Microarray Data Mining: Facing the Challenges
Gregory Piatetsky-Shapiro
KDnuggets and U. Mass Lowell
gregory
at
kdnuggets.com
Pablo Tamayo
MIT / Broad Institute
tamayo
at
broad.mit.edu
1. MOLECULAR BIOLOGY AND DNA
All organisms on Earth, except for viruses, consist of cells. Yeast,
for example, has one cell, while humans have trillions of cells. All
cells have a nucleus, and inside nucleus there is DNA, which
encodes the “program” for making future organisms. DNA has
coding and non-coding segments, and coding segments, called
“genes”, specify the structure of proteins, which are large
molecules, like hemoglobin, that do the essential work in every
organism. Practically all cells in the same organism have the same
genes, but these genes can be expressed differently at different
times and under different conditions. Genes make proteins in two
steps. First, DNA is transcribed into messenger RNA or mRNA,
which in turn is translated into proteins. The different patterns of
gene expression following carefully tuned biological programs,
according to tissue type, developmental stage, environment and
genetic background account for the huge variety of different cells
states and types. Virtually all major differences in cell state or
type are correlated with changes in the mRNA levels of many
genes.
2. MICROARRAYS: AN OVERVIEW
In recent years there has been an explosion in the rate of
acquisition of biomedical data. Advances in molecular genetics
technologies, such as DNA microarrays [1-8] allow us for the first
time to obtain a "global" view of the cell. For example, we can
now routinely investigate the biological molecular state of a cell
measuring the simultaneous expression of tens of thousands of
genes using DNA microarrays.
Different types of microarray use different technologies for
measuring mRNA expression levels; detailed description of these
technologies is beyond the scope of this paper. Here we will focus
on the analysis of data from Affymetrix arrays, which are
currently one of the most popular commercial arrays. However,
the methodology for analysis of data from other arrays would be
similar, but would use different technology-specific data
preparation and cleaning steps.
Figure 1: Affymetrix GeneChip® (right),
its grid (center) and a cell in a grid (left).
This type of microarray is a silicon chip that can measure the
expression levels of thousands of genes simultaneously. This is
done by hybridizing a complex mixture of mRNAs (derived from
tissue or cells) to microarrays that display probes for different
genes tiled in a grid-like fashion. Hybridization events are
detected using a fluorescent dye and a scanner that can detect
fluorescence intensities. The scanners and associated software
perform various forms of image analysis to measure and report
raw gene expression values. This allows for a quantitative readout
of gene expression on a gene-by-gene basis. As of 2003, there are
one-chip microarrays that measure expression of over 30,000
genes, covering most of the human genome.
Microarrays have opened the possibility of creating data sets of
molecular information to represent many systems of biological or
clinical interest. Gene expression profiles can be used as inputs to
large-scale data analysis, for example, to serve as fingerprints to
build more accurate molecular classification, to discover hidden
taxonomies or to increase our understanding of normal and
disease states.
The first generation of microarray analysis methodologies
developed over the last 5 years has demonstrated that expression
data can be used in a variety of class discovery or class prediction
biomedical problems including those relevant to tumor
classification [10-14]. Machine learning and statistical techniques
applied to gene expression data have been used to address the
questions of distinguishing tumor morphology, predicting post-
treatment outcome, and finding molecular markers for disease.
Today the microarray-based classification of different
morphologies, lineages and cell histologies can be performed
successfully in many instances. The performance in predicting
treatment outcome or drug response has been more limited but
some of the results are quite promising. Most results of
microarray analysis still require further experimental validation
and follow up study. Many current efforts are being directed in
this direction. In a few cases the results of microarray analysis
have found their way into more serious consideration in clinical
Figure 2: An example raw microarray image for a single
sample (image courtesy of Affymetrix). The intensity of
image on the left is translated by microarray software
into numbers like the ones on the right.
Gene
Value
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D26068_at
144
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use such as being part of clinical trials (e.g. the use of an
outcome-specific, computationally selected gene marker such as
PKC beta and associated inhibitor for Lymphoma treatment, [9]).
3. CHALLENGES FOR MICROARRAY
DATA MINING
Analysis of microarrays presents a number of unique challenges
for data mining. Typical data mining applications in domains like
banking or web, have a large number of records (thousands and
sometimes millions), while the number of fields is much smaller
(at most several hundred). In contrast, a typical microarray data
analysis study may have only a small number of records (less than
a hundred), while the number of fields, corresponding to the
number of genes, is typically in thousands. Given the difficulty of
collecting microarray samples, the number of samples is likely to
remain small in many interesting cases.
However, having so many fields relative to so few samples,
creates a high likelihood of finding “false positives” that are due
to chance – both in finding differentially expressed genes, and in
building predictive models. We need especially robust methods to
validate the models and assess their likelihood.
The main types of data analysis needed to for biomedical
applications include:
•
Gene Selection – in data mining terms this is a process of
attribute selection, which finds the genes most strongly
related to a particular class (see for example [15-21]).
•
Classification – classifying diseases or predicting outcomes
based on gene expression patterns, and perhaps even
identifying the best treatment for given genetic signature (see
for example [27-37]).
•
Clustering – finding new biological classes or refining
existing ones (see for example [22-26]).
Most papers in this special issue focus on these three topics, with
some of the papers spanning several topics. Two papers use a
number of techniques for biological discovery, and one paper
presents a survey of approaches for applying machine learning to
low-level analysis of microarray data.
3.1 Gene Selection
Attempts to find invariant or differential molecular behavior
relevant to a given biological problem are also limited by the fact
that in many cases little is known about the normal biological
variation expected in a given tissue or biological state. To
complicate matters a biological state is often defined only along
very coarse-grained phenotypic lines. This issue of normal
variation is directly addressed by V. Nadimpally and M. Zaki,
where they analyze genes which show normal variance across
genetically identical mice to characterize a set that could be used
to identify false positives in differential gene expression studies,
but also to provide insights into the processes underlying natural
variation. Their analysis applied to six mouse tissues resulted in
several genes which showed significant biological variations even
among identical mice and provides a valuable compendium of
normal variation in gene expression for mouse models.
Another approach to determining variability in small samples is
taken by S. Mukherjee, P. Sykacek, S. Roberts, and S. Gurr, who
propose a gene-ranking algorithm using bootstrapped P-values.
This approach is especially beneficial for taking into account
small-sample variability in observed values of the test statistic.
They show that this method outperforms widely used two-sample
T-test on artificial data and apply the method to two real datasets.
Most of the current gene selection methods in use today evaluate
each gene in isolation and ignore the gene to gene correlations.
From a biological perspective, however, we know that groups of
genes working together as pathway components and reflecting the
states of the cell are the real atomic units, or features, by which we
might be more likely to predict the character or type of a
particular sample and its corresponding biological state. It is these
patterns of coherent gene expression that must form the input data
on which sophisticated computational methods should operate
(see for example [38-45]). In this context B. Hanczar, M.
Courtine, A. Bennis, C. Hennegar, K. Clement, and J. Zucker
propose to increase the accuracy of microarray classification by
selecting appropriate “prototype” genes that represent a group of
genes that share a profile and better represent the phenotypic class
of interest. They present interesting results of the advantages of
using prototype-based feature selection to classify
adenocarcinomas.
3.2 Classification
Because the microarray dataset has many more features than
records, the common statistical and machine learning procedures
such as global feature selection can lead to false discoveries due
to random chance. R. Simon highlights some of the common
errors in identifying informative features and developing accurate
classifiers, and shows the correct approach.
M. O’Connell presents a review of methods available in Insightful
S+ArrayAnalyzer, which cover the full spectrum of microarray
data analysis, including data preprocessing, experimental design,
quality control, gene selection and differential expression
analysis, classification, and clustering. (Note: S+ArrayAnalyzer
includes much of the Bioconductor functionality
(
www.bioconductor.org
), in addition to methods developed at
Insightful.)
E. Bair and R. Tibshirani present a “nearest shrunken centroid”
method that has been successfully used to detect clinically
relevant differences in cancer patients. This method has been
shown to be effective in dealing with noise inherent in
microarrays and is implemented in their P AM system, available
for researchers.
S. Dudoit, M. van der Laan, S. Keles, A. Molinaro, S. Sinisi, and
S. Teng propose a unified loss-based methodology for estimator
construction, selection, and performance assessment with cross-
validation. They present new theoretical results that show that
cross-validation selection can be used in intensive searches of
large parameter spaces, even in finite sample situations. They also
present a new D/S/A algorithm for classification, which makes
Delete/Substitute/Add moves to find the best gene sets that
minimize estimation error.
Other challenges to the deployment of molecular classification
models come from the significant technical challenge in dealing
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with the variability due to the use of different technologies,
platforms and heterogeneous sources of material. One would
expect that different datasets representing the same biological
system will display some amount of “invariant” biological
characteristics independent of the idiosyncrasies or details of the
sample sources, the preparation procedures and the technological
platforms used to obtain the data. These invariant biological
characteristics, when properly captured and exposed, can provide
the basis to build more robust, general and accurate classification
models. These models hopefully will be based more on
reproducible biological behavior and less on biases, idiosyncrasies
and technological details. The method of B. Y. M. Fung and V. T.
Y. Ng to classify heterogeneous factors based on IFs (impact
factors) addresses this problem. The IFs provide a way to measure
the variations between individual classes in train and test samples
and can be integrated into standard classifiers such as Weighted
Voting or k-NN resulting in a significantly improvement in the
accuracy for classifying heterogeneous samples.
3.3 Clustering and Visualization
One important clustering task is to identify groups of co-
expressed genes recognize coherent expression patterns. However,
the interpretation of co-expressed genes and coherent patterns
strongly depends on the domain knowledge, which makes it
difficult to fully automate. D. Jiang, J. Pei, and A. Zhang present
an approach to this problem, based on interactive exploration of
gene expression patterns. They develop a novel tool, called
“coherent pattern index graph”, which gives users visual feedback
of strength and existence of coherent patterns.
Another aspect of clustering is finding gene networks and gene
interactions. X. Wu, Y. Ye, and L. Zhang propose a graphical
model based interaction analysis for this purpose. They apply
graphical Gaussian model to discover pairwise gene interactions
and use loglinear model to discover multi-gene interactions.
Scientific results are generally improved if we can look at the
same events from different perspectives. There are many sources
of biological knowledge that could be integrated into analysis of
gene expression. P. Glenisson and J. Mathys explore how to
combine expression data and literature extracted information to
reveal biologically meaningful clusters that are not found from
microarray data alone. This is an example of integrative genomics
[46-47].
3.4 Biological Discovery
One of the main goals of microarray data analysis is discovery of
biological knowledge, such as metabolic pathways. H.
Mamitsuka, Y. Okuno, and A. Yamaguchi present an approach
that builds a Markov model using the graph structure of a known
pathway, and then estimates parameters using the microarray data.
A different approach is taken by M. Curran, H. Liu, F. Long, and
N. Ge, who study methods for relating gene expression pattern to
the pattern of transcription factor binding sites (TFBS). They use
a linear model to fit the microarray data and identify potential up-
regulated genes based on a specific biological hypothesis.
Potential TFBS are then retrieved for the identified positive genes
and randomly selected controls. Then, after removing similar
binding sites, logistic regression is used to choose the best model
to predict gene type (positive, control) based on the TFBS
predictors.
3.5 Low-level analysis
The improvement of many analytical solutions to molecular
classification problems is also conditional to the development of
better and more powerful data analysis techniques, not only in
high-level, but also in low level analysis. In low level analysis the
focus is in providing better readouts, i.e. biological parameter and
molecular probe estimates that are more accurate and faithfully
reflect the actual biological state under study. B. Rubinstein, J.
McAuliffe, S. Cawley, M. Palaniswami, K. Ramamohanarao, and
T. Speed advocate the use of more sophisticated machine learning
approaches particularly in low level analysis. An example of a low
level analysis problem they addressed is the expression level
summarization problem: given a probe set’s intensities, after
background correction and normalization, estimate the amount of
target transcript present in the biological sample. They pointed out
the potential of semi-supervised, heterogeneous and incremental
learning for common microarray analysis settings such as those
with partially labeled datasets, data from disparate domains, and
sequential datasets. They identify the applicability of these
machine learning approaches in other more general problems such
as mass spectrometry, and fluorescence activated cell sorting.
4. Summary
Microarrays are a revolutionary new technology with great
potential to provide accurate medical diagnostics, help find the
right treatment and cure for many diseases and provide a detailed
genome-wide molecular portrait of cellular states. The papers
included in this issue are a good sample of second generation
methodologies and techniques that are being used or under
development today. As it can be seen from the results they are
very promising and extend the possibilities of applying
computational analysis and data mining to aid research in biology
and medicine. We would like to close this introduction with a
brief discussion to emphasize the large potential payoff of these
analytical efforts but also pointing out the huge challenges ahead.
There is little doubt about the potential of computational and
statistical analysis of molecular probes to improve the
understanding of the cell and the possibilities of molecular
medicine, Finding new insights into the molecular basis of
biological processes and searching for new drugs and treatments
is a problem of high complexity and where the techniques of
molecular biology has been applied for many decades. The
process is analogous to a large search of a few molecular entities,
connections or relationships in a large sea of possibilities. One
important goal of current and future computational analysis
methods --short of reverse engineering the entire cell circuitry,
which by the way it is still an intractable problem-- should be to
reduce that search and help expose the most promising candidates
(gene, proteins, drugs etc.) for further study. Current methods
already succeed to some extent in exposing relationships and
correlations and provide new hypothesis to be tested. Better
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accuracy, more robust models and estimators are clearly
welcomed but sooner of later the biological interpretation of the
computational or statistical results, e.g. gene selection, clustering,
prediction, has to be done. For this reason one of the main
challenges, besides finding relevant molecular features or building
successful empirical models, is to reveal the biological
mechanisms that are responsible for their success. Typically a
computational researcher will apply his or her favorite algorithm
to some microarray dataset and quickly obtain a voluminous set of
results. These results are likely to be useful but only if they can be
put in context and followed up with more detailed studies for
example by a biologist or a clinical researcher. Often this follow
up and interpretation is not done carefully enough because of the
additional significant research involvement, the lack of domain
expertise or proper collaborators, or due to the limitations of the
computational analysis itself. This last is an important point as
many approaches, despite being successful, left open the question
regarding what the significant features or patterns mean from a
biological perspective. Extracting knowledge from discovered
patterns is a serious scientific bottleneck and a desirable goal of
the next generation of molecular pattern recognition and data
mining methods should be to provide a more integrated (e.g.
along the lines of integrative genomics) and unified framework
that not only builds models but also aids in the interpretation and
understanding of them.
We hope that this special issue on Microarray Data Mining will
make more researchers interested in the field and its challenges
and will be a contribution towards realizing the potential of
microarrays for biology and medicine.
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