Heat maps
of gene expression values show how experimental conditions influenced
production (expression) of mRNA for a set of genes. Green indicates
reduced expression. Cluster analysis has placed a group of down regulated genes in the upper left corner.
In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression)
of thousands of genes at once, to create a global picture of cellular
function. These profiles can, for example, distinguish between cells
that are actively dividing, or show how the cells react to a particular
treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell.
Several transcriptomics technologies can be used to generate the necessary data to analyse. DNA microarrays measure the relative activity of previously identified target genes. Sequence based techniques, like RNA-Seq, provide information on the sequences of genes in addition to their expression level.
Background
Expression profiling is a logical next step after sequencing a genome:
the sequence tells us what the cell could possibly do, while the
expression profile tells us what it is actually doing at a point in
time. Genes contain the instructions for making messenger RNA (mRNA),
but at any moment each cell makes mRNA from only a fraction of the
genes it carries. If a gene is used to produce mRNA, it is considered
"on", otherwise "off". Many factors determine whether a gene is on or
off, such as the time of day, whether or not the cell is actively
dividing, its local environment, and chemical signals from other cells.
For instance, skin cells, liver
cells and nerve cells turn on (express) somewhat different genes and
that is in large part what makes them different. Therefore, an
expression profile allows one to deduce a cell's type, state,
environment, and so forth.
Expression profiling experiments often involve measuring the
relative amount of mRNA expressed in two or more experimental
conditions. This is because altered levels of a specific sequence of
mRNA suggest a changed need for the protein coded by the mRNA, perhaps
indicating a homeostatic response or a pathological condition. For example, higher levels of mRNA coding for alcohol dehydrogenase
suggest that the cells or tissues under study are responding to
increased levels of ethanol in their environment. Similarly, if breast
cancer cells express higher levels of mRNA associated with a particular transmembrane receptor
than normal cells do, it might be that this receptor plays a role in
breast cancer. A drug that interferes with this receptor may prevent or
treat breast cancer. In developing a drug, one may perform gene
expression profiling experiments to help assess the drug's toxicity,
perhaps by looking for changing levels in the expression of cytochrome P450 genes, which may be a biomarker of drug metabolism. Gene expression profiling may become an important diagnostic test.
Comparison to proteomics
The
human genome contains on the order of 25,000 genes which work in
concert to produce on the order of 1,000,000 distinct proteins. This is
due to alternative splicing, and also because cells make important changes to proteins through posttranslational modification
after they first construct them, so a given gene serves as the basis
for many possible versions of a particular protein. In any case, a
single mass spectrometry experiment can identify about
2,000 proteins or 0.2% of the total. While knowledge of the precise proteins a cell makes (proteomics)
is more relevant than knowing how much messenger RNA is made from each
gene, gene expression profiling provides the most global picture
possible in a single experiment. However, proteomics methodology is
improving. In other species, such as yeast, it is possible to identify
over 4,000 proteins in just over one hour.
Use in hypothesis generation and testing
Sometimes, a scientist already has an idea of what is going on, a hypothesis,
and he or she performs an expression profiling experiment with the idea
of potentially disproving this hypothesis. In other words, the
scientist is making a specific prediction about levels of expression
that could turn out to be false.
More commonly, expression profiling takes place before enough is
known about how genes interact with experimental conditions for a
testable hypothesis to exist. With no hypothesis, there is nothing to
disprove, but expression profiling can help to identify a candidate
hypothesis for future experiments. Most early expression profiling
experiments, and many current ones, have this form
which is known as class discovery. A popular approach to class
discovery involves grouping similar genes or samples together using one
of the many existing clustering methods such the traditional k-means or hierarchical clustering, or the more recent MCL.
Apart from selecting a clustering algorithm, user usually has to choose
an appropriate proximity measure (distance or similarity) between data
objects.
The figure above represents the output of a two dimensional cluster, in
which similar samples (rows, above) and similar gene probes (columns)
were organized so that they would lie close together. The simplest form
of class discovery would be to list all the genes that changed by more
than a certain amount between two experimental conditions.
Class prediction is more difficult than class discovery, but it
allows one to answer questions of direct clinical significance such as,
given this profile, what is the probability that this patient will
respond to this drug? This requires many examples of profiles that
responded and did not respond, as well as cross-validation techniques to discriminate between them.
Limitations
In
general, expression profiling studies report those genes that showed
statistically significant differences under changed experimental
conditions. This is typically a small fraction of the genome for
several reasons. First, different cells and tissues express a subset of
genes as a direct consequence of cellular differentiation
so many genes are turned off. Second, many of the genes code for
proteins that are required for survival in very specific amounts so many
genes do not change. Third, cells use many other mechanisms to regulate
proteins in addition to altering the amount of mRNA,
so these genes may stay consistently expressed even when protein
concentrations are rising and falling. Fourth, financial constraints
limit expression profiling experiments to a small number of observations
of the same gene under identical conditions, reducing the statistical power
of the experiment, making it impossible for the experiment to identify
important but subtle changes. Finally, it takes a great amount of effort
to discuss the biological significance of each regulated gene, so
scientists often limit their discussion to a subset. Newer microarray analysis techniques
automate certain aspects of attaching biological significance to
expression profiling results, but this remains a very difficult problem.
The relatively short length of gene lists published from
expression profiling experiments limits the extent to which experiments
performed in different laboratories appear to agree. Placing expression
profiling results in a publicly accessible microarray database
makes it possible for researchers to assess expression patterns beyond
the scope of published results, perhaps identifying similarity with
their own work.
Validation of high throughput measurements
Both DNA microarrays and quantitative PCR exploit the preferential binding or "base pairing"
of complementary nucleic acid sequences, and both are used in gene
expression profiling, often in a serial fashion. While high throughput
DNA microarrays lack the quantitative accuracy of qPCR, it takes about
the same time to measure the gene expression of a few dozen genes via
qPCR as it would to measure an entire genome using DNA microarrays. So
it often makes sense to perform semi-quantitative DNA microarray
analysis experiments to identify candidate genes, then perform qPCR on
some of the most interesting candidate genes to validate the microarray
results. Other experiments, such as a Western blot
of some of the protein products of differentially expressed genes, make
conclusions based on the expression profile more persuasive, since the
mRNA levels do not necessarily correlate to the amount of expressed
protein.
Statistical analysis
Data analysis of microarrays has become an area of intense research.
Simply stating that a group of genes were regulated by at least
twofold, once a common practice, lacks a solid statistical footing. With
five or fewer replicates in each group, typical for microarrays, a
single outlier
observation can create an apparent difference greater than two-fold. In
addition, arbitrarily setting the bar at two-fold is not biologically
sound, as it eliminates from consideration many genes with obvious
biological significance.
Rather than identify differentially expressed genes using a fold change cutoff, one can use a variety of statistical tests or omnibus tests such as ANOVA, all of which consider both fold change and variability to create a p-value,
an estimate of how often we would observe the data by chance alone.
Applying p-values to microarrays is complicated by the large number of multiple comparisons
(genes) involved. For example, a p-value of 0.05 is typically thought
to indicate significance, since it estimates a 5% probability of
observing the data by chance. But with 10,000 genes on a microarray, 500
genes would be identified as significant at p < 0.05 even if there
were no difference between the experimental groups. One obvious solution
is to consider significant only those genes meeting a much more
stringent p value criterion, e.g., one could perform a Bonferroni correction on the p-values, or use a false discovery rate
calculation to adjust p-values in proportion to the number of parallel
tests involved. Unfortunately, these approaches may reduce the number of
significant genes to zero, even when genes are in fact differentially
expressed. Current statistics such as Rank products
aim to strike a balance between false discovery of genes due to chance
variation and non-discovery of differentially expressed genes. Commonly
cited methods include the Significance Analysis of Microarrays (SAM) and a wide variety of methods are available from Bioconductor and a variety of analysis packages from bioinformatics companies.
Selecting a different test usually identifies a different list of significant genes
since each test operates under a specific set of assumptions, and
places a different emphasis on certain features in the data. Many tests
begin with the assumption of a normal distribution
in the data, because that seems like a sensible starting point and
often produces results that appear more significant. Some tests consider
the joint distribution of all gene observations to estimate general variability in measurements, while others look at each gene in isolation. Many modern microarray analysis techniques involve bootstrapping (statistics), machine learning or Monte Carlo methods.
As the number of replicate measurements in a microarray
experiment increases, various statistical approaches yield increasingly
similar results, but lack of concordance between different statistical
methods makes array results appear less trustworthy. The MAQC Project
makes recommendations to guide researchers in selecting more standard
methods (e.g. using p-value and fold-change together for selecting the
differentially expressed genes) so that experiments performed in
different laboratories will agree better.
Different from the analysis on differentially expressed
individual genes, another type of analysis focuses on differential
expression or perturbation of pre-defined gene sets and is called gene
set analysis. Gene set analysis demonstrated several major advantages over individual gene differential expression analysis.
Gene sets are groups of genes that are functionally related according
to current knowledge. Therefore, gene set analysis is considered a
knowledge based analysis approach. Commonly used gene sets include those derived from KEGG pathways, Gene Ontology
terms, gene groups that share some other functional annotations, such
as common transcriptional regulators etc. Representative gene set
analysis methods include Gene Set Enrichment Analysis (GSEA),
which estimates significance of gene sets based on permutation of
sample labels, and Generally Applicable Gene-set Enrichment (GAGE), which tests the significance of gene sets based on permutation of gene labels or a parametric distribution.
Gene annotation
While
the statistics may identify which gene products change under
experimental conditions, making biological sense of expression profiling
rests on knowing which protein each gene product makes and what
function this protein performs. Gene annotation provides functional and
other information, for example the location of each gene within a
particular chromosome. Some functional annotations are more reliable
than others; some are absent. Gene annotation databases change
regularly, and various databases refer to the same protein by different
names, reflecting a changing understanding of protein function. Use of
standardized gene nomenclature helps address the naming aspect of the problem, but exact matching of transcripts to genes remains an important consideration.
Categorizing regulated genes
Having
identified some set of regulated genes, the next step in expression
profiling involves looking for patterns within the regulated set. Do the
proteins made from these genes perform similar functions? Are they
chemically similar? Do they reside in similar parts of the cell? Gene ontology
analysis provides a standard way to define these relationships. Gene
ontologies start with very broad categories, e.g., "metabolic process"
and break them down into smaller categories, e.g., "carbohydrate
metabolic process" and finally into quite restrictive categories like
"inositol and derivative phosphorylation".
Genes have other attributes beside biological function, chemical
properties and cellular location. One can compose sets of genes based on
proximity to other genes, association with a disease, and relationships
with drugs or toxins. The Molecular Signatures Database and the Comparative Toxicogenomics Database are examples of resources to categorize genes in numerous ways.
Finding patterns among regulated genes
Ingenuity Gene Network Diagram
which dynamically assembles genes with known relationships. Green
indicates reduced expression, red indicates increased expression. The
algorithm has included unregulated genes, white, to improve
connectivity.
Regulated genes are categorized in terms of what they are and what they do, important relationships between genes may emerge.
For example, we might see evidence that a certain gene creates a
protein to make an enzyme that activates a protein to turn on a second
gene on our list. This second gene may be a transcription factor
that regulates yet another gene from our list. Observing these links we
may begin to suspect that they represent much more than chance
associations in the results, and that they are all on our list because
of an underlying biological process. On the other hand, it could be that
if one selected genes at random, one might find many that seem to have
something in common. In this sense, we need rigorous statistical
procedures to test whether the emerging biological themes is significant
or not. That is where gene set analysis comes in.
Cause and effect relationships
Fairly
straightforward statistics provide estimates of whether associations
between genes on lists are greater than what one would expect by chance.
These statistics are interesting, even if they represent a substantial
oversimplification of what is really going on. Here is an example.
Suppose there are 10,000 genes in an experiment, only 50 (0.5%) of which
play a known role in making cholesterol.
The experiment identifies 200 regulated genes. Of those, 40 (20%) turn
out to be on a list of cholesterol genes as well. Based on the overall
prevalence of the cholesterol genes (0.5%) one expects an average of 1
cholesterol gene for every 200 regulated genes, that is, 0.005 times
200. This expectation is an average, so one expects to see more than one
some of the time. The question becomes how often we would see 40
instead of 1 due to pure chance.
According to the hypergeometric distribution,
one would expect to try about 10^57 times (10 followed by 56 zeroes)
before picking 39 or more of the cholesterol genes from a pool of 10,000
by drawing 200 genes at random. Whether one pays much attention to how
infinitesimally small the probability of observing this by chance is,
one would conclude that the regulated gene list is enriched in genes with a known cholesterol association.
One might further hypothesize that the experimental treatment
regulates cholesterol, because the treatment seems to selectively
regulate genes associated with cholesterol. While this may be true,
there are a number of reasons why making this a firm conclusion based on
enrichment alone represents an unwarranted leap of faith. One
previously mentioned issue has to do with the observation that gene
regulation may have no direct impact on protein regulation: even if the
proteins coded for by these genes do nothing other than make
cholesterol, showing that their mRNA is altered does not directly tell
us what is happening at the protein level. It is quite possible that the
amount of these cholesterol-related proteins remains constant under the
experimental conditions. Second, even if protein levels do change,
perhaps there is always enough of them around to make cholesterol as
fast as it can be possibly made, that is, another protein, not on our
list, is the rate determining step
in the process of making cholesterol. Finally, proteins typically play
many roles, so these genes may be regulated not because of their shared
association with making cholesterol but because of a shared role in a
completely independent process.
Bearing the foregoing caveats in mind, while gene profiles do not
in themselves prove causal relationships between treatments and
biological effects, they do offer unique biological insights that would
often be very difficult to arrive at in other ways.
Using patterns to find regulated genes
As
described above, one can identify significantly regulated genes first
and then find patterns by comparing the list of significant genes to
sets of genes known to share certain associations. One can also work the
problem in reverse order. Here is a very simple example. Suppose there
are 40 genes associated with a known process, for example, a
predisposition to diabetes. Looking at two groups of expression
profiles, one for mice fed a high carbohydrate diet and one for mice fed
a low carbohydrate diet, one observes that all 40 diabetes genes are
expressed at a higher level in the high carbohydrate group than the low
carbohydrate group. Regardless of whether any of these genes would have
made it to a list of significantly altered genes, observing all 40 up,
and none down appears unlikely to be the result of pure chance: flipping
40 heads in a row is predicted to occur about one time in a trillion
attempts using a fair coin.
For a type of cell, the group of genes whose combined expression
pattern is uniquely characteristic to a given condition constitutes the gene signature
of this condition. Ideally, the gene signature can be used to select a
group of patients at a specific state of a disease with accuracy that
facilitates selection of treatments.
Gene Set Enrichment Analysis (GSEA) and similar methods
take advantage of this kind of logic but uses more sophisticated
statistics, because component genes in real processes display more
complex behavior than simply moving up or down as a group, and the
amount the genes move up and down is meaningful, not just the direction.
In any case, these statistics measure how different the behavior of
some small set of genes is compared to genes not in that small set.
GSEA uses a Kolmogorov Smirnov
style statistic to see whether any previously defined gene sets
exhibited unusual behavior in the current expression profile. This leads
to a multiple hypothesis testing challenge, but reasonable methods
exist to address it.
Conclusions
Expression
profiling provides new information about what genes do under various
conditions. Overall, microarray technology produces reliable expression
profiles.
From this information one can generate new hypotheses about biology or
test existing ones. However, the size and complexity of these
experiments often results in a wide variety of possible interpretations.
In many cases, analyzing expression profiling results takes far more
effort than performing the initial experiments.
Most researchers use multiple statistical methods and exploratory
data analysis before publishing their expression profiling results,
coordinating their efforts with a bioinformatician or other expert in DNA microarrays.
Good experimental design, adequate biological replication and follow up
experiments play key roles in successful expression profiling
experiments.
