Model order selection for biomolecular data clustering
 Alberto Bertoni^{1} and
 Giorgio Valentini^{1}Email author
https://doi.org/10.1186/147121058S2S7
© Bertoni and Valentini; licensee BioMed Central Ltd. 2007
Published: 03 May 2007
Abstract
Background
Cluster analysis has been widely applied for investigating structure in biomolecular data. A drawback of most clustering algorithms is that they cannot automatically detect the "natural" number of clusters underlying the data, and in many cases we have no enough "a priori" biological knowledge to evaluate both the number of clusters as well as their validity. Recently several methods based on the concept of stability have been proposed to estimate the "optimal" number of clusters, but despite their successful application to the analysis of complex biomolecular data, the assessment of the statistical significance of the discovered clustering solutions and the detection of multiple structures simultaneously present in highdimensional biomolecular data are still major problems.
Results
We propose a stability method based on randomized maps that exploits the highdimensionality and relatively low cardinality that characterize biomolecular data, by selecting subsets of randomized linear combinations of the input variables, and by using stability indices based on the overall distribution of similarity measures between multiple pairs of clusterings performed on the randomly projected data. A χ^{2}based statistical test is proposed to assess the significance of the clustering solutions and to detect significant and if possible multilevel structures simultaneously present in the data (e.g. hierarchical structures).
Conclusion
The experimental results show that our model order selection methods are competitive with other stateoftheart stability based algorithms and are able to detect multiple levels of structure underlying both synthetic and gene expression data.
Background
Unsupervised clustering algorithms play a crucial role in the exploration and identification of structures underlying complex biomolecular data, ranging from transcriptomics to proteomics and functional genomics [1–4].
Unfortunately, clustering algorithms may find structure in the data, even when no structure is present instead. Moreover, even if we choose an appropriate clustering algorithm for the given data, we need to assess the reliability of the discovered clusters, and to solve the model order selection problem, that is the proper selection of the "natural" number of clusters underlying the data [5, 6]. From a machine learning standpoint, this is an intrinsically "illposed" problem, since in unsupervised learning we lack an external objective criterion, that is we have not an equivalent of a priori known class label as in supervised learning, and hence the evaluation of the reliability of the discovered classes becomes elusive and difficult. From a biological standpoint, in many cases we have no sufficient biological knowledge to "a priori" evaluate both the number of clusters (e.g. the number of biologically distinct tumor classes), as well as the validity of the discovered clusters (e.g. the reliability of new discovered tumor classes) [7].
To deal with these problems, several methods for assessing the validity of the discovered clusters and to test the existence of biologically meaningful clusters have been proposed (see [8] for a review).
Recently, several methods based on the concept of stability have been proposed to estimate the " optimal" number of clusters in complex biomolecular data [9–11]. In this conceptual framework multiple clusterings are obtained by introducing perturbations into the original data, and a clustering is considered reliable if it is approximately maintained across multiple perturbations.
Different procedures have been introduced to randomly perturb the data, ranging from bootstrapping techniques [9, 12, 13], to noise injection into the data [14] or random projections into lower dimensional subspaces [15, 16].
In particular, Smolkin and Gosh [17] applied an unsupervised version of the random subspace method [18] to estimate the stability of clustering solutions. By this approach, subsets of features are randomly selected multiple times, and clusterings obtained on the corresponding projected subspaces are compared with the clustering obtained in the original space to assess its stability. Even if this approach gives useful information about the reliability of highdimensional clusterings, we showed that random subspace projections may induce large distortions in gene expression data, thus obscuring their real structure [15]. Moreover, a major problem with data perturbations obtained through random projections from a higher to a lower dimensional space is the choice of the dimension of the projected subspace.
In this paper we extend the Smolkin and Gosh approach to more general randomized maps from higher to lowerdimensional subspaces, in order to reduce the distortion induced by random projections. Moreover, we introduce a principled method based on the Johnson and Lindenstrauss lemma [19] to properly choose the dimension of the projected subspace. Our proposed stability indices are related to those proposed by BenHur et al. [13]: their stability measures are obtained from the distribution of similarity measures across multiple pairs of clustered data perturbed through resampling techniques. In this work we propose stability indices that depend on the distribution of the similarity measures between pairs of clusterings, but data perturbation is realized through random projections to lower dimensional subspaces, in order to exploit the highdimensionality of biomolecular data.
Another major problem related to stabilitybased methods is to estimate the statistical significance of the structures discovered by clustering algorithms. To face this problem we propose a χ^{2}based statistical test that may be applied to any stability method based on the distribution of similarity measures between pairs of clusterings. We experimentally show that by this approach we may discover multiple structures simultaneously present in the data (e.g. hierarchical structures), associating a pvalue to the clusterings selected by a given stabilitybased method for model order selection.
Methods
In this section we present our approach to stabilitybased model order selection, considering randomized maps with bounded distortion to perturb the data, stability indices based on the distribution of the clustering similarity measures, and finally we present our χ^{2}based test for assessing the significance of the clustering solutions.
Data perturbations using randomized maps with bounded distortions
A major requirement for clustering algorithms is the reproducibility of their solutions with other data sets drawn from the same source; this is particularly true with biomolecular data, where the robustness of the solutions is of paramount importance in biomedical applications. From this standpoint the reliability of a clustering solution is tied to its stability: we may consider reliable a cluster if it is stable, that is if it is maintained across multiple data sets drawn from the same source. In real cases, however, we may dispose only of limited data, and hence we need to introduce multiple " small" perturbations into the original data to simulate multiple "similar" samples from the same underlying unknown distribution. By applying appropriate indices based on similarity measures between clusterings we can then estimate the stability and hence the reliability of the clustering solutions.
We propose to perturb the original data using random projections μ : ℝ^{ d }→ ℝ^{ d' }from high ddimensional spaces to lower d'dimensional subspaces. A related approach is presented in [17], where the authors proposed to perturb the data randomly choosing a subset of the original features (random subspace projection [18]); the authors did not propose any principled method to choose the dimension of the projected subspace, but a key problem consists in finding a d' such that for every pair of data p, q ∈ ℝ^{ d }, the distances between the projections μ(p) and μ(q) are approximately preserved with high probability. A natural measure of the approximation is the distortion dist_{ μ }:
If dist_{ μ }(p, q) = 1, the distances are preserved; if 1  ε ≤ dist_{ μ }(p, q) ≤ 1 + ε, we say that an εdistortion level is introduced.
In [15] we experimentally showed that random subspace projections used in [17] may introduce large distortions into gene expression data, thus introducing bias into stability indices based on this kind of random projections. For these reasons we propose to apply randomized maps with guaranteed low distortions, according to the JohnsonLindenstrauss (JL) lemma [19], that we restate in the following way: Given a ddimensional data set D = {p_{1}, p_{2},...,p_{ n }} ⊂ ℝ^{ d }and a distortion level ε, randomly choosing a d'dimensional subspace S ⊂ ℝ^{ d }, with d' = c logn/ε^{2}, where c is a suitable constant, with high probability (say ≥ 0.95) the random projection μ :ℝ^{ d }→ S verifies 1  ε ≤ dist_{ μ }(p_{ i }, p_{ j }) ≤ 1 + ε for all p_{ i }≠ p_{ j }.
In practice, using randomized maps that obey the JL lemma, we may perturb the data introducing only bounded distortions, approximately preserving the metric structure of the original data [15]. Note that the dimension of the projected subspace depends only on the cardinality of the original data and the desired εdistortion, and not from the dimension d of the original space.
The embedding exhibited in [19] consists in projections from ℝ^{ d }in random d'dimensional subspaces. Similar results may be obtained by using simpler maps [20, 21], represented through random d' × d matrices $R=1/\sqrt{{d}^{\prime}}({r}_{ij})$, where r_{ ij }are random variables such that:
E[r_{ ij }] = 0, Var[r_{ ij }] = 1
 1.
Bernoulli random projections: represented by d' × d matrices $R=1/\sqrt{{d}^{\prime}}({r}_{ij})$, where r_{ ij }are uniformly chosen in {1, 1}, such that Prob(r_{ ij }= 1) = Prob(r_{ ij }= 1) = 1/2 (that is the r_{ ij }are Bernoulli random variables). In this case the JL lemma holds with c ≃ 4.
 2.
Achlioptas random projections [20]: represented by d' × d matrices $P=1/\sqrt{{d}^{\prime}}({r}_{ij})$, where r_{ ij }are chosen in {$\sqrt{3}$, 0, $\sqrt{3}$}, such that Prob(r_{ ij }= 0) = 2/3, Prob(r_{ ij }= $\sqrt{3}$) = Prob(r_{ ij }= $\sqrt{3}$) = 1/6. In this case also we have E[r_{ ij }] = 0 and Var[r_{ ij }] = 1 and the JL lemma holds.
 3.
Normal random projections [21, 22]: this JL lemma compliant randomized map is represented by a d' × d matrix $R=1/\sqrt{{d}^{\prime}}({r}_{ij})$, where r_{ ij }are distributed according to a gaussian with 0 mean and unit variance.
 4.
Random Subspace (RS) [17, 18]: represented by d' × d matrices $R=\sqrt{d/d\text{'}}({r}_{ij})$, where r_{ ij }are uniformly chosen with entries in {0, 1}, and with exactly one 1 per row and at most one 1 per column. Unfortunately, RS does not satisfy the JL lemma.
Using the above randomized maps (with the exception of RS projections), the JL lemma guarantees that, with high probability, the "compressed" examples of the data set represented by the matrix D_{ R }= RD have approximately the same distance (up to a εdistortion level) of the corresponding examples in the original space, represented by the columns of the matrix D, as long as d' ≥ c logn/ε^{2}.
We propose a general MOSRAM (Model Order Selection by RAndomized Maps) algorithmic scheme, that implements the above ideas about random projection with bounded distortions to generate a set of similarity indices of clusterings obtained by pairs of randomly projected data. The main difference with respect to the method proposed in [13] is that by our approach we perturb the original data using a randomized mapping μ : ℝ^{ d }→ ℝ^{ d' }:
MOSRAM algorithm
Input:
D : a dataset;
k_{ max }: max number of clusters;
m : number of similarity measures;
μ : a randomized map;
$\mathcal{C}$: a clustering algorithm;
sim : a clustering similarity measure.
Output:
M(i, k): a bidimensional list of similarity measures for each k (1 ≤ i ≤ m, 2 ≤ k ≤ k_{ max })
begin
for k := 2 to k_{ max }
for i := 1 to m
begin
proj_{ a }:= μ(D)
proj_{ b }:= μ(D)
C_{ a }:= $\mathcal{C}$(proj_{ a }, k)
C_{ b }:= $\mathcal{C}$(proj_{ b }, k)
M(i, k) := sim(C_{ a }, C_{ b })
end
end.
The algorithm computes m similarity measures for each desired number of clusters k. Every measure is achieved by applying sim to the clustering C_{ a }and C_{ b }, outputs of the clustering algorithm $\mathcal{C}$, having as input k and the projected data proj_{ a }and proj_{ b }. These data are generated through randomized maps μ, with a desired distortion level ε. It is worth noting that we make no assumptions about the shape of the clusters, and in principle any clustering algorithm $\mathcal{C}$, randomized map μ, and clustering similarity measure sim may be used (e.g. the Jaccard or the Fowlkes and Mallows coefficients [23]).
Stability indices based on the distribution of the similarity measures
Using the similarity measures obtained through the MOSRAM algorithm, we may compute stability indices to assess the reliability of clustering solutions.
More precisely, let $\mathcal{C}$ be a clustering algorithm, ρ a random perturbation procedure (e.g. a resampling or a random projection) and sim a suitable similarity measure between two clusterings (e.g. the Fowlkes and Mallows similarity).
We may define the random variable S_{ k }, 0 ≤ S_{ k }≤ 1:
S_{ k }= sim($\mathcal{C}$(D_{1}, k), $\mathcal{C}$(D_{2}, k)) (2)
where D_{1} = ρ^{(1)}(D) and D_{2} = ρ^{(2)}(D) are obtained through random and independent perturbations of the data set D; the intuitive idea is that if S_{ k }is concentrated close to 1, the corresponding clustering is stable with respect to a given controlled perturbation and hence it is reliable.
Let f_{ k }(s) be the density function of S_{ k }and F_{ k }(s) its cumulative distribution function. A parameter of concentration implicitly used in [13] is the integral g(k) of the cumulative distribution:
Note that if S_{ k }is centered in 1, g(k) is close to 0, and hence it can be used as a measure of stability. Moreover, the following facts show that g(k) is strictly related to both the expectation E[S_{ k }] and the variance Var[S_{ k }] of the random variable S_{ k }:
Fact 1
E[S_{ k }] = 1  g(k).
Indeed, integrating by parts:
Fact 2
Var[S_{ k }] ≤ g(k)(1  g(k).
Since 0 ≤ S_{ k }≤ 1 it follows ${S}_{k}^{2}$ ≤ S_{ k }; therefore, using Fact 1:
Var[S_{ k }] = E[${S}_{k}^{2}$]  E[S_{ k }]^{2} ≤ E[S_{ k }]  E[S_{ k }]^{2} = g(k)(1  g(k)) (5)
In conclusion, g(k) ≃ 0 then E[S_{ k }] ≃ 1 and Var[S_{ k }] = 0, i.e. S_{ k }is centered close to 1. As a consequence, E[S_{ k }] can be used as an index of the reliability of the kclustering: if E[S_{ k }] ≃ 1, the clustering is stable, if E[S_{ k }] ≪ 1 the clustering can be considered less reliable.
We can estimate E[S_{ k }] by means of m similarity measures M(i, k)(1 ≤ i ≤ m) computed by the MOSRAM algorithm. In fact E[S_{ k }] may be estimated by the empirical mean ξ_{ k }:
A χ^{2}based test for the assessment of the significance of the solutions
In this section we propose a method for automatically finding the " optimal" number of clusters and to detect significant and possibly multilevel structures simultaneously present in the data. First of all, let us consider the vector (ξ_{2}, ξ_{3},...,ξ_{H+1}) (eq. 6) computed by using the output of the MOSRAM algorithm. We may perform a sorting of this vector:
where p is the permutation index such that ξ_{p(1)}≥ ξ_{p(2)}≥ ... ≥ ξ_{(H)}. Roughly speaking, this ordering represents the "most reliable" p(1)clustering down to the least reliable p(H)clustering; exploiting this we would establish which are the significant clusterings (if any) discovered in the data.
To this end, for each k ∈ $\mathcal{K}$ = {2, 3,...,H + 1}, let us consider the random variable S_{ k }defined in eq. 2, whose expectation is our proposed stability index. For all k and for a fixed threshold t^{ o }∈ [0, 1] consider the Bernoulli random variable B_{ k }= I(S_{ k }> t^{ o }), where I is the indicator function: I(P) = 1 if P is True, I(P) = 0 if P is False. The sum ${X}_{k}={\displaystyle {\sum}_{j=1}^{m}{B}_{k}^{j}}$ of i.i.d. copies of B_{ k }is distributed according to a binomial distribution with parameters m and θ_{ k }= Prob(I(S_{ k }> t^{ o })).
If we hypothesize that all the binomial populations are independently drawn from the same distribution (i.e. θ_{ k }= θ, for all k ∈ $\mathcal{K}$), for sufficiently large values of m the random variables $\frac{{X}_{k}m{\theta}_{k}}{\sqrt{m{\theta}_{k}(1{\theta}_{k})}}$ are independent and approximately normally distributed. Consider now the random variable:
This variable is known to be distributed as a χ^{2} with $\mathcal{K}$  1 degrees of freedom, informally because the constraint $\widehat{\theta}$ between the random variables X_{ k }, k ∈ $\mathcal{K}$ introduces a dependence between them, thus leading to a loss of one degree of freedom. By estimating the variance mθ(1  θ), with the statistic m $\widehat{\theta}$(1  $\widehat{\theta}$), we conclude that the following statistic
is approximately distributed according to ${\chi}_{\left\mathcal{K}\right1}^{2}$ (see, e.g. [24] chapter 12, or [25] chapter 30 for more details).
A realization x_{ k }of the random variable X_{ k }(and the corresponding realization y of Y) can be computed by using the output of the MOSRAM algorithm:
Using y, we can test the following alternative hypotheses:

Ho: all the θ_{ k }are equal to θ(the considered set of kclusterings are equally reliable)

Ha: the θ_{ k }are not all equal between them (the considered set of kclusterings are not equally reliable)
If $y\ge {\chi}_{\alpha ,\left\mathcal{K}\right1}^{2}$ we may reject the null hypothesis at α significance level, that is we may conclude that with probability 1  α the considered proportions are different, and hence that at least one kclustering significantly differs from the others.
 1.
Consider the ordered vector ξ = (ξ_{p(1)}, ξ_{p(2)},...,ξ_{p(H)})
 2.
Repeat the χ^{2}based test until no significant difference is detected or the only remaining clustering is p(1)(the topranked one). At each iteration, if a significant difference is detected, remove the bottomranked clustering from ξ
The output of the proposed procedure is the set of the remaining (top sorted) kclusterings that correspond to the set of the estimate stable number of clusters (at α significance level). Equivalently, following the sorting of ξ, we may compute the pvalue (probability of committing an error if we reject the null hypothesis) for all the ordered groups of clusterings from the p(1)...p(H) to the the p(1), p(2) group, each time removing the bottom ranked clustering from the ξ vector. Note that if the set of the remaining topranked clusterings contains more than one clustering, we may find multiple structures simultaneously present in the data (at α significance level).
Results and discussion
We present experiments with synthetic and gene expression data to show the effectiveness of our approach. At first, using synthetic data, we show that our proposed methods can detect not only the "correct" number of clusters, but also multiple structures underlying the data. Then we apply our MOSRAM algorithm to discover the " natural" number of clusters in gene expression data, and we compare the results with other algorithms for model order selection. In our experiments we used the classical kmeans [26] and Prediction Around Medoid (PAM) [27] clustering algorithms, and we applied the Bernoulli, Achlioptas and Normal random projections, but in this section we show only the results obtained with Bernoulli projections, since with the other randomized maps we achieved the same results without any significant difference. In all our experiments we set the threshold t^{ o }(see Section "A χ^{2}based test for the assessment of the significance of the solutions" to 0.9. Moreover we applied our proposed χ^{2}based procedure to individuate sets of significant kclusterings into the data. The methods and algorithms described in this paper have been implemented in the mosclust R package, publicly available at [28].
Detection of multiple levels of structure in synthetic data
Samplel: similarity indices. Similarity indices for the synthetic sample1 data set for different kclusterings, sorted with respect to their mean values.
k  mean  variance  pvalue 

2  1.0000  0.0000  1.0000 
6  1.0000  0.0000  1.0000 
7  0.9217  0.0016  0.0000 
8  0.8711  0.0033  0.0000 
9  0.8132  0.0042  0.0000 
5  0.8090  0.0104  0.0000 
3  0.8072  0.0157  0.0000 
10  0.7715  0.0056  0.0000 
4  0.7642  0.0158  0.0000 
Leukemia data set. Stability indices for different kclusterings sorted with respect to their mean values.
k  mean  variance  pvalue 

2  0.8285  0.0077  1.0000 
3  0.8060  0.0124  0.7328 
4  0.6589  0.0060  2.3279e06 
5  0.6012  0.0073  9.5199e11 
6  0.5424  0.0057  6.3282e15 
7  0.5160  0.0062  0.0000 
8  0.4865  0.0050  0.0000 
9  0.4819  0.0060  0.0000 
10  0.4744  0.0049  0.0000 
Lymphoma data set. Stability indices for different kclusterings sorted with respect to their mean values.
k  mean  variance  pvalue 

2  0.9566  0.0028  1.0000 
3  0.7900  0.0149  0.0000 
4  0.6963  0.0128  0.0000 
5  0.6387  0.0075  0.0000 
6  0.6135  0.0082  0.0000 
7  0.6129  0.0079  0.0000 
9  0.5864  0.0063  0.0000 
8  0.5792  0.0079  0.0000 
10  0.5744  0.0058  0.0000 
Experiments with DNA microarray data
To show the effectiveness of our methods with gene expression data we applied MOSRAM and the proposed statistical test to Leukemia [29] and Lymphoma [1] samples. These data sets have been analyzed with other model order selection algorithms previously proposed [10, 13, 30–32]: at the end of this section we compare the results obtained with the cited methods with our proposed MOSRAM algorithm.
Leukemia
This well known data set [29] is composed by 72 leukemia samples analyzed with oligonucleotide Affymetrix microarrays. The Leukemia data set is composed by a group of 25 acute myeloid leukemia (AML) samples and another group of 47 acute lymphoblastic leukemia (ALL) samples, that can be subdivided into 38 BCell and 9 TCell subgroups, resulting in a twolevel hierarchical structure. We applied the same preprocessing steps performed by the authors of the Leukemia study [29], obtaining 3571 genes from the original 7129 gene expression values. We further selected the 100 genes with the highest variance across samples, since low variance genes are unlikely to be informative for the purpose of clustering [10, 31]. We analyzed both the 3571dimensional data and the data restricted to the 100 genes with highest variance, using respectively Bernoulli projections with ε ∈ {0.1, 0.2, 0.3, 0.4} and projections to 80dimensional subspaces. In both cases the kmeans clustering algorithm has been applied.
Lymphoma
Three different lymphoid malignancies are represented in the Lymphoma gene expression data set [1]: Diffuse Large BCell Lymphoma (DLBCL), Follicular Lymphoma (FL) and Chronic Lymphocytic Leukemia (CLL). The gene expression measurements are obtained with a cDNA microarray specialized for genes related to lymphoid diseases, the Lymphochip, which provides expression levels for 4026 genes [34]. The 62 available samples are subdivided in 42 DLBCL, 11 CLL and 9 FL. We performed preprocessing of the data according to [1], replacing missing values with 0 and then normalizing the data to zero mean and unit variance across genes. As a final step, according to [10], we further selected the 200 genes with highest variance across samples, obtaining a resulting data set with 62 samples and 200 genes. As in the previous experiment, we processed both the highdimensional original data and the data with the reduced set of highvariance genes, using respectively Bernoulli projections with ε ∈ {0.1, 0.2, 0.3, 0.4} and projections to 160dimensional subspaces. The kmeans clustering algorithm has been applied.
Comparison with other methods
We compared the results obtained by the MOSRAM algorithm with other model order selection methods using the Leukemia and Lymphoma data sets analyzed in the previous section. In particular we focused our comparison with other stateoftheart stabilitybased methods proposed in the literature.
The Model Explorer algorithm adopts subsampling techniques to perturb the data (data are randomly drawn without replacement) and applies stability measures based on the empirical distribution of the stability measures [13]. This approach is quite similar to ours but we applied random projections to perturb the data and a statistical test to identify significant numbers of clusters, instead of simply qualitatively looking at the distributions of the stability indices. The Figure of Merit measure is based on a resampling approach too, but the stability of the solutions is assessed directly comparing the solution obtained on the full sample with that obtained on the subsamples [32]. We considered also stabilitybased methods that apply supervised algorithms to assess the quality of the discovered clusterings instead of comparing pairs of perturbed clusterings [10, 31]: the main differences between these last approaches are the choice of the supervised predictor and other parameters (no guidance is given in [31], while in [10] a more structured approach is proposed). Finally we considered also a nonstabilitybased method, the Gap statistic, that applies an estimates of the gap between the total sum withinclass dissimilarities and a null reference distribution (the uniform distribution on the smallest hyperrectangle that contains all the data) to assess the "optimal" number of clusters in the data.
Results comparison. Comparison between different methods for model order selection in gene expression data analysis
Methods  Class. risk (Lange et al. 2004)  Gap statistic (Tibshirani et al. 2001)  Clest (Dudoit and Fridlyand 2002)  Figure of Merit (Levine and Domany 2001)  Model Explorer (BenHur et al 2002)  MOSRAM  "True" number k 

Data set  
Leukemia (Golub et al. 1999)  k = 3  k = 10  k = 3  k = 2, 8, 19  k = 2  k = 2, 3  k = 2, 3 
Lymphoma (Alizadeh et al. 2000)  k = 2  k = 4  k = 2  k = 2, 9  k = 2  k = 2  k = 2, (3) 
These results show that our proposed methods based on randomized maps are wellsuited to the characteristics of DNA microarray data: indeed the low cardinality of the examples, the very large number of features (genes) involved in microarray chips, the redundancy of information stored in the spots of microarrays are all characteristics in favour of our approach. On the contrary using bootstrapping techniques to obtain smaller samples from just small samples of patients should induce more randomness in the estimate of cluster stability. A resampling based approach appears to be better suited to evaluate the cluster stability of genes, since significantly larger samples are available in this case [12]. The alternative based on noise injection into the data to obtain multiple instance of perturbed data poses difficult statistical problems for evaluating what kind and which magnitude of noise should be added to the data [17].
All the perturbationbased methods need to properly select a parameter to control the amount of perturbation of the data: resampledbased methods need to select the "optimal" fraction of the data to be subsampled; noiseinjectionbased methods needs to choice the amount of noise to be introduced; random subspace and random projectionsbased methods needs to select the proper dimension of the projected data. Anyway, our approach provides a theoretically motivated method to automatically find an "optimal" value for the perturbation parameter, and in our experiments we observed that values of ε ≤ 0.2 led to reliable results. Moreover our proposed approach provides also a statistical test that may be applied also with other stabilitybased methods to assess the significance of the discovered solutions.
Despite of the convincing experimental results obtained with stabilitybased methods there are some drawbacks and open problems associated with these techniques. Indeed, as shown by [8], a given clustering may converge to a suboptimal solution owing to the shape of the data manifold and not to the real structure of the data, thus introducing bias in the stability indices. Moreover in [37] it has been shown that stability based methods based on resampling techniques, when costbased clustering algorithms are used, may fail to detect the correct number of clusters, if the data are not symmetric. However it is unclear if these results may be extended to other stabilitybased methods (e.g. to our proposed methods based on random projections) or to other more general classes of clustering algorithms.
Conclusion
We proposed a stabilitybased method, based on random projections, for assessing the validity of clusterings discovered in highdimensional postgenomic data. The reliability of the discovered kclusterings may be estimated exploiting the distribution of the clustering pairwise similarities, and a χ^{2}based statistical test tailored to unsupervised model order selection. In the theoretical framework of randomized maps that satisfy the JL lemma, a principled approach to select the dimension of the projected data, and to approximately preserve the structure of the original data is given, thus yielding to the design of reliable stability indices for model order selection in biomolecular data clusterings.
The χ^{2}based statistical test may be applied to any stability method that make use of the distribution of the similarity measures between pairs of clusterings.
Our experimental results with synthetic data and real gene expression data show that our proposed method is able to find significant structures, comprising multiple structures simultaneously present into biomolecular data.
As an outgoing development, considering that the χ^{2}based test assumes that the random variables representing distributions for different number of clusters are normally distributed, we are developing a new distributionindependent approach based on the Bernstein inequality to assess the significance of the discovered kclusterings.
Declarations
Acknowledgements
This work has been developed in the context of CIMAINA Center of Excellence, and it has been funded by the Italian COFIN project Linguaggi formali ed automi: metodi, modelli ed applicazioni and by the European Pascal Network of Excellence. We would like to thank the anonymous reviewers for their comments and suggestions.
This article has been published as part of BMC Bioinformatics Volume 8, Supplement 2, 2007: Probabilistic Modeling and Machine Learning in Structural and Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/14712105/8?issue=S2.
Authors’ Affiliations
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