# NCR-PCOPGene: An Exploratory Tool for Analysis of Sample-Classes Effect on Gene-Expression Relationships

Authors: Juan Cedano; Mario Huerta; Enrique Querol
Journal: Advances in Bioinformatics
 (2008)

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## Abstract

Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene expressions under different conditions, to classify these conditions, and to study their effect on the different relationships. The web application makes it easier to define the sample classes, grouping the microarray experiments either by using (a) biological, statistical, or any other previous knowledge or (b) their effect on the expression relationship maintained among specific genes of interest. By means of the type (a) class definition, the researcher can add biological information to the gene-expression relationships. The type (b) class definition allows for linking genes correlated neither linearly nor nonlinearly. Conclusions. The PCOPGene tools are especially suitable for microarrays with large sample series. This application helps to identify cellular states and the genes involved in it in a flexible way. The application takes advantage of the ability of our system to relate gene expressions; even when these relationships are noncontinuous and cannot be found using linear or nonlinear analytical methods.


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## Body

## 1. Introduction

DNA microarray technology enables
high-throughput gene-expression analysis, which allows researchers to compare
the activity of genes in multiple cellular conditions. There are several
relevant web applications for microarray analysis, that is, GEO [1], BIOREL [2], ArrayExpress [3], and MicroGen [4]. Currently, most tools try to
extract biological information from such high-throughput expression data
combining information from coexpressed genes [5] as well as additional annotations
extracted from Gene-Ontology [6], phylogenetic information [7], or pathway data [8]. In this paper, a different and
complementary new approach based on the effect of the experiments on the
fluctuations of gene-expression relationships is proposed.The suitable data for our type of
analysis can be provided by(a) temporal series, useful to study synchronous cellular events, and (b) serial analysis of gene-expression
samples under different conditions (i.e., chemotherapy, temperature, radiation,
starvation, etc.) which are more useful for studying asynchronous events. The
progressive increase of microarray sample series [9] motivates a more thorough analysis
of expression relationships and gene dependencies throughout these large
series, trying to rescue global gene behaviours, cellular states and
phenotypes. The GEO database [1] facilitates the study of the
microarray experiments grouped into predefined classes introduced by the
microarray authors. Nevertheless, if the researcher wants to understand the
microarray experiments effect on the expression relationships and elucidate
hidden cell states, he/she needs a more specific approach. To study the effect
of the microarray experiments on the relationships among different sets of
genes, our web tool allows the user to define flexible sample classes. It involves
a significant new approach by going from the linear, nonlinear, and mutually exclusive
gene relationships to the complex, noncontinuous dependence among genes or sets
of genes.Our strategy begins from the
analysis of the “continuous gene-expression relationships” (abbreviated in the
paper as “gene relationships” or “expression relationships”). This analysis
provides the “inner pattern” of the expression relationships.
This “inner
pattern” describes the relationship in ann-space
(sample space of n dimensions), where
each axis represents the expression level of each one of the genes which are
being related, and from a data cloud with the microarray experiments. This
pattern analysis detects the lineal, nonlineal, and mutually excluding
relationships, providing a series of points in the n-space which describe the “inner pattern” of the expression
relationship. Beyond this first analysis remains the second objective: the
study of the “noncontinuous dependence” among gene expressions. To perform
this, the sample classes are defined allowing for the study of their effect on
gene expressions and the stated continuous dependencies. This task facilitates
relating genes which do not have continuous relationships but rather have
local, unidirectional, or other kinds of complex dependence in their
expression. The final objective is that the sample classes defined help to
identify the hidden cellular states and phenotype changes (and provide the
genes involved).


## 2. Methods

Pattern
extraction methodologies are very powerful techniques to extract biological
knowledge from data, as was shown in our previous work [10],
where this approach was used for analysis of populations, diagnosis, and
prognosis. In the present work, a useful extension to high-throughput
microarray analysis is presented.


### 2.1. Preprocess

When microarray data are uploaded,
the inner pattern of the expression relationship of all pairs of microarray
genes is calculated automatically. These calculations are made by the principal
curves of oriented points (PCOPs)
calculus [11] and recognise the linear, nonlinear
and mutually-excluding relationships between genes (both genes cannot be
overexpressed or underexpressed at the same time, or one gene can be
overexpressed if the other one is underexpressed) [12]. The noncorrelation factor between
each pair of gene expressions is calculated in the preprocess pattern analysis,
leading to the identification of those genes which are more correlated with
each gene. How it will lead to the “analysis expansion” around the researcher’s
genes of interest is shown in Results, and a brief description of the PCOP
calculation [11] is given in Section 2.4.


### 2.2. Defining Sample Classes

The researcher could test his/her
hypotheses and intuitions by defining the sample classes. The sample-classes
definition can be made in the following three different ways.(i)
Selecting the samples from a range of gene-expression data.(ii)
Clustering the samples from a gene-expression relationship: once the inner pattern among a set of
correlated genes of interest is calculated (once again by PCOP calculus [11]), the samples that constitute the
different “local behaviours” of this inner pattern can be clustered (and a high
correlation implies a better-fitted pattern). Then, the effect of these
conditions can be studied on any other genes relationship, where these genes
could not be correlated with the set initially used to cluster the microarray
experiments. Note that although in the preprocess PCOP is calculated for two
dimensions only (a pair of genes), now it is calculated for n≥2 dimensions (the n genes of the gene set).(iii)
Classifying the microarray experiments into different classes using
previous knowledge.


### 2.3. Colouring the Sample Classes in a Genes Relationship

Now, the
graphical interface facilitates the visualisation of the defined-classes effect
on gene relationships by colouring the samples (as is shown in Figure1) for
every set of genes of interest with the intention of studying their effect. The
study of this effect is especially relevant in the nonlinear relationships in
order to understand the biological sense of the slope change, possibly due to a
phenotype change.Sample class definitions using the PCOPGene web interface.Example 1: in the SGC(GUCY1B3) and Q6(QSN6) relationship ((a) f*=0.08), the samples of the two extremes of the SGC/Q6 inner pattern are clustered into two classes by clicking on the POPs, these classes are coloured in the GATA3 and AML1(RUNX1) relationship ((b) f=0.10). Example 2: in the GATA3 and AML1(RUNX1) comparison window (b), the samples of GATA3 over-expressed are grouped with the range-selection facility and coloured in the SGC and Q6 relationship (a). Example 3: in the GATA3, AML1(RUNX1), SGC and Q6 relationship ((c) f=0.28), the plot of the SGC and Q6 plane is shown. The coloured classes shown have been obtained by clustering the microarray experiments according to their correlation. These classes represent differentiated cell processes with different genes involved. (f* = non-correlation factor provided by the PCOP
calculus [10]).
(a)(b)(c)The class definitions  of types (i) (selecting the samples from a range of
gene-expression values) and (ii) (clustering the samples from a gene-expression
relationship) are made using these graphical visualisations of the
initial-genes-of-interest pattern analysis. Type (iii) is made using the web
interface that enquires for the samples belonging to each class, or directly
uploading the file.


### 2.4. Defining the Sample Classes from a Nonlinear Expression Relationship

The system makes use of the principal
curves of oriented points (PCOPs)
calculus to obtain the nonlinear inner pattern [11, 13] used to cluster the samples (remember
that PCOP calculus is also used to obtain the correlation degree between all
pairs of microarray genes). The analysed variables with the PCOP method can be
independent because the method uses a hidden variable for ordering the data (in
contrast to other nonlinear analyses like regression curves) which is suitable
for gene-expression comparisons [14]. PCOP is defined by the
generalisation, at the local level, of the principal-components variance
properties. From the sample-space data of n dimensions (one per gene expression), our system obtains discretised states
named “principal oriented points” (POPs), which represent this principal
component generalisation at the local level or local area. The series of principal
oriented points (POPs) obtained makes up the principal curve of oriented points
(PCOPs) or inner
pattern of the data cloud [11, 13]. The final series of POPs obtained
will minimise the dispersion degree of the samples around PCOP [11, 13].
As a result, even with no isotropic distribution of the data, we obtain
a very realistic inner pattern, compared, for example, with methods like least
squares, and a very accurate data-dispersion measurement (the noncorrelation
factor), compared with artificial intelligence methods [11, 15].Next, when the user selects the POPs
(the different discretised states obtained in the PCOP calculus) using the
graphical interface, the samples belonging to each POP scope or local area are
selected. One of the main keys of PCOP calculus is precisely the scope
detection of the local area of each POP, as has been explained in a previous
work [11]. In this way, the different sample
classes are finally defined based on the “local behaviours” of the gene-expression
relationship. For more details about this clustering method based on the
fluctuations of the inner pattern, consult our previous work  [10].


### 2.5. Gene Search Based on the Distribution of the Classes along the Gene-Expression Range

To make it
easier to relate the genes in a noncontinuous approach, the user can carry out
a gene search based on the expression level required for each class defined.
This noncontinuous approach leads to the correlation of gene expressions that
cannot be related by the continuous analysis facilitated by the pattern
analysis (as shown in the actual examples described in Section3). In the
“class-distribution” search, the genes can be searched for some of the defined
classes by being upregulated or downregulated, with respect to the basal value,
or by being disjointed, overexpressed, or underexpressed, with respect to some
of the other classes. All possible combinations are allowed where different
combinations will supply different gene sets.


### 2.6. Contextualisation Consulting the GEO Database

In order to
get complementary information from other public microarray data, the GEO
database could be consulted. These queries attempt to know if the genes
supplied by the “class-distribution” search tool (genes that follow the
user-defined class distribution in their expression) are gene markers in the
GEO datasets.  To achieve this
information, the NCR-PCOPGene queries the microarray “gene-centric” GEO Profiles
[1]. The profiles across all GEO
datasets where the query gene displays significant expression differences among
the GEO-predefined sample classes are obtained. In this way, the queried gene
can be considered, for example, as a marker of osmotic stress in a microarray
to analyse cellular stress response, or as a marker of metastasis in another
microarray to analyse disease states, and so forth.  These query-gene properties can then be
assigned to the sample classes obtained with the
NCR-PCOPGene.


### 2.7. Availability and Requirements

(1)
Project name: PCOPGene.(2)
Project home page:http://ibb.uab.es/revresearch/(3)
Operating system(s): Web-based application.(4)
Programming language: PHP, Java, flash script, CGI,
C++, Matlab (interfaces for Matlab users).(5)
Requirements: Mozilla 5.0, sea monkey 1.0, Firefox 1.0 or Explorer 6.0. flash plug-in 7.0 and Java 1.3.1 or higher
versions.(6)
Licence: free access; source codes available in the web.(7)
Any restrictions to use by nonacademics: we prefer no use for profit.A
demo user for visualising the above examples is available. A multimedia
tutorial is provided to describe the application use and it is indexed from the
application help icon. For mathematical and computational details, technical
reports are also available in the web.


## 2.1. Preprocess

When microarray data are uploaded,
the inner pattern of the expression relationship of all pairs of microarray
genes is calculated automatically. These calculations are made by the principal
curves of oriented points (PCOPs)
calculus [11] and recognise the linear, nonlinear
and mutually-excluding relationships between genes (both genes cannot be
overexpressed or underexpressed at the same time, or one gene can be
overexpressed if the other one is underexpressed) [12]. The noncorrelation factor between
each pair of gene expressions is calculated in the preprocess pattern analysis,
leading to the identification of those genes which are more correlated with
each gene. How it will lead to the “analysis expansion” around the researcher’s
genes of interest is shown in Results, and a brief description of the PCOP
calculation [11] is given in Section 2.4.


## 2.2. Defining Sample Classes

The researcher could test his/her
hypotheses and intuitions by defining the sample classes. The sample-classes
definition can be made in the following three different ways.(i)
Selecting the samples from a range of gene-expression data.(ii)
Clustering the samples from a gene-expression relationship: once the inner pattern among a set of
correlated genes of interest is calculated (once again by PCOP calculus [11]), the samples that constitute the
different “local behaviours” of this inner pattern can be clustered (and a high
correlation implies a better-fitted pattern). Then, the effect of these
conditions can be studied on any other genes relationship, where these genes
could not be correlated with the set initially used to cluster the microarray
experiments. Note that although in the preprocess PCOP is calculated for two
dimensions only (a pair of genes), now it is calculated for n≥2 dimensions (the n genes of the gene set).(iii)
Classifying the microarray experiments into different classes using
previous knowledge.


## 2.3. Colouring the Sample Classes in a Genes Relationship

Now, the
graphical interface facilitates the visualisation of the defined-classes effect
on gene relationships by colouring the samples (as is shown in Figure1) for
every set of genes of interest with the intention of studying their effect. The
study of this effect is especially relevant in the nonlinear relationships in
order to understand the biological sense of the slope change, possibly due to a
phenotype change.Sample class definitions using the PCOPGene web interface.Example 1: in the SGC(GUCY1B3) and Q6(QSN6) relationship ((a) f*=0.08), the samples of the two extremes of the SGC/Q6 inner pattern are clustered into two classes by clicking on the POPs, these classes are coloured in the GATA3 and AML1(RUNX1) relationship ((b) f=0.10). Example 2: in the GATA3 and AML1(RUNX1) comparison window (b), the samples of GATA3 over-expressed are grouped with the range-selection facility and coloured in the SGC and Q6 relationship (a). Example 3: in the GATA3, AML1(RUNX1), SGC and Q6 relationship ((c) f=0.28), the plot of the SGC and Q6 plane is shown. The coloured classes shown have been obtained by clustering the microarray experiments according to their correlation. These classes represent differentiated cell processes with different genes involved. (f* = non-correlation factor provided by the PCOP
calculus [10]).
(a)(b)(c)The class definitions  of types (i) (selecting the samples from a range of
gene-expression values) and (ii) (clustering the samples from a gene-expression
relationship) are made using these graphical visualisations of the
initial-genes-of-interest pattern analysis. Type (iii) is made using the web
interface that enquires for the samples belonging to each class, or directly
uploading the file.


## 2.4. Defining the Sample Classes from a Nonlinear Expression Relationship

The system makes use of the principal
curves of oriented points (PCOPs)
calculus to obtain the nonlinear inner pattern [11, 13] used to cluster the samples (remember
that PCOP calculus is also used to obtain the correlation degree between all
pairs of microarray genes). The analysed variables with the PCOP method can be
independent because the method uses a hidden variable for ordering the data (in
contrast to other nonlinear analyses like regression curves) which is suitable
for gene-expression comparisons [14]. PCOP is defined by the
generalisation, at the local level, of the principal-components variance
properties. From the sample-space data of n dimensions (one per gene expression), our system obtains discretised states
named “principal oriented points” (POPs), which represent this principal
component generalisation at the local level or local area. The series of principal
oriented points (POPs) obtained makes up the principal curve of oriented points
(PCOPs) or inner
pattern of the data cloud [11, 13]. The final series of POPs obtained
will minimise the dispersion degree of the samples around PCOP [11, 13].
As a result, even with no isotropic distribution of the data, we obtain
a very realistic inner pattern, compared, for example, with methods like least
squares, and a very accurate data-dispersion measurement (the noncorrelation
factor), compared with artificial intelligence methods [11, 15].Next, when the user selects the POPs
(the different discretised states obtained in the PCOP calculus) using the
graphical interface, the samples belonging to each POP scope or local area are
selected. One of the main keys of PCOP calculus is precisely the scope
detection of the local area of each POP, as has been explained in a previous
work [11]. In this way, the different sample
classes are finally defined based on the “local behaviours” of the gene-expression
relationship. For more details about this clustering method based on the
fluctuations of the inner pattern, consult our previous work  [10].


## 2.5. Gene Search Based on the Distribution of the Classes along the Gene-Expression Range

To make it
easier to relate the genes in a noncontinuous approach, the user can carry out
a gene search based on the expression level required for each class defined.
This noncontinuous approach leads to the correlation of gene expressions that
cannot be related by the continuous analysis facilitated by the pattern
analysis (as shown in the actual examples described in Section3). In the
“class-distribution” search, the genes can be searched for some of the defined
classes by being upregulated or downregulated, with respect to the basal value,
or by being disjointed, overexpressed, or underexpressed, with respect to some
of the other classes. All possible combinations are allowed where different
combinations will supply different gene sets.


## 2.6. Contextualisation Consulting the GEO Database

In order to
get complementary information from other public microarray data, the GEO
database could be consulted. These queries attempt to know if the genes
supplied by the “class-distribution” search tool (genes that follow the
user-defined class distribution in their expression) are gene markers in the
GEO datasets.  To achieve this
information, the NCR-PCOPGene queries the microarray “gene-centric” GEO Profiles
[1]. The profiles across all GEO
datasets where the query gene displays significant expression differences among
the GEO-predefined sample classes are obtained. In this way, the queried gene
can be considered, for example, as a marker of osmotic stress in a microarray
to analyse cellular stress response, or as a marker of metastasis in another
microarray to analyse disease states, and so forth.  These query-gene properties can then be
assigned to the sample classes obtained with the
NCR-PCOPGene.


## 2.7. Availability and Requirements

(1)
Project name: PCOPGene.(2)
Project home page:http://ibb.uab.es/revresearch/(3)
Operating system(s): Web-based application.(4)
Programming language: PHP, Java, flash script, CGI,
C++, Matlab (interfaces for Matlab users).(5)
Requirements: Mozilla 5.0, sea monkey 1.0, Firefox 1.0 or Explorer 6.0. flash plug-in 7.0 and Java 1.3.1 or higher
versions.(6)
Licence: free access; source codes available in the web.(7)
Any restrictions to use by nonacademics: we prefer no use for profit.A
demo user for visualising the above examples is available. A multimedia
tutorial is provided to describe the application use and it is indexed from the
application help icon. For mathematical and computational details, technical
reports are also available in the web.


## 3. Results

Let us describe some real-use cases
of the NCR-PCOPGene and the relevance of the new knowledge supplied (the
gene-expression dependence put forward in the examples are new and unknown,
until now). The three ways to define the sample classes and a basic-analysis
procedure have been used.


### 3.1. Microarray Data

The suitable data to be analysed by
the NCR-PCOPGene are microarrays with large synchronous or asynchronous sample
series. The analysis presented in the paper uses microarray data provided by
the National Cancer Institute (USA) [16]. They correspond to the profiles of
9703 cDNAS representing ~8000 unique genes of 60 cell-lines, in relation to the
activity profiles of 1400 drugs. They provide a resulting table of 1376 genes
and 118 compounds with the most representative substances and genes normalised
for the 60 cell-lines (suitable data for knowledge discovery using our tools).


### 3.2. Basic Analysis Procedure

The analysis begins in the
researcher’s genes of interest, usually gene markers of a specific disease,
cell state, or function. As was commented on in
Section2, the correlation factors between all
microarray pairs of genes (for the linearly, nonlinearly, and mutually
excluding relationships) are automatically calculated when the microarray data
are uploaded (the preprocess). Thus, for each gene the system provides its rank
of best-correlated genes and the user can expand the initial
correlated-gene-set based on these ranks [14]. And then finally, the user can
launch the PCOP calculus of the gene set.Once the pattern analysis of the
query genes has been performed, the graphical interface will show their
expression-relationship, inner pattern, and their fluctuations. Also, their
noncorrelation factor of the set is provided (remember that a better correlation
among the initial gene-set implies an inner pattern that fits better,
facilitating the detection of the fluctuations and slope changes, and also the
clustering of the different microarray experiments associated with each one).To find the expression dependence of
these initial sets of correlated genes with other genes either linearly or
nonlinearly correlated with the first ones (on the contrary, it is easier and
faster to perform PCOP analysis directly), the user should proceed as follows:
first, he/she must discretise the continuous relationship among the initial
gene set forming the different classes (by simply clicking on the POPs along
the relationship’s inner-pattern in the plot interface); second, applying the
“classes-distribution” search tool for a certain distribution of that discretisation,
the genes that follow the required distribution in their expressions are
obtained. Finally, the researcher can now perform the pattern analysis of the
genes provided and observe, in its interface visualisation, the effect of each
sample class on its expression relationship (with the samples of the classes
coloured, as in Figure1). This procedure will show the noncontinuous
dependence among the genes provided by the search and the initial gene set in
the manner specified in the search. If the distribution required in the search
is changed, the genes provided and their noncontinuous dependence, with respect
to the initial ones, will vary too.


### 3.3. Example 1: Defining the Sample Classes from a Nonlinear Genes Relationship

Let us now look at a real analysis.
We wish to relate the Soluble Guanylate Cyclase Beta1 3 (SGC) and Quiescin Q6(Q6)   genes (our genes of interest). The SGC is underexpressed in cellular
stress [17], whereas Q6 is overexpressed in the
last phases of tissue remodelling [18]. Additionally, we would like to
relate this pair of genes to GATA-binding protein 3 (GATA3) and acute myeloid leukaemia 1 (AML1,
RUNX1). GATA3 is involved in growth control and the maintenance of the
differentiated state in epithelial cells [19]. The impairment of the AML1 function deregulates the pathways
leading to cellular proliferation and differentiation  [20].
The two gene sets show a correlation between their respective members
(noncorrelation factors of SGC versus
Q6 =     0.08; GATA3 versus AML1  =   0.1) and their inner pattern can
be calculated and visualised in the graphical interfaces, as is shown in Figure 1. The problem is that these two sets of genes are neither linearly nor
nonlinearly correlated (noncorrelation factor for SGC versus  Q6 versus GATA3 versus AML1 =  0.28). But perhaps they are maintaining a noncontinuous
dependence in their expressions, and we cannot discern it with the analysis for
continuous data-clouds. However, we can try to find it using the classes’
definition.To perform this, two clusters are built from theSGC and Q6 relationship
by selecting the POPs located in the two extremes of their inner-pattern
relationship, one corresponding to the cellular-stress samples and the other to
the tissue-remodelling samples (Figure 1, SGC and Q6 relationship). These clusters
will constitute two different sample classes.Now, the classes are applied to theGATA3 and AML1 relationship, painting their
respective samples with red and blue colours (Figure 2). As can be observed in
Figure 2, almost all samples corresponding to cellular stress (red) appear with
an underexpression of GATA3 and AML1, indicating that the two genes are
not overexpressed in cellular stress. However, the tissue-remodelling-class
samples (blue) appear along the GATA3 and AML1 relationship as being over-
and underexpressed and indicate that some of these tissue-remodelling
conditions are affected by the GATA3 and AML1 differentiation ways while
some are not. This points out that the overexpression of GATA3 and AML1 implies an
overexpression of SGC and Q6, but not the opposite. Note that this
“unidirectional” relationship is impossible to detect by pattern- or
correlation-analysis methods.Figure 2
Example 1: effect of classes derived from Q6 and SGC on GATA3 and AML1 expression. In the picture is shown the GATA3 (x axis) and AML1 (y axis) relationship, where the data-cloud shows the  microarray experiments and the PCOP, connecting the POPs, describes the inner pattern of the expression relationship. The samples under-expressed in the Q6 and SGC relationship are coloured in red, and the samples over-expressed in the Q6 and SGC relationship are coloured in blue. This points out that the under-expression of Q6 and SGC  implies an under-expression of GATA3 and AML1, but that an over-expression of Q6 and SGC does not always imply an over-expression of  GATA3 and AML1. The data set represents the activity of 118 substances normalised for 60 cell-lines
[16].


### 3.4. Example 2: Defining the Sample Classes from Selecting Expression Ranges

As seen
above, there are some experiments involved in tissue remodelling, but not in
theGATA3 differentiation processes.
It would be interesting to study them. For this purpose, those samples where GATA3 is overexpressed were selected
(using the graphical interface) to define a new sample class
(Figure 1, GATA3 and AML1 relationship).This class of samples is coloured in theSGC and Q6 relationship, as shown in
Figure 3. As we can see, the differentiation induced by GATA3 is independent of the tissue-remodelling level achieved by
the SGC and Q6 relationship. Note that the sample selection from POP is the
appropriate method to classify cellular states because the cause or effect of a
cell state is the combined expression of multiple genes.Figure 3
Example 2: effect of over-expressed class from GATA3 and AML1 on SGC and Q6 expression: SGC (x axis) and   Q6 (y axis) relationship, where the data-cloud shows the  microarray experiments and the PCOP, connecting the POPs, describes the inner pattern of the expression relationship. The samples of the class representing the over-expression of GATA3 and AML1 are coloured. In this way, the four gene expressions are related, SGC and Q6 linearly, with GATA3 and AML1 maintaining a complex noncontinuous relationship with the other two. The data set represents the activity of 118 substances normalised for 60 cell-lines [16].


### 3.5. Example 3: Defining the Sample Classes by Classifying the Experiments Using Previous Knowledge

Previous
knowledge can arise basically from two different origins: a biological/clinical
origin or a statistical one.  In our case,
the microarray experiments are grouped by their linear correlation using principal
components, but other methods like biclustering [21, 22] or Locally Linear Embedding [23, 24] can be used with better accuracy to
define the classes. Note that in order to define the classes, the microarray
experiments play the variable role and the genes make up the data cloud. Defining the classes by the microarray-experiments correlation, sets
of genes are similarly expressed under the conditions of certain experiments (a
class), but not under the conditions of the others. Thus, we can establish the
hypothesis that each sample class represents a differentiated cell process,
with the genes implicated in it acting jointly.The effect
of the defined classes on relationships among genes of interest can now be
observed. Colouring these classes in the relationship among the four genes of
the above examples (Figure1, GATA3,
RUNX, SGC, and Q6 relationship),
and remembering the observations of the above examples, the genes that may act
jointly would be (i)
the genes involved in cell stress (yellow and red) and (ii)   the genes involved in tissue
remodelling (green and blue), this last set being divided into genes related to GATA3 and AML1 differentiation
(green), and those not implicated in it (blue).


### 3.6. Example 4: Gene Search Based on the Distribution of the Classes along Their Expression Range

With the
classes obtained in the last example (Figure1, GATA3, RUNX, SGC, and Q6 relationship) and using the “class-distribution” gene-search tool, it is
interesting to search the genes which mark the “transition” from the cell
process involved in tissue remodelling without GATA3 differentiation (blue) to the cell process linked to tissue
remodelling with GATA3 differentiation (green). For this purpose, the following “class-distribution”
gene search must be performed: the blue and green classes are overexpressed, with
respect to the basal value; the rest of the classes are underexpressed, with respect to the
basal value; and the green-class samples are more overexpressed than the blue-class
samples.Furthermore,
we can identify gene markers of specific cell processes or pathologies (in
relation to their expression levels) from the supplied genes by means of the
queries made by the application against the “gene-centric” GEO profiles. Thus,
the analysis can be focused on the relevant genes for biomedical user interest.


## 3.1. Microarray Data

The suitable data to be analysed by
the NCR-PCOPGene are microarrays with large synchronous or asynchronous sample
series. The analysis presented in the paper uses microarray data provided by
the National Cancer Institute (USA) [16]. They correspond to the profiles of
9703 cDNAS representing ~8000 unique genes of 60 cell-lines, in relation to the
activity profiles of 1400 drugs. They provide a resulting table of 1376 genes
and 118 compounds with the most representative substances and genes normalised
for the 60 cell-lines (suitable data for knowledge discovery using our tools).


## 3.2. Basic Analysis Procedure

The analysis begins in the
researcher’s genes of interest, usually gene markers of a specific disease,
cell state, or function. As was commented on in
Section2, the correlation factors between all
microarray pairs of genes (for the linearly, nonlinearly, and mutually
excluding relationships) are automatically calculated when the microarray data
are uploaded (the preprocess). Thus, for each gene the system provides its rank
of best-correlated genes and the user can expand the initial
correlated-gene-set based on these ranks [14]. And then finally, the user can
launch the PCOP calculus of the gene set.Once the pattern analysis of the
query genes has been performed, the graphical interface will show their
expression-relationship, inner pattern, and their fluctuations. Also, their
noncorrelation factor of the set is provided (remember that a better correlation
among the initial gene-set implies an inner pattern that fits better,
facilitating the detection of the fluctuations and slope changes, and also the
clustering of the different microarray experiments associated with each one).To find the expression dependence of
these initial sets of correlated genes with other genes either linearly or
nonlinearly correlated with the first ones (on the contrary, it is easier and
faster to perform PCOP analysis directly), the user should proceed as follows:
first, he/she must discretise the continuous relationship among the initial
gene set forming the different classes (by simply clicking on the POPs along
the relationship’s inner-pattern in the plot interface); second, applying the
“classes-distribution” search tool for a certain distribution of that discretisation,
the genes that follow the required distribution in their expressions are
obtained. Finally, the researcher can now perform the pattern analysis of the
genes provided and observe, in its interface visualisation, the effect of each
sample class on its expression relationship (with the samples of the classes
coloured, as in Figure1). This procedure will show the noncontinuous
dependence among the genes provided by the search and the initial gene set in
the manner specified in the search. If the distribution required in the search
is changed, the genes provided and their noncontinuous dependence, with respect
to the initial ones, will vary too.


## 3.3. Example 1: Defining the Sample Classes from a Nonlinear Genes Relationship

Let us now look at a real analysis.
We wish to relate the Soluble Guanylate Cyclase Beta1 3 (SGC) and Quiescin Q6(Q6)   genes (our genes of interest). The SGC is underexpressed in cellular
stress [17], whereas Q6 is overexpressed in the
last phases of tissue remodelling [18]. Additionally, we would like to
relate this pair of genes to GATA-binding protein 3 (GATA3) and acute myeloid leukaemia 1 (AML1,
RUNX1). GATA3 is involved in growth control and the maintenance of the
differentiated state in epithelial cells [19]. The impairment of the AML1 function deregulates the pathways
leading to cellular proliferation and differentiation  [20].
The two gene sets show a correlation between their respective members
(noncorrelation factors of SGC versus
Q6 =     0.08; GATA3 versus AML1  =   0.1) and their inner pattern can
be calculated and visualised in the graphical interfaces, as is shown in Figure 1. The problem is that these two sets of genes are neither linearly nor
nonlinearly correlated (noncorrelation factor for SGC versus  Q6 versus GATA3 versus AML1 =  0.28). But perhaps they are maintaining a noncontinuous
dependence in their expressions, and we cannot discern it with the analysis for
continuous data-clouds. However, we can try to find it using the classes’
definition.To perform this, two clusters are built from theSGC and Q6 relationship
by selecting the POPs located in the two extremes of their inner-pattern
relationship, one corresponding to the cellular-stress samples and the other to
the tissue-remodelling samples (Figure 1, SGC and Q6 relationship). These clusters
will constitute two different sample classes.Now, the classes are applied to theGATA3 and AML1 relationship, painting their
respective samples with red and blue colours (Figure 2). As can be observed in
Figure 2, almost all samples corresponding to cellular stress (red) appear with
an underexpression of GATA3 and AML1, indicating that the two genes are
not overexpressed in cellular stress. However, the tissue-remodelling-class
samples (blue) appear along the GATA3 and AML1 relationship as being over-
and underexpressed and indicate that some of these tissue-remodelling
conditions are affected by the GATA3 and AML1 differentiation ways while
some are not. This points out that the overexpression of GATA3 and AML1 implies an
overexpression of SGC and Q6, but not the opposite. Note that this
“unidirectional” relationship is impossible to detect by pattern- or
correlation-analysis methods.Figure 2
Example 1: effect of classes derived from Q6 and SGC on GATA3 and AML1 expression. In the picture is shown the GATA3 (x axis) and AML1 (y axis) relationship, where the data-cloud shows the  microarray experiments and the PCOP, connecting the POPs, describes the inner pattern of the expression relationship. The samples under-expressed in the Q6 and SGC relationship are coloured in red, and the samples over-expressed in the Q6 and SGC relationship are coloured in blue. This points out that the under-expression of Q6 and SGC  implies an under-expression of GATA3 and AML1, but that an over-expression of Q6 and SGC does not always imply an over-expression of  GATA3 and AML1. The data set represents the activity of 118 substances normalised for 60 cell-lines
[16].


## 3.4. Example 2: Defining the Sample Classes from Selecting Expression Ranges

As seen
above, there are some experiments involved in tissue remodelling, but not in
theGATA3 differentiation processes.
It would be interesting to study them. For this purpose, those samples where GATA3 is overexpressed were selected
(using the graphical interface) to define a new sample class
(Figure 1, GATA3 and AML1 relationship).This class of samples is coloured in theSGC and Q6 relationship, as shown in
Figure 3. As we can see, the differentiation induced by GATA3 is independent of the tissue-remodelling level achieved by
the SGC and Q6 relationship. Note that the sample selection from POP is the
appropriate method to classify cellular states because the cause or effect of a
cell state is the combined expression of multiple genes.Figure 3
Example 2: effect of over-expressed class from GATA3 and AML1 on SGC and Q6 expression: SGC (x axis) and   Q6 (y axis) relationship, where the data-cloud shows the  microarray experiments and the PCOP, connecting the POPs, describes the inner pattern of the expression relationship. The samples of the class representing the over-expression of GATA3 and AML1 are coloured. In this way, the four gene expressions are related, SGC and Q6 linearly, with GATA3 and AML1 maintaining a complex noncontinuous relationship with the other two. The data set represents the activity of 118 substances normalised for 60 cell-lines [16].


## 3.5. Example 3: Defining the Sample Classes by Classifying the Experiments Using Previous Knowledge

Previous
knowledge can arise basically from two different origins: a biological/clinical
origin or a statistical one.  In our case,
the microarray experiments are grouped by their linear correlation using principal
components, but other methods like biclustering [21, 22] or Locally Linear Embedding [23, 24] can be used with better accuracy to
define the classes. Note that in order to define the classes, the microarray
experiments play the variable role and the genes make up the data cloud. Defining the classes by the microarray-experiments correlation, sets
of genes are similarly expressed under the conditions of certain experiments (a
class), but not under the conditions of the others. Thus, we can establish the
hypothesis that each sample class represents a differentiated cell process,
with the genes implicated in it acting jointly.The effect
of the defined classes on relationships among genes of interest can now be
observed. Colouring these classes in the relationship among the four genes of
the above examples (Figure1, GATA3,
RUNX, SGC, and Q6 relationship),
and remembering the observations of the above examples, the genes that may act
jointly would be (i)
the genes involved in cell stress (yellow and red) and (ii)   the genes involved in tissue
remodelling (green and blue), this last set being divided into genes related to GATA3 and AML1 differentiation
(green), and those not implicated in it (blue).


## 3.6. Example 4: Gene Search Based on the Distribution of the Classes along Their Expression Range

With the
classes obtained in the last example (Figure1, GATA3, RUNX, SGC, and Q6 relationship) and using the “class-distribution” gene-search tool, it is
interesting to search the genes which mark the “transition” from the cell
process involved in tissue remodelling without GATA3 differentiation (blue) to the cell process linked to tissue
remodelling with GATA3 differentiation (green). For this purpose, the following “class-distribution”
gene search must be performed: the blue and green classes are overexpressed, with
respect to the basal value; the rest of the classes are underexpressed, with respect to the
basal value; and the green-class samples are more overexpressed than the blue-class
samples.Furthermore,
we can identify gene markers of specific cell processes or pathologies (in
relation to their expression levels) from the supplied genes by means of the
queries made by the application against the “gene-centric” GEO profiles. Thus,
the analysis can be focused on the relevant genes for biomedical user interest.


## 4. Conclusions

NCR-PCOPGene
strength  resides in(a) the flexibility of the classes’ definition, due to the
nonlinear pattern analysis of gene expressions and the sample clustering along
the inner pattern, combined with (b) the high-throughput approach of microarray technology, which, by means of the
“class-distribution” gene search and the gene-correlation table, leads the
researcher to expand his/her analysis. As a result, our application can help to
relate gene expressions when their relationships are noncontinuous and cannot
be found using linear or nonlinear analytical methods.The
flexibility of the tool leads to the combination of the three ways for
determining the classes shown in this paper, to define and redefine the classes.
For example, using the “class definition
from a nonlinear genes relationship” way, the classes can be clustered from
two different, uncorrelated, initial gene sets (as long as no sample appears in
more than one class). Therefore, the user could search the genes that are
partially related to both sets of genes in a specific manner (e.g.,
being correlated with one set in the underexpression, but with the other in the
overexpression analyses). Or initially using the “previous-knowledge” way, the user redefines the predefined classes by their effect on the
gene-expression-relationships (performing subclasses from the original ones to
study subprocesses in which the genes of interest are involved).In summary,
it is a powerful tool to study genes of interest and test researchers’
hypotheses by taking advantage of the high-throughput capability of microarray
technology.



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*Source: 789026-2008-12-10.xml*