty to decipher the transcriptome, gene
expression profiles pertaining to a wide variety of bio-
logical questions are being rapidly generated by scien-
tists worldwide and are deposited and subsequently made
accessible through public repositories such as ArrayEx-
press [1] and the Gene Expression Omnibus [2]. With so
much wealth of high throughput biological data made
available, biologists have become motivated to utilise
these sets of data in an attempt to investigate common
regulatory signatures, which may be implicating the
transcriptome state across multiple gene expression pro-
files sharing a similar biological theme. One of the most
widely accepted methodologies of comparing expression
profiles is based on the assumption that genes across
different biological conditions sharing similar expression
patterns are likely to be involved in the same biological
processes [2], and therefore, may share common regula-
tory signatures. By using this method of comparison,
which is one of the most successful methods to date,
coupled with the availability of publicly available data
SciRes Copyright © 2009
M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116 107
SciRes Copyright © 2009 JBiSE
repositories offering gene expression profiles, biologists
have been granted the opportunity to answer complex
biological questions pertinent to biological phenomena
underlying various different disease states.
To this end, we have developed a novel bioinformatics
software pipeline called MicroPath, which specialises in
the cross comparison of multiple gene expression data-
sets and attempts to identify common regulatory signa-
tures from the standpoint of molecular pathway analysis.
When one scrutinises current literature relevant to auto-
mated solutions of gene expression analysis, it becomes
apparent that there is an increasing demand for software
applications that offer an efficient pipeline to the analysis
of multiple gene expression profiles. Although current
meta-analyses studies have been conducted with the
purpose of employing statistical techniques to compare
cDNA and affymetrix gene expression profiles [3,4,5,6],
it cannot be denied that there is a mounting need for this
process to be automated. Nevertheless, various ap-
proaches/algorithms of statistical nature have already
been implemented with the purpose of identifying the
most relevant pathways in a given experiment [7,8,9]
together with methods such as Gene Set Enrichment
Analysis (GSEA), which ranks genes based on the cor-
relations between their expressions and observed pheno-
types in the context of biological pathway discoveries
[10]. There are also tools available that functionally an-
notate gene expression data [11,12]. Albeit, it remains
infeasible for biologists to cross compare several expres-
sion profiles without an automated solution, and hence,
they are faced with the labour-intensive task of employ-
ing manual methods to carry out their comparisons. Mi-
croPath uses the meta-analytic standard and has been
specifically developed to: compare several significantly
expressed sets of genes in order to find the intersection
of common genes using both number crunching methods
as well as the classical permutation and combination
principle, extract putative regulatory signatures using
both statistical and graph-based approaches and finally,
mapping these sub-sets of co-expressed genes to mo-
lecular pathways all in the form of a high throughput
The front-end of MicroPath was developed in Visual
Basic.Net and Perl, and the database back-end was de-
veloped in MySQL. Upon analysing the users input files
(gene expression profiles), processed data is displayed
intuitively on the graphical user interface, which is
equipped with various interactive objects such as chart-
ing facilities, buttons, drop-down menus and user in-
put/output dialogues. The interface is also equipped with
a function to export processed data into Microsoft excel
for further scrutiny and use.
2.1. System Architecture
MicroPath carries out meta-profiling of multiple gene
expression datasets using two different approaches.
Firstly, the intersection of common genes is identified
across n number of expression profiles, which is then
plotted graphically using a simple number crunching
exercise. The second approach applies to a situation
where an attempt to identify common genes across n
number of expression profiles using the aforementioned
approach fails due to the absence of common genes
across all datasets (this situation is especially common
when a large number of expression profiles are compared,
which reduces the probability of finding a common gene
amongst them). Consequently, MicroPath applies the
permutations and combinations mathematical principle
to solve this problem (refer to implementation of
meta-analysis strategy below for details). Once the in-
tersection of a set of common genes has been identified
and subsequently displayed on the interface (using either
of the above methods), the next stage in the analysis is to
extract patterns from the intersection in order to identify
common genes that are being expressed in accordance
with the biological question. MicroPath offers a semi-
automated graph-based approach to achieve this as well
as classical statistics to identify the overall correlation of
gene expression. Finally, co-expressed genes (common
genes that are expressed in accordance to the relevant
biological question) are mapped to all molecular path-
ways known to date in order to reveal their molecular
dependencies (refer to Figure 1 for the complete system
2.2. Implementation of Meta-analysis Strategy
In theory, an intersection of a sub-set of common genes
across multiple gene expression profiles should be eas-
ily attainable using simple number crunching methods
of comparison. In practice, this is not always the case
since the likelihood of identifying genes sharing com-
mon accession identifiers decreases as the number of
profiles to compare increases. This inverse relationship
makes sense both mathematically and biologically.
From a biological perspective, regulatory signatures
tend to be diluted over entire datasets and as a result,
only a proportion of the total number of profiles to
compare may actually share common genes. In such a
scenario, using a simple method of comparison would
break down at some point and no common genes
would be reported to the user, although common genes
may be present within n-1 expression profiles. To pre-
vent potentially interesting biological findings to be
hampered at this point in the analysis, we have applied
the principle of mathematical combinations to the
comparison of multiple gene expression profiles. All
possible combinations of comparing n number of
datasets with each other are firstly computed using the
combination equation:
108 M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116
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This generates the total number of permutations of
comparing datasets (Cr) for given values of n (total
number of datasets imported by user) and r (number of
intended datasets used to search for common genes when
zero common genes are reported across n datasets) (Ta-
ble 1).
Figure 1. Functions of MicroPath. Users are prompted to import up to 10 gene expression profiles, which are then compared using a
direct comparison method. If this method yields zero common genes, MicroPath automatically attempts to identify an intersection of
common genes by reducing the search space to n-1 datasets using permutations and combinations. This process is continued until at
least 1 common gene is reported. Following this, users are provided with a function to search for expression patterns graphically and gene
expression correlations are calculated statistically using the pearson’s correlation coefficient algorithm. Finally, co-expressed genes are
mapped to all molecular pathways of KEGG in a high throughput fashion by automatically accessing its API via SOAP-Lite.
M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116 109
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Table 1. Multiple gene expression profile search strategy generated from applying the principle
of permutations and combinations. The first column represents the total number of expression
datasets, n, that users may import (this is the search space). The second column represents, r,
the number of expression datasets to compare if zero common genes are reported to be
matched across n datasets. The final column represents the total number of mathematical com-
binations possible for each given value of n and r.
Total number of
expression datasets (n)
Number of intended
expression datasets to
compare when
comparing n datasets
yields no results (r)
n - r
Total number of
combinations of r (Cr)
10 9 1 10
10 8 2 45
10 7 3 120
10 6 4 210
10 55252
10 4 6 210
10 3 7 120
10 2 8 45
9 8 1 9
9 7 2 36
9 6 3 84
9 5 4 126
9 4 5 126
9 3 6 84
9 2 7 36
8 7 1 8
8 6 2 28
8 5 3 56
8 4 4 70
8 3 5 56
8 2628
7 6 1 7
7 5 2 21
7 4 3 35
7 3 4 35
7 2521
6 5 1 6
6 4 2 15
6 3320
6 2 4 15
5 4 1 5
5 3 2 10
5 2 3 10
4 3 1 4
4 2 2 6
3 2 1 3
These combinations of datasets (Cr) are then used as a
criterion to search for common genes across r number of
gene expression profiles when comparing n number of
datasets fail to yield any common genes. However in this
scenario, n number of datasets are still used as the search
space from which all possible combinations (Cr ) of r
datasets are compared to each other in order to increase
the probability of finding a common gene. Once common
genes have been identified using this method, MicroPath
will report the results to the interface.
2.3. Extracting Gene Expression Patterns
Graphically and Statistically
Following the identification of common genes across n
datasets using either of the methods described earlier, the
next stage in the analysis is to generate a graphical repre-
sentation of this expression data from which biologically
meaningful patterns can be extracted. Because signals
pertaining to transcriptome states tend to be diluted over
entire profiles, a specific criterion is required to narrow
down the common genes of interest to include only those
genes that are consistently regulated according to the bio-
logical question. The assumption we have made is that any
given common gene across n datasets can exhibit one of
three specific behaviours. It can either be consistently
upregulated across all datasets, downregulated across all
datasets and up or downregulated across all datasets.
Based on the nature of the specific biological question,
users can select the appropriate pattern from the options,
which will result in a graphical display of those genes
which satisfy the search criteria. Together with this faculty
to graphically extract patterns for individual gene expres-
sion data points, MicroPath also implements the pearsons
correlation coefficient statistical test in order to extract a
global gene expression pattern existing between common
genes pertaining to two individual expression profiles. The
correlations are calculated in a pair-wise manner until
each expression data has been statistically compared to all
other datasets within n, according to the pearsons correla-
tion coefficient equation:
() ()
110 M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116
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Each pair-wise score is then finally averaged in order
to provide a global measure of correlation existing be-
tween n expression profiles. Scores are reported from-1
(perfect negative correlation) to+1 (perfect positive cor-
2.4. High Throughput Molecular Pathway
To decipher molecular mechanisms fundamental to the
researcher’s biological question, it is necessary to map
common gene expression profiles of co-expressed genes
to molecular pathways. This is because biological path-
ways reveal molecular dependencies that exist between
genes by illustrating how they collaborate with one an-
other when they participate in specific biological func-
tions. Furthermore, pathways reveal various signalling
cascades that play imperative roles in dictating these
gene associations. In light of this, we have implemented
Micropath to access the Application Programming Inter-
face (API) of the molecular pathway database belonging
to KEGG [13] using SOAP-Lite in order to dynamically
interact with the static pathway maps. Perl scripts were
written for MicroPath to specifically 1) search for user’s
co-expressed genes in all biological pathways, 2) high-
light genes on to pathways, and 3) return the results of
the search to Micropath’s interface (i.e. URL’s of colour
coded pathway maps) (Figure 2). Once MicroPath has
searched for all of the user’s co-expressed genes in all of
the molecular pathways, the URL of each pathway is
displayed on the sub-interface. In order to avoid redun-
dancy issues, the URL for each pathway will highlight
all co-expressed genes that participate in a given path-
way. To help users identify biologically meaningful
pathways relevant to their specific biological question,
MicroPath will calculate the number of genes identified
in a given pathway and 1) express this as a percentage in
relation to the total number of common genes from the
intersection and 2) express this as a percentage in rela-
tion to the total number of genes belonging to that path-
Clicking on these links will generate the specific
KEGG pathway in HTML on which users co-expressed
genes will be highlighted.
Figure 2. Flow diagram of how MicroPath carries out high throughput molecular pathway analysis by connecting to the API of KEGG.
M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116 111
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2.5. Generating and Processing Gene Ex-
pression Datasets
Gene expression datasets used for the purpose of this
article were generated from our in-house microarray ex-
periments as well as published datasets, where the fold
change approach was used to select a set of differentially
expressed genes from pre-processed data. Matchminer
[14] and the Synergizer [15] tools were used to convert
gene Hugo identifiers and long names into Genbank ac-
cession Id’s in order to ensure that the gene identifiers
were of the same type across all datasets prior to com-
parison. Raw expression data was generated, filtered and
normalised using GenePix pro 4.1 [16] and Acuity 4.0
[17] software. Although we used cDNA microarray data
for the purpose of demonstrating MicroPath’s capabilities,
other data types generated from different platforms such
as affymetrix can also be analysed provided Genbank ac-
-cession identifiers are used to represent the genes.
Regardless of the biological question, a typical microar-
ray experiment almost always results in the generation of
a set of differentially expressed genes, which represents
genes of most importance to the biologist. Therefore, by
carrying out several biologically related microarray ex-
periments, several sets of differentially expressed genes
would be generated, which would need to be compared
and mined efficiently in order to help answer the bio-
logical questions asked by the investigators from differ-
ent research laboratories around the world. Employing
manual methods of comparison in this situation would be
very inefficient and infeasible. In light of this, to demon-
strate the benefits that can be derived from analysing
multiple gene expression profiles using MicroPath, we
employed datasets generated from our in-house microar-
ray experiments as well as published data. The biological
question related to these studies focussed on unravelling
the underlying molecular mechanisms dictating immune
tolerance by analysing the role of Egr-2 in implicating
T-cell tolerance. Although the Early Growth Response
gene (Egr-2) has been recently characterised as a candi-
date tolerance-inducing transcription factor, which inter-
acts with specific genes in order to induce the state of
T-cell tolerance [18,19], the possibility of further puta-
tive unknown target genes exists that may be vital to the
mechanism of tolerance. Hence, the biological purpose
of our experiments was to attempt to identify such po-
tentially important genes via the comparison of biologi-
cally related expression datasets using MicroPath.
Data consisting of a set of differentially expressed
genes generated from the comparison of tolerance Vs
activated mice CD4+ T cells was obtained from the Ar-
rayExpress website (accession number: e-mexp-283).
The first in-house experiment aimed to generate differ-
entially expressed genes from the comparison of an
un-stimulated T cell line from which the Egr-2 gene had
been knocked out and a wild type un-stimulated cell line.
The second in-house experiment focussed on the com-
parison between an Egr-2 knock-out T cell line activated
with CD3/CD28 for 6 hours and a wild type cell line also
activated with CD3/CD28 for 6 hours. Results generated
from these experiments were then compared with the
aforementioned published tolerance data using MiNer in
order to understand the molecular mechanisms control-
ling immune tolerance.
3.1. Comparison of Gene Expression Pro-
files Pertaining to Immune Tolerance
The first step in the analysis was to subject the above-
mentioned expression profiles to MicroPath in order to
identify genes amongst them that had the same accession
identifiers. Having done this, MicroPath identified 31
differentially expressed genes that were common to all
three expression datasets and generated a graph to de-
lineate their expression values (Table 2, Figure 3). A
simple number crunching exercise was used to perform
this task since its use generated a reasonable number of
common genes, which did not warrant the use of permu-
tations and combinations to perform the search. The next
step was to use these 31 differentially expressed genes as
a search space to determine those genes that have the
potential to be co-expressed. In order to do this, we em-
ployed MicroPath’s graphical utility to extract gene ex-
pression patterns, which led to the identification of 6/31
genes that were found to be upregulated in tolerance Vs
activated CD4+T-cells and downregulated in both
p-KOA0 Vs WTA0 and p-KOA6 Vs WTA6 datasets
(Table 2). The remaining 25 common differentially ex-
pressed genes were found to be highly and lowly ex-
pressed in tolerance and knock-out datasets respectively.
Statistical analysis revealed an overall pearson’s correla-
tion score of 0.109 from the pair-wise comparison of
tolerance data with p-KOA0 Vs WTA0 and a score of
-0.123 from the comparison of tolerance with p-KOA6
Vs WTA6. Furthermore, Reverse Transcriptase PCR
experiments confirmed that 15 genes from our tolerance
Vs activated data were found to be highly expressed in
immune tolerance and from these 15 genes, 8 were found
to be common amongst all three expression profiles (Ta-
ble 2).
Because Egr-2 has been previously characterised and
found to be highly upregulated in immune tolerance,
these results generated from MicroPath are biologically
significant because as expected, those genes that were
highly expressed in our tolerance Vs activated datasets
were found to be insignificantly expressed in our
p-KOA6 Vs WTA6 and p-KOA0 Vs WTA0 datasets
(from which the Egr-2 gene was knocked out of the cell
lines). Amongst these genes, Ap1s1, Shd, Surf6, Vil2,
Lilrb4, Tbx21 and Pdcd1lg2 (Table 2) have been con-
firmed to be upregulated in the process of immune toler-
ance [20], all of which were found to exhibit low expres-
sion values in our knock-out expression datasets. This
consistent gene expression pattern can be seen graphi-
cally in Figure 3. However, from the 31 interesting
common genes, 16 were not confirmed to be involved in
112 M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116
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Table 2. Tabulated overview of gene accession ids, Hugo ids and fold change values belonging to 31 common
genes identified from the comparison of tolerant Vs activated CD4+T cells, p-KOA0 Vs WTA0 and p-KOA6 Vs
WTA6 expression datasets. Entries highlighted in bold represent genes that were found to be up-regulated in tol-
erance Vs activated CD4+ T cells and down-regulated in both p-KOA0 Vs WTA0 and p-KOA6 Vs WTA6 datasets.
Entries with * represent genes that have been confirmed to be highly expressed in tolerance by RT-PCR.
GeneID HUGOID FoldChange(p
FoldChan ge(p
Fol d Change
0.371336 0.6245256.373
0.542474 0.31525 4.965
0.243646 0.7999 1.658
0.080480.116434 2.857
1.31334 0.46688 5.42
0.18816 0.393664.552
0.17495 0.535822.838
0.272072 0.1263014.365
0.149539 1.4758063.836
0.498240.319645 3.151
0.313489 0.1326891.573
0.903350.8226 4.745
3.083863 1.6607393.521
0.024803 0.42533 1.613
0.53540.71558 4.363
0.616460.035844 1.665
0.980840.191964 4.701
0.466922 0.34601 2.032
0.584494 0.4202774.905
2.13728 0.694585.677
0.792335 1.1108982.111
2.776384 3.0044494.809
0.477520.500297 2.246
0.118995 0.4285911.664
0.06660.053081 4.284
0.124767 0.32731 1.595
0.778767 0.44703 1.718
0.291602 0.00772 1.609
0.291219 0.23966 5.091
1.140087 0.0791823.921
0.154049 0.2645412.035
tolerance by RT-PCR yet some of them also exhibited a
coherent pattern of gene expression. For example, Ptma,
Scd2, Hdac6, Pltp and Chka were all highly expressed in
tolerance and conversely downregulated in both knock
out datasets. There is a possibility that these genes may
also be insignificantly expressed due to the absence of
Egr-2. However, conducting RT-PCR for these specific
genes would be required in order to confirm that their
over-expression results in T-cell tolerance.
3.2. Deciphering Gene Regulatory Networks
of Co-Xpressed Genes Via High Throug
-Hput Molecular Pathway Analysis
The final stage of the analysis entails using MicroPath’s
function to connect to the Application Programming In-
terface (API) of KEGG via SOAP-Lite in order to carry
out high throughput molecular pathway analysis. There-
fore, for this stage in the analysis, we used MicroPath to
map 31 of our co-expressed interesting genes to KEGG
pathways and from these 31 genes, 14/31 were identified
in a total of 31 molecular pathways (Table 3). Interest-
ingly, several of these pathways were related to the study
of immunology and illustrated biological networks such
as MapKinase, Jak-Stat, T-cell receptor signalling and
Cytokine-cytokine interactions. More specifically, the
Pdcd1lg2 gene (accession id: NM_021396) was identi-
fied in the Cell Adhesion Molecules (CAM) pathway
(Table 3) and studies have confirmed that the over-ex-
pression of Pdcd1lg2 has resulted in consistently low
levels of Interleukin-2 (IL-2) in naive CD4(+) T-cells
[21]. Further studies have correlated the over-expression
of this gene to the negative regulation of T-cell activation.
In one particular study, PDL2 (Pdcd1lg2) deficient mice
were created in order to characterise the function of this
gene in T-cell activation and tolerance, and results gen-
erated from this study suggested that Antigen-presenting
cells from PDL2-deficient mice were found to be more
potent in activating T-cells in vitro when compared to the
wild-type counterparts [22]. These findings are conclu-
sive and correlate well with the results generated from
our in-house microarray experiments because using Mi-
croPath to compare all three of our datasets followed by
extracting gene expression patterns from them resulted in
an important finding that Pdcd1lg2 was not only found to
be over-expressed in tolerance (fold change of 3.921),
but it was also under-expressed in our KOA0 Vs WTA0
and KOA6 Vs WTA6 knock-out datasets (with a fold
change of 1.140 and 0.079 respectively) (Table 2). This
M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116 113
SciRes Copyright © 2009 JBiSE
particular finding is in agreement with the aforemen-
tioned studies, concluding that Pdcd1lg2 has a negative
inhibitory role towards the process of T-cell activation.
In addition, molecular pathway analysis of the Inter-
leukin-10 (IL-10) gene using MicroPath, identified its
role in the Cytokine-cytokine interaction, Jak-STAT and
T-cell receptor signalling pathways; all three of which
are important immunological pathways. IL-10 is a well
known cytokine, which has previously been shown to
successfully induce immune tolerance in Dendritic Cells
[23]. Results generated from MicroPath revealed that
IL-10 was highly expressed in our tolerance data with a
fold change of 3.521, which was found to be expressed
lower in our KOA0 Vs WTA0 profile (fold change:
3.084). Interestingly, following activated with
CD3/CD28 for 6 hours, its expression dropped signifi-
cantly to 1.66, perhaps attributable to the absence of
Egr-2. Likewise, other genes from the 31 co-expressed
interesting genes show similar patterns of expression and
perhaps may be candidate genes for Egr-2 mediated
T-cell tolerance. However, this is yet to be confirmed by
publications. Finally, the pathway analysis function of
MicroPath was used to calculate the percentage of genes
identified in each pathway in relation to 1) the intersec-
tion of common genes and 2) the total number of genes
comprising each pathway. From the results, the Cell Ad-
hesion Molecules (CAM) pathway was particularly sig-
nificant since 12.91% of the overall pathway was af-
fected by 6.84% of genes common to all 3 expression
profiles (Table 4).
Figure 3. A preliminary graphical overview of common interesting genes generated from the comparison of tolerant
Vs activated CD4+ T cells (green), p-KOA0 Vs WTA0 (red) and p-KOA6 Vs WTA6 (blue) expression datasets. It can
be seen that genes that are highly expressed in tolerance appear to be expressed poorly in the knock-out datasets.
This pattern is consistent throughout the 31 gene expression data points.
Table 3. Tabulated data generated from high throughput molecular pathway analysis of co-regulated genes. 14/31
common interesting genes were identified in a total of 31 molecular pathway maps of KEGG.
Accession ID HUGO ID Pathway IDTotal No of
Accession ID
ID Pathway ID Total No of
NM_007381 Acadl
5 NM_009510 Vil2
mmu04810 2
NM_007664 Cdh2 mmu045141 NM_008205 H2-M9
NM_013488 Cd4
4 NM_013814 Galnt1mmu00512
mmu01030 2
NM_011696 Vdac3 mmu040201 NM_019777 Ikbke
mmu04620 2
NM_011125 Pltp mmu03320 1 NM_010102 Edg6 mmu04080 1
NM_016772 Ech1
3 NM_021396 Pdcd1lg2mmu04514 1
NM_010548 Il10
3 NM_013652 Ccl4
mmu04620 2
114 M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116
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The fundamental strength of MicroPath stems from
the implementation of a novel search strategy for the
comparison of multiple gene expression profiles. Al-
though there are a few software that cater for multiple
gene expression comparison, there is currently no
software that searches for common genes beyond sim-
ple number crunching methods of comparison (Table
5). Just because a direct comparison of a given num-
ber of datasets may not yield any common genes, it
does not mean that the analysis should end here since
there is a potential to identify common genes across
n-1 profiles. MicroPath ensures that such genes are
identified, which current software would overlook.
When coupled with other important functions such as
pattern extraction and pathway analysis, it becomes
apparent that MicroPath would offer valuable assis-
tance to biologists wanting to decipher their high
throughput data.
Table 4. Results generated from pathway analysis showing the extent to which each pathway is affected by common genes from the
intersection. The percentages reflect the proportion of common genes that contribute towards controlling the proportion of each pathway.
Pathway ID Pathway Name GenBank
Accession ID Result from Analysis
mmu00071 Fatty Acid Metabolism NM_007381 3.26% of genes contribute 8.45% role in pathway
mmu00280 Valine, leucine and isoleucine degradation NM_007381 3.26% of genes contribute 2.73% role in pathway
mmu00410 Beta Alanine Metabolism NM_007381 3.26% of genes contribute 7.14% role in pathway
mmu00640 Propanoate Metabolism NM_007381 3.26% of genes contribute 5.88% role in pathway
mmu03320 PPAR Signalling Pathway NM_007381 3.26% of genes contribute 1.92% role in pathway
mmu04514 Cell Adhesion Molecules
12.91% of genes contribute 6.84 % role in pathway
mmu04612 Antigen Processing & Presentation NM_013488 3.26% of genes contribute 2.44% role in pathway
mmu04640 Hematopoietic Cell Lineage NM_013488 3.26% of genes contribute 0.76 % role in pathway
mmu04660 T Cell Receptor Signalling Pathway NM_013488
NM_010548 6.45 % of genes contribute 3.33 % role in pathway
mmu04020 Calcium Signalling Pathway NM_011696 3.26% of genes contribute 2.33 % role in pathway
mmu00350 Tyrosine Metabolism NM_016772 3.26% of genes contribute 2.17 % role in pathway
mmu04060 Cytokine-cytokine receptor interaction NM_010548
NM_013652 6.45 % of genes contribute 0.73 % role in pathway
mmu04630 JAK-STAT Signalling Pathway NM_010548 3.26% of genes contribute 3.85 % role in pathway
mmu04670 Leukocyte Transendothelial Migration NM_009510 3.26% of genes contribute 1.25 % role in pathway
mmu04810 Regulation of Actin Cytoskeleton NM_009510 3.26% of genes contribute 1.47 % role in pathway
mmu04940 Type I Diabetes Mellitus NM_008205 3.26% of genes contribute 4.35 % role in pathway
mmu00512 O-Glycan Biosynthesis NM_013814 3.26% of genes contribute 10 % role in pathway
mmu04010 MAPK Signalling Pathway NM_019777 3.26% of genes contribute 0.83 % role in pathway
mmu04620 Toll-Like Receptor Signalling Pathway NM_019777
NM_013652 6.45% of genes contribute 1.32 % role in pathway
mmu04080 Neuroactive Ligand-Receptor Interaction NM_010102 3.26% of genes contribute 1.15 % role in pathway
Table 5. Functional comparison of MicroPath to similar software packages and applications.
Function MicroPath EXPANDER
Studio [26] KEGG [13] BioCarta
Suitable for high throughput
data analysis YES YES YES YES NO NO YES
Suitable for comparing multiple
gene expression profiles YES YES NO YES NO NO YES
Implementation of efficient
algorithm to search for common
genes from n-1 datasets
Graphical representation of gene
expression values from multiple
Pattern extraction from Graph
Construction of pathway maps YES NO NO YES YES YES NO
Mapping gene expression data
to pathway maps YES NO NO YES NO NO NO
User interactive software (S) or
Database (D) S S S S D D S
M. Khan et al. / J. Biomedical Science and Engineering 2 (2009) 106-116 115
SciRes Copyright © 2009 JBiSE
4. Conclusion
In this article, we have illustrated the potential benefits
that can be derived from using MicroPath for the analy-
sis of multiple gene expression profiles. Each function of
the software has been developed to streamline the overall
analysis pipeline, providing users with a walkthrough of
how their data is biologically deciphered. Here, we have
applied to our software, microarray datasets generated
from different laboratories pertaining to the molecular
mechanisms underlying immune tolerance. However,
MicroPath is capable of analysing data for any given
biological question, whether the datasets are taken from
public repositories such as ArrayExpress or generated
from in-house microarray experiments. We believe that
its faculty to use both number crunching and permuta-
tions and combinations as the search strategy to identify
the intersection of common genes, coupled with its func-
tion to extract gene expression patterns graphically and
statistically makes this an attractive software for biolo-
gists to use. Finally, its ability to carry out live streaming
of mapping genes to biological pathways makes it a use-
ful tool for the automation of multiple gene expression
Availability and requirements
Project name: MicroPath
Project home page: www.1066technologies.co.uk/mi-
Operating system(s): MicroPath has been tested on
Windows 2000, XP and Vista
Programming language: Visual Basic.Net, Perl
Other requirements: None
License: N/A
Any restrictions to use by non-academics: No
This study was supported by grants from the UK Medical Research
Council (MRC) (Grant number: G0300520).
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