Improved Statistical Test for
Data Integrating ANOVA with
This research was done at The Rockefeller
University and supervised by Brian Kirk2
1. 180 East End Ave, New York, NY, 10128. Email: [email protected] Phone: 212249-0134
2. Email: [email protected] Phone: 212-327-7064
Microarrays are exciting new biological instruments. Microarrays promise to have
important applications in many fields of biological research, but they are currently fairly
inaccurate instruments and this inaccuracy increases their expense and reduces their usefulness.
This research introduces a new technique for analyzing data from some types of microarray
experiments that promises to produce more accurate results. Essentially, the method works by
identifying patterns in the genes.
Motivation: Statistical significance analysis of microarray data is currently a pressing
problem. A multitude of statistical techniques have been proposed, and in the case of simple two
condition experiments these techniques work well, but in the case of multiple condition
experiments there is additional information that none of these techniques take into account. This
information is the shape of the expression vector for each gene, i.e., the ordered set of expression
measurements, and its usefulness lies in the fact that genes that are actually affected biologically
by some experimental circumstance tend to fall into a relatively small number of clusters. Genes
that do appear to fall into these clusters should be selected for by the significance test, those that
do not should be selected against.
Results: Such a test was successfully designed and tested using a large number of
artificially generated data sets. Where the above assumption of the correlation between
clustering and significance is true, this test gives considerably better performance than
conventional measures. Where the assumption is not entirely true, the test is robust to the
Microarrays are part of an exciting new class of biotechnologies that promise to allow
monitoring of genome-wide expression levels (1). Although the technique presented in this
paper is quite general and would, in principle, work with other types of data, it has been
developed for microarray data. Microarray experiments work by testing expression levels of
some set of genes in organisms exposed to at least two different conditions, and usually then
trying to determine which (if any) of these genes are actually changing in response to the change
in experimental condition (2). Unfortunately, the random variation in microarrays is often large
relative to the changes trying to be detected. This makes it very hard to eliminate false positives,
genes whose random fluctuations make them erroneously seem to respond to the experimental
condition, and false negatives, genes whose random fluctuations mask the fact that they are
actually responding to the experimental condition. It is standard statistical practice to handle this
problem by using replicates, i.e., several microarrays run in identical settings (1-9). Although
replicates are highly effective, the expense of additional replicates makes it worthwhile to
minimize the number needed by using more effective statistical tests.
When there are only two conditions in an experiment, the conventional statistical test for
differential expression is the t-test (2,6). But Long, et. al. (5, also 6,7) realized that by running a
separate t-test on each gene, valuable information is lost, namely the fact that the actual
population standard deviation of all the genes is rather similar. Due to the small number of
replicates in microarray experiments there are usually a few genes that, by chance, appear to
have extremely low standard deviations and thus register even a very small change as highly
significant by a conventional t-test. In reality -- and as additional replicates would confirm -- the
standard deviations of these genes are much higher, and the change is not at all significant. In
response to this issue, Long et. al. derived using Bayesian statistics a method of transforming
standard deviations that, in essence, moves outlying standard deviations closer to the mean
standard deviation. Unfortunately, the resulting algorithm, which they call Cyber-T, works only
with two condition experiments. Since this research is concerned with multiple condition
experiments, it was necessary to expand the method to work with this type of experiment, the
standard statistical test for which is called ANOVA (ANalysis Of Variations) (8). Here we report
the successful extension of the Cyber-T algorithm to multiple condition experiments, creating the
algorithm we call Cyber-ANOVA.
In microarray experiments involving several conditions, the expression vector of each
gene formed by consecutive measurements of its expression level has a distinctive shape, which
contains useful information. The more conditions in the experiment (and thus numbers in the
expression vector), the more distinctive will be the shape. Often biologists are interested in
finding genes with expression vectors of similar shape, and this interest has generated a welldeveloped process for grouping genes by shape known as clustering (9). The basis of clustering
techniques is the concept of correlation, the similarity between two vectors. We chose to use the
Pearson Correlation, because this measure generally captures more accurately the actual
biological idea of correlation (2). With the Pearson correlation, as elsewhere, distance is defined
as one minus the correlation and measures the dissimilarity between two vectors. Using this
measure of distance, it is possible to solve the problem of taking a large number of unorganized
vectors and placing them into clusters of similar shapes: this is known as K-means clustering
(12). Once the K-means algorithm has been completed, it is often useful to determine how well
each gene has clustered. This is done by computing a representative vector, often called a center,
for each cluster and calculating the distance between each gene and the center of that gene’s
The fundamental thesis of this research is that genes actually affected by experimental
circumstances tend to fall into a relatively small number of clusters, and that this information can
be used to make a more accurate statistical test. While it is in principle possible, it is not often
the case that large numbers of affected genes behave in seemingly random and entirely dissimilar
patterns. Given all the clusters of clearly significant genes, genes that fit well in a cluster are
more likely to be differentially expressed than those that do not. Unfortunately, this hypothesis is
very difficult to test, for no microarray experiments have been replicated enough to allow for
accurate determination of differentially expressed genes. The studies that have come closest to
this conclusion are Zhao et al, (10) and Jiang et al., (11). Both studies ran K-Means clustering on
multiple condition data and found several distinct cluster shapes that accounted for all clearly
We used this connection between clustering and significance to create a more accurate
statistical test whose essential steps are the following. First Cyber-ANOVA is run on the entire
dataset and the highly significant genes are clustered. The other genes are placed into the cluster
in which they fit best, and the distance between each gene and the center of its cluster is
computed. There are now two values for each gene, the P value (probability) from the Cyber4
ANOVA test and the D value (distance) from the clustering. These two values are combined and
the resulting value is more accurate than either one alone.
The potential advantage of this algorithm was validated by the creation and successful
testing of a large number of artificial data sets. Artificial data was chosen over real experimental
data for two main reasons: (1) in experimental data, uncertainty always exists over which genes
are actually differentially expressed; this presents major problems in determining how well some
given algorithm works; and (2) no single microarray experiment is actually representative of all
possible microarray experiments – to determine accurately the performance advantage of an
algorithm, a large number of very different experiments must be used, a prohibitively expensive
and difficult process. Artificial creation of data sets allows every parameter of the data to be
perfectly controlled and the algorithm’s performance can be tested on every possible
combination of parameters.
The basic method of analysis was to generate an artificial data set from a number of
parameters, run the clustering algorithm on the data and measure its performance, run CyberANOVA on that data and on similar data but with additional replicates and measure its
performance, and then compare the results. The performance gain of the clustering algorithm is
probably best expressed as equivalent replicate gain: the number of additional replicates that
would be needed to produce the results of the clustering algorithm using a standard CyberANOVA. On reasonable simulated data, we found that the benefit was approximately one
additional replicate added to an original two. With additional improvements to the algorithm
design, this saving could probably be increased still further, and the advantage is clearly
significant enough to warrant use in actual microarray experiments.
The algorithm is implemented as a large (>2000 lines) macro script in Microsoft Visual
Basic for Applications controlling Microsoft Excel. The essential steps of the algorithm are the
1. Run Cyber-ANOVA on all genes;
2. Adjust P values for multiple testing;
3. Use the results of (2) to choose an appropriate number of most significant genes;
4. Cluster this selection of genes using K-Means;
5. Take the remaining genes and group each into the cluster with which it correlates best;
6. Compute the distance between each gene and the center of its cluster;
7. Compute a combined measurement of significance for each gene.
1. Run Cyber-ANOVA on all genes. Cyber-ANOVA is just a simple extension of Cyber-T, but its
novelty causes it to warrant some discussion. Cyber-T takes as input a list of genes, means in
each condition, standard deviations in each condition, and number of replicates. It gives as
output a set of P-values much like those from a conventional t-test, but more accurate. The
improvement is based on the idea of regularizing the standard deviations, essentially moving
outlying standard deviations closer to the mean. At the same time, it recognizes that standard
deviation may change significantly (and inversely) with intensity level, and thus genes are
regularized only with genes of similar intensity. The algorithm works by first computing for
each gene a background standard deviation, the average standard deviation of the hundred genes
with closest intensity, and then combining the background standard deviation with the observed
standard deviation in a type of weighted average. A standard t-test is then run, but using the
regularized standard deviation instead of the observed one.
Cyber-ANOVA works by the same method, taking as input means and standard
deviations in any number of conditions. Each background standard deviation is calculated using
the intensities of the genes in their own condition, and the values are combined using the same
formula. But because there are more than two conditions, a t-test cannot be applied; instead we
use ANOVA, inputting the regularized standard deviation instead of the observed one. The result
gives a dramatic increase in accuracy, an equivalent replicate gain of roughly one.
2. Adjust P values for multiple testing. To run the clustering algorithm, we need to select
a number of genes that are highly significant to form the seed clusters around which all other
genes will cluster. The difficult step in the process is deciding how many genes to select: it is
critical to get at least a few seeds for all clusters but also critical not to have too many false
positives. Since the first requirement is impossible to determine by calculation, we use the
second. In order to choose an appropriate number of expected false positives, we must have an
accurate estimate of the false positive rate. The simplest way of estimating the false positive rate
is a Bonferroni correction, which simply multiplies each p value by the total number of tests.
This method assumes that all genes are independent, and tends to be too conservative,
particularly with small numbers of replicates, and a number of more accurate techniques have
been proposed. However, for this step, only a rough estimate is necessary, and thus the
Bonferroni correction suffices.
3. Use the results of (2) to choose an appropriate number of most significant genes. Once
the expected false positive rate for any number of the most significant genes has been estimated,
some number of genes must be selected such that the number of expected false positives is some
percentage of the number selected genes. There is no particular correct way to define this
percentage; it depends on how significant the genes in the least significant cluster are, which
cannot be determined directly. However, we have found that the algorithm is tolerant to
deviations from optimality (Table 6).
4. Cluster this selection of genes using K-Means. The chosen group of most significant
genes is clustered using the K-Means algorithm with the Pearson correlation. The primary
problem with all K-means clustering algorithms is that the number of clusters must be declared
at the start (13). But like the determination of the number of genes to include in the initial
clustering, there is no problem as long as K is rather high. This can be explained conceptually by
considering what happens as K increases to differentially expressed and non-differentially
expressed genes. If there are only differentially expressed genes in the original set, then as K is
raised, genes that were originally placed in one cluster will separate into multiple clusters.
Fortunately, this is guaranteed by the process of K-Means to lower the D values and create better
cluster shapes. If there are non-differentially expressed genes, then as K is increased these genes
will tend to separate out from the differentially expressed genes and form their own clusters – a
highly desirable occurrence. Unfortunately, if K becomes very large, the noise genes may start
to cluster very well with other noise genes forming erroneously low distances, and the other
noise genes added in the next step will have a better chance of incidentally finding a cluster with
which they correlate well. In principle, too large a K might be partially monitored by checking
for very small clusters and for clusters with very high average distances, both of which could be
removed and their genes forced to join a different cluster. These methods have not yet been
5. Take the remaining genes and group each into the cluster with which it correlates best.
The clusters formed in step 4 act as seeds for the remaining genes. The clusters formed
optimally include all of the cluster shapes of actually differentially expressed genes and no
others. It was possible to form these clusters, even if they were less than ideal, by using only a
subset of the genes in the data set. Now it is possible to look at the remaining genes and to see
how well each belongs in one of the clusters. Ideally, each differentially expressed gene would
correlate perfectly with exactly one cluster and each noise gene would correlate poorly with all
of them. In practice, of course, this is far from the case, but the principle remains. Since each
gene is, of course, only expected to correlate well with one cluster, each gene in the rest of the
dataset is added to the cluster with which it correlates best.
6. Compute the distance between each gene and the center of its cluster. The
measurement of how well each gene belongs in its cluster is now determined by computing the
Pearson correlation between each gene and the center of its cluster. The resulting correlations
are converted to distances (distance = 1- correlation). These distance values are the key
numbers produced by the first half of the algorithm. They represent whether a gene vector’s
shape is similar to the shape of gene vectors known to be significant. The basis of this research
is that low distances tend to imply differentially expressed genes. Unfortunately, distance values
alone are not an accurate measure of significance – ranking genes by distance alone produces
results far worse that the original Cyber-ANOVA. Instead, the distance (“D”) values must be
combined with the original P values to give new values better than either alone.
7. Compute a combined measurement of significance for each gene. The goal now is to
take the P value and the D value and combine them in some way to make a new value. Just like
with the calculation of the previous probabilities, what we are really interested in is finding the
probability of a non-differentially expressed gene with a certain standard deviation attaining both
a P value less than or equal to the observed P value and a D value less than or equal to the
observed D value. Unfortunately, the theoretical basis for the connection between P and D
values is not well defined, and it may be very complicated and experiment-dependent; there may
well be no useful analytic solution to this problem. Instead, we generate the distribution of P and
D values for each experiment. First thousands of non-differentially expressed genes are
generated using the standard deviation that would be expected at their intensity level and their P
and D values are calculated. Then, for each gene in the data set, the number of non-differentially
expressed genes that have both P and D values equal to or lower than the P and D values of that
gene is counted. That number is divided by the number of non-differentially expressed genes
generated, and that quotient gives an approximate probability.
It is important to note that for highly significant genes, this method does not give accurate
probabilities, for if 105 non-differentially expressed genes are generated, only genes with an
actual probability of occurring of about 10-5 are likely to be generated. Thus, genes in the data
set that are actually significant to a probability of below 10-5 will almost certainly not have any
generated genes more significant and will all be assigned probabilities of zero. But this is not a
major concern, since these genes are so significant that there is little question that they are
differentially expressed, and their precise rank is not likely to be important. If it is, then the
genes with zero combined values can be ranked internally by their P values. In this case, within
the very most significant genes, the algorithm will not have had any effect.
Generation of the Data
To test the performance of the algorithm, a number of artificial data sets were generated.
The goal was to see how much benefit could be derived from the algorithm and how different
parameter combinations would impact the results. Above all, the data is intended to mimic real
microarray data, at least in all respects that are likely to significantly influence the results of the
test. The basic process of data generation was to use certain parameters to create a mean value
and standard deviation for each gene and condition and to use a random number generator
assuming a normal distribution to generate plausible experimental values. Some of the most
important parameters are the numbers of differentially and non-differentially expressed genes,
number of groups, cluster shapes, standard deviations, and fold changes.
For each data set, about 8500 genes were created, with the number of differentially
expressed genes ranging from about 100 to about 500. The rest were genes whose mean value
did not change across conditions, but that mean value did vary between different nondifferentially expressed genes. The number of groups used is a key determinant of the
performance of the algorithm, more groups causing better performance. Most of the data sets
generated used six groups, but a set with twenty groups was tried.
The cluster shapes of non-differentially expressed genes are, of course, flat lines, for that
is the definition of being non-differentially expressed. The cluster shapes of the differentially
expressed genes were more complicated. They are drawn loosely from Zhao et. al. (10) but
really the specific shapes chosen are not all that important. An important property of the Pearson
correlation is that, given some expression vector, the probability of some other expression vector
having a distance to the original vector below some value is the same regardless of the shape of
the original vector; it depends only on the number of conditions (8). On the other hand, given
two original vectors, the probability of a test vector having a distance below some value to either
one of them does depend on the shapes of the original vectors. As intuition suggests, vectors of
opposite shape (like a linear increasing vector and linear decreasing vector) make it more likely
for a test vector to cluster well with one of them. To the extent that cluster shape does affect the
algorithm, the more similar the cluster shapes, the better the algorithm will perform. Cluster
shapes can be graphed and assigned names based on their shape. Data was generated using up to
twelve cluster shapes (Figure 1a, 1b).
Figure 1A. Six of the Twelve Cluster Shapes
Log Expression Level
Figure 1B. The Other Six Cluster Shapes
Log Expression Level
The method of assigning standard deviations was chosen to be as realistic as possible.
Cyber-ANOVA takes into account the fact that standard deviation often changes significantly
with absolute intensity in microarray experiments, and the generated data model this
phenomenon. The data of Long, et. al, (5) which is freely available, was graphed, mean intensity
vs. standard deviation, and a quadratic regression calculated. This regression equation is an
explicit function for standard deviation in terms of mean, and it was used with a modification to
calculate standard deviations for the generated data. The modification is required because it is
not the case that standard deviation depends solely on intensity. The mean intensity vs. standard
deviation graph does not make a perfect line; there is considerable width to that curve, and this
too was simulated. The standard deviation itself was randomized with a small meta-standard
deviation. The assumption of normality in this generation is probably false, but the change in the
algorithm performance caused by introducing the meta-randomization at all is so small that it
seems highly unlikely that a better model would produce a significant difference.
To take full advantage of the standard deviation dependence on mean, non-differentially
expressed genes were generated with means separated by hundredths between 8 and 16 (on a log2
scale). This created a considerable range of standard deviations for each data set. The means of
differentially expressed genes were generated starting from 8 different baselines, the integers
from 8 to 15.
Each differentially expressed gene was generated at five different fold changes: 1.5, 1.7,
2.0, 3.0, and 6.0 on an unlogged scale. The six and three fold change genes generally made up
the seed clustering group, and the 1.5 and 1.7 fold change genes were responsible for most of the
difference in performance between algorithms.
Once data has been generated and assigned P values using Cyber-ANOVA and combined
values using the clustering algorithm, there must be some way to compare the performance. We
introduce a new method for determining the performance of an algorithm, which we believe to
fix a shortcoming in the standard method.
The most common method of scoring appears to be a consistency test (1-7) which works
the following way. Say there is a data set with N genes known to be differentially expressed.
First, some significance test is performed and each gene assigned a significance level. Next, the
genes are ranked by significance level, most significant first. Out of the top N most significant
genes, the number D of genes that are actually differentially expressed is determined, and the
score is D / N.
The problem with the consistency test is that ignores the fact that it often will matter
whether the (N-D) genes are listed consecutively right after the Nth gene or at the very bottom of
the list. Biologists would prefer that the differentially expressed genes be listed as close to the
top as possible, for it is easier to find an interesting gene or group of genes if it is higher on the
As a solution, we introduce an algorithm called rank sum. Rank sum also takes a list of
genes ranked by significance level, but computes the score differently. Rank sum is equal to the
sum of the ranks of all the N differentially expressed genes minus (1+2+3+…N), the minimum
score possible. Lower rank sums indicate better performance, and a perfect significance test
gives a rank sum of zero.
But rank sum has significant flaws, too, for it gives too much weight to the genes placed
after the Nth rank. In the worst case, one single misplaced gene could change the score from 0 to
the number of genes in the experiment, 8000 in this case. Here is a successful modification.
Instead of summing the actual ranks, we sum for all differentially expressed genes F(Rank),
where F is some increasing concave-down function. There is no particular right choice for F, for
the preferred function will vary depending on the experiment and experimenter, but both
logarithmic and polynomial (to a power less than, say, 1/2) functions give almost identical
The modified rank sum successfully determines how well a significance test has ranked
the genes. But the score from the rank sum itself is not particularly useful information; it must
be placed in a context. Since the goal of any significance test is ultimately to reduce the number
of replicates necessary to attain accurate results, we convert the rank sum to a new measure that
we call equivalent replicate gain. Equivalent replicate gain is defined as the number of replicates
needed to reach the same rank sum using only a standard Cyber-ANOVA. Specifically, this is
done by running Cyber-ANOVA on several data sets with parameters all identical except for the
number of replicates. The graph of number of replicates vs. rank sum is drawn and a regression
calculated. The clustering algorithm is then run and a rank sum obtained. That rank sum is
entered into the inverse of the regression function to find the approximate number of replicates
required to attain the same performance using only Cyber-ANOVA. The equivalent replicate
gain is the difference between that number of replicates and the number of replicates actually
used. Another useful measure is the equivalent replicate gain percentage, the equivalent replicate
gain divided by the equivalent replicate number. This gives an approximation of the percent cost
saving possible by using the algorithm and fewer replicates. The combination of the techniques
of rank sum and equivalent replicate gain percentage has been very successful.
We have generated a number of artificial data sets using a wide range of parameters and
found the equivalent replicate gain percentage of the algorithm in many situations. The most
important parameters were found be the number of conditions, the number of cluster shapes, the
number of replicates, the number of non-differentiated genes in the initial clustering, and the
number of clusters omitted from the initial clustering.
In absolutely ideal situations, the algorithm can give an enormous benefit. An
experiment with twenty conditions, one cluster shape, two replicates, and perfect initial
clustering approaches such an ideal situation and gives excellent results (Table 1) .
Number of Conditions
Table 1. Performance of the clustering algorithm on data with two numbers of conditions, one
cluster shape, two replicates, and perfect initial clustering.
A more realistic case would involve fewer conditions, say, six, and particularly with this
smaller number of conditions, the number of cluster shapes becomes a factor in the performance
of the algorithm. The performance benefit is still quite significant: recall that equivalent
replicate gain percentage is an approximation of the cost saving due to fewer replicates. As
expected, the performance benefit is reduced by additional cluster shapes (Table 2).
Number of Cluster
Table 2. Performance of the clustering algorithm on data with two numbers of cluster shapes,
using six conditions, two replicates, and perfect initial clustering.
Even when identical parameters are used to generate the data, the rank sum of any
algorithm will fluctuate between data sets differing only by random number generation.
Fortunately, this variation appears to affect Cyber-ANOVA and the Clustering algorithm equally,
for the equivalent replicate gain percentage does not change significantly (Table 3). This
invariance has the beneficial effect of making it unnecessary to run multiple data sets for each
parameter combination; more than one adds little information.
Table 3. Performance of the clustering algorithm on data generated randomly four times using
identical parameters: six conditions, two replicates, two cluster shapes, and perfect initial
Change in the standard deviation or in the fold changes of the genes will certainly affect
both the rank sum from both Cyber-ANOVA and clustering. Over small changes, the equivalent
replicate gain percentage is not seriously affected, but if the standard deviation becomes so large
as to corrupt the initial clustering, the performance loss is significant (Table 4).
(in multiples of the
normal SD used
Table 4. Performance of the clustering algorithm on data with three levels of standard deviation,
using six conditions, two replicates, two cluster shapes, and perfect initial clustering.
Like Cyber-ANOVA, and in fact all statistical tests, the equivalent replicate gain
percentage of the algorithm decreases as the number of replicates becomes larger and the test
becomes more accurate (Table 5). If the initial clustering is correct, there is no loss in
performance, but in experiments with parameters similar to those of Table 5, the algorithm
probably ceases to be worthwhile after four replicates.
Number of Replicates
Table 5. Performance of the clustering algorithm on data with three numbers of replicates, using
six conditions, twelve cluster shapes, and perfect initial clustering. The rate of decrease in
performance at higher number of replicates is compared with that rate in Cyber-ANOVA. Note
that for the clustering algorithm, the equivalent replicate gain percentage is expressed as a gain
from Cyber-ANOVA, but that for Cyber-ANOVA, the equivalent replicate gain percentage is
expressed as a gain from a standard ANOVA test. Thus, on the scale that Cyber-ANOVA is being
compared to, the clustering algorithm result for a two replicate data set is equivalent to the plain
ANOVA of four replicates.
The most serious impact to the algorithm’s performance occurs if the initial clustering is
done incorrectly. The most difficult and important step is choosing the number of genes to
include in the initial group; if either too many or too few are chosen, the performance of the
algorithm will be diminished significantly. The ultimate goal is to choose a set of genes that
includes all the clusters of differentially expressed genes without including any non-differentially
expressed genes. It is true that this might be very difficult to do with experimental data, but it is
fortunately true that the algorithm is robust to small departures from optimality (Table 6). The
only time that the algorithm’s performance drops below the performance of Cyber-ANOVA is
when almost all of the clusters are left out.
Genes Selected Number of
Table 6. Performance of the clustering algorithm on data with several expected false positive
rates for the initial clustering, using six conditions, two replicates, and twelve cluster shapes.
The potential cost saving of the algorithm in cases when the correlation between distance
and significance is strong is highly significant. It is interesting to note that for normal data with
two replicates such as that in Table 5, the percent equivalent replicate gain between CyberANOVA and Clustering is roughly equal to the performance gain between standard ANOVA and
Cyber-ANOVA, on the order of 30%.
It is true that this performance degrades in some cases, but this is also true of CyberANOVA, and also of other algorithms (3, 4). One situation that causes the percentage equivalent
replicate gain of all these algorithms to decrease is higher number of replicates. Unfortunately,
the performance decreases more rapidly in the clustering algorithm. This phenomenon is
perhaps best explained by a quality of information argument: additional replicates increase the
quality of information of the P values, but have little effect on the quality of the information of
the D values. Additional replicates will cause differentially expressed genes to cluster better, in a
sense reducing the number of false negatives, but will not reduce the false positives, because
non-differentially expressed genes are just as likely to randomly have low distances given any
number of replicates. Thus, as the number of replicates increases, the D values gradually cause
The most major degradation of the clustering algorithm’s performance, however, is in a
situation that does not affect the other algorithms, that of bad initial clustering. Reducing the
severity of this problem will be the primary direction for further research on this algorithm.
Some possible solutions include (1) clustering all the genes but weighing their significance in the
clustering algorithm by P value; and (2) setting the number of genes to use as seeds by trying
many values and seeing the point above which new clusters stop appearing to form. Regardless
of whether this issue can be resolved in the general case, if in some experiment it is strongly
suspected (perhaps on biological grounds) that no clusters have been omitted from the initial set
of genes, this clustering method can be used and safely expected to produce a substantial
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