- Methodology article
- Open Access

# Conditional clustering of temporal expression profiles

- Ling Wang
^{1}, - Monty Montano
^{2}, - Matt Rarick
^{2}and - Paola Sebastiani
^{3}Email author

**9**:147

https://doi.org/10.1186/1471-2105-9-147

© Wang et al; licensee BioMed Central Ltd. 2008

**Received: **01 November 2007

**Accepted: **11 March 2008

**Published: **11 March 2008

## Abstract

### Background

Many microarray experiments produce temporal profiles in different biological conditions but common cluster techniques are not able to analyze the data conditional on the biological conditions.

### Results

This article presents a novel technique to cluster data from time course microarray experiments performed across several experimental conditions. Our algorithm uses polynomial models to describe the gene expression patterns over time, a full Bayesian approach with proper conjugate priors to make the algorithm invariant to linear transformations, and an iterative procedure to identify genes that have a common temporal expression profile across two or more experimental conditions, and genes that have a unique temporal profile in a specific condition.

### Conclusion

We use simulated data to evaluate the effectiveness of this new algorithm in finding the correct number of clusters and in identifying genes with common and unique profiles. We also use the algorithm to characterize the response of human T cells to stimulations of antigen-receptor signaling gene expression temporal profiles measured in six different biological conditions and we identify common and unique genes. These studies suggest that the methodology proposed here is useful in identifying and distinguishing uniquely stimulated genes from commonly stimulated genes in response to variable stimuli. Software for using this clustering method is available from the project home page.

## Keywords

- Bayesian Information Criterion
- Biological Condition
- Marginal Likelihood
- Cluster Assignment
- Cluster Gene Expression

## Background

Cluster analysis of gene expression data is commonly used to group gene expression measurements, cross-sectionally or longitudinally, into categories of genes that have similar patterns of expression. Various clustering methods have been proposed to analyze microarray data [1–4], and hierarchical and k-means clustering have been applied to the discovery and characterization of the regulatory mechanisms of several processes and organisms [5–10]. Time course microarray experiments allow investigators to look at the behaviors of genes over time and hence introduce another dimension of observation [11]. In the past few years, several methods for clustering temporal gene expression data have been proposed that use autoregressive models (CAGED) [12], hidden Markov models [13], mixture models with variations of the EM algorithm [14] or B splines [15], and Bayesian hierarchical models with an agglomerative search using smoothing splines [16]. However, it has been suggested that autoregressive models [12] and some other clustering procedures [13, 17] are better suited to cluster long temporal gene expression data, possibly measured at regularly spaced time points [18]. We introduced an extension to CAGED that overcomes this limitation and is more suitable to cluster short gene expression profiles in [19]. This method uses polynomial models to describe the expression profiles and uses proper prior distributions for the model parameters to make the result of clustering invariant under linear transformations of time [20].

Furthermore, the algorithm finds the optimal number of clusters during the clustering process using a Bayesian decision-theoretic approach.

The common objective of all these different methods is to cluster gene expression temporal profiles observed in one specific biological condition, but in many experiments the temporal expression profiles are observed under different biological conditions: for example Diehn and coauthors [21] measured the gene expression profile of T cells under various activating stimulations to identify those genes that react uniquely to specific activating stimulations. To account for different biological conditions, Storey et al. [22] introduced an F statistic for differential analysis of time course expression data that produced a principled way to find the genes that are expressed differently in two experimental conditions. Here, the objective is different: we are not only interested in discovering the genes that have different dynamics in different experimental conditions. We wish to be able to simultaneously discover these genes and also group them together with other genes that have the same temporal expression profiles.

Other authors have already proposed solutions to this problem. Heard et al. [23] have proposed to extend their model-based clustering of temporal expression profiles in multiple conditions by modeling the concatenated time series. This approach has the advantage of increasing the robustness of clustering by simultaneously using the information from the different experiments. However, this gain of robustness comes at the price that only concatenated time series are clustered, so those genes that have a similar profile in one experimental condition but different profiles in the other conditions will be lost because they are allocated to different clusters. Ng et al [24] proposed a mixture model with random effect that uses the EM algorithm to cluster gene expression data from either time course experiment or experiments with replicates. The flexible nature of linear mixed model gives a unified approach to cluster data collected from various designed experiments and, in principle, this method is applicable to clustering temporal data measured in different conditions. However, similarly to the method in [23], it only clusters the concatenated time series from multiple conditions.

Here we propose an extension of our polynomial-based method to cluster short expression profiles measured in different conditions. This method that we call *conditional clustering* stratifies the data according to the experimental conditions and performs separate cluster analysis within the strata, then attempts to merge the resulting clusters if the merging could improve a Bayesian metrics. Because clustering within each stratum does not use all the information for those genes that have common patterns across more than one experimental condition, we further use an iterative procedure to find those genes that have unique expression profile under a specific condition and genes that have common expression profiles under two, or even more conditions. An application of this novel procedure to real data produces biologically meaningful gene sets.

## Results and discussion

*different genes*that have similar expression patterns in one or more experimental conditions, but also the profiles of the

*same gene*with similar expression patterns in two or more conditions. Figure 1 shows an example. We use a two-step process. First we use the algorithm we introduced in [19] to cluster data in each experimental condition separately. Then we check if any of the resulting clusters from the first step can be merged according to our merging criterion, and introduce an iterative procedure that searches for genes with common patterns of expression across two or more conditions as well as genes with a unique pattern of expression in some particular condition.

### Cluster analysis within conditions

*m*genes measured at

*n*time points

*t*

_{ i }are generated from an unknown number of processes and the goal is to group them into a number of clusters so that each cluster contains those genes with expression profiles generated from the same process. Each process is described by a Bayesian model of the log-transformed expression profile ${x}_{g}=\{{x}_{g{t}_{1}},{x}_{g{t}_{2}},\mathrm{...},{x}_{g{t}_{n}}\}$, where the expected pattern is the polynomial model:

In matrix notation we can write

*x*_{
g
}= *Fβ*_{
g
}+ *ε*_{
g
} (1)

where ${x}_{g}={({x}_{g{t}_{1}},{x}_{g{t}_{2}},\mathrm{...},{x}_{g{t}_{n}})}^{T}$, *F* is the *n* × (*p* + 1) design matrix in which the *i*^{
th
}row is $(1,{t}_{i},{t}_{i}^{2}\mathrm{...},{t}_{i}^{p})$, *β*_{
g
}= (*μ*_{
g
}, *β*_{g 1}, ..., *β*_{
gp
})^{
T
}is the vector of regression coefficients, and ${\epsilon}_{g}={({\epsilon}_{g{t}_{1}},{\epsilon}_{g{t}_{2}},\mathrm{...},{\epsilon}_{g{t}_{n}})}^{T}$ is the vector of errors. We assume that the errors are normally distributed, with $E({\epsilon}_{g{t}_{i}})=0$ and $Var({\epsilon}_{g{t}_{i}})=1/{\tau}_{g}$, for any *t*_{
i
}, and *p* is the polynomial order. To complete the specification of the Bayesian model, we assume the standard conjugate normal-gamma prior density for the parameters *β*_{
g
}and *τ*_{
g
}so that the marginal distribution of *τ*_{
g
}and the distribution of *β*_{
g
}, conditional on *τ*_{
g
}, are

*τ*_{
g
}~ Gamma(*α*_{1}, *α*_{2})

*β*_{
g
}|*τ*_{
g
}~ *N*(*β*_{0}, (*τ*_{
g
}*R*_{0})^{-1})

where *E*(*τ*_{
g
}) = *α*_{1}*α*_{2}, *E*(1/*τ*_{
g
}) = 1/((*α*_{1} -1)*α*_{2}), and *E*(*β*_{
g
}) = *β*_{0} = (*β*_{00}, *β*_{01}, ..., *β*_{0p})^{
T
}, and *R*_{0} is the (*p* + 1) × (*p* + 1) identity matrix. The prior hyper-parameters *α*_{1}, *α*_{2}, *β*_{0} are identical for all genes. We use the data of the non-expressed genes to specify the prior hyper-parameters, as suggested in [19, 25]. To compare different partitions of the genes, we compute the posterior probability of different clustering models so that, given the observed gene expression profiles, the best clustering model is the one with maximum posterior probability. This method was originally suggested in [12] and works as follows. Let *M*_{
c
}denote the model with *c* clusters of gene expression data, where each cluster *C*_{
k
}groups the set of expression profiles generated by the same polynomial models with coefficients *β*_{
k
}and variance 1/*τ*_{
k
}, *k* = 1, ..., *c*. Each cluster contains *m*_{
k
}genes that are jointly modeled as

*x*_{
k
}= *F*_{
k
}*β*_{
k
}+ *ε*_{
k
}

*x*

_{ k }and the matrix

*F*

_{ k }are defined by stacking the vectors ${x}_{k1},{x}_{k2},\mathrm{...},{x}_{k{m}_{k}}$ and the regression matrix ${F}_{k1},{F}_{k2},\mathrm{...},{F}_{k{m}_{k}}$ in the following way

Note that we now label the vectors *x*_{
g
}assigned to the same cluster *C*_{
k
}with the double script *k*_{
g
}, in which *k* denotes the cluster membership, and *g* = 1, ..., *m*_{
k
}is the index for the genes in this cluster. Here *m* = ∑_{
k
}*m*_{
k
}, *ε*_{
k
}is the vector of uncorrelated errors with *E*(*ε*_{
k
}) = 0 and *V*(*ε*_{
k
}) = 1/*τ*_{
k
}.

*M*

_{ c }is

*P*(

*M*

_{ c }|

*x*) ∝

*P*(

*M*

_{ c })

*f*(

*x*|

*M*

_{ c }), where

*P*(

*M*

_{ c }) is the prior probability,

*x*consists of all the time series data, and

*f*(

*x*|

*M*

_{ c }) = ∫

*f*(

*x*|

*θ*)

*f*(

*θ*|

*M*

_{ c })

*dθ*is the marginal likelihood. The vector of parameters

*θ*contains all the parameters specifying the model

*M*

_{ c }, the function

*f*(

*θ*|

*M*

_{ c }) is its prior density, and the function

*f*(

*x*|

*θ*) is the likelihood function. Since we assume that the profiles assigned to different clusters are independent, the overall likelihood function is

*p*

_{ k }is the marginal probability that a gene expression profile is assigned to the cluster

*C*

_{ k }. We assume a symmetric Dirichlet distribution for the parameters

*p*

_{ k }, with hyper-parameters

*η*

*k*∝

*p*

_{ k }. and overall precision

*η*= ∑

_{ k }

*η*

_{ k }. Then the marginal likelihood

*f*(

*x*|

*M*

_{ c }) can be calculated in closed form and is given by the formula

When all clustering models are a priori equally likely, the posterior probability *p*(*M*_{
c
}|*x*) is proportional to the marginal likelihood *f*(*x*|*M*_{
c
}) that becomes our probabilistic scoring metric.

To make the computation feasible, the same agglomerative, finite-horizon heuristic search strategy introduced by Ramoni et al in [12] is used. This heuristic search orders the merging of profiles by their distance, so that the closest profiles are tested for merging first. The procedure first calculates the *m*(*m* - 1) pair-wise distances for the *m* genes and then attempts to merge the two closest genes into one cluster. If this merging increases the likelihood it is accepted, the two genes are assigned to the same clusters and their profile is replaced by the *average profile* that is a point-by-point average of the expression of the two genes. The same procedure is repeated for the new set of *m* - 1 profiles. If this merging is not accepted, the heuristic tries to merge the next second closest genes to see if their merging increases the likelihood or not. This procedure continues until an acceptable merging is found, otherwise it stops. This strategy automatically determines the best number of clusters when it does not find a pair of profiles to be merged into the same cluster and therefore stops. Note that the decision to merge profiles is based on the posterior probability and the distance between profiles is only used to speed up computations. In the implementation we have two possible choices of distances: the Euclidean distance and the negative of the correlation coefficient.

To find the best polynomial order, we repeat the conditional cluster analysis for different values *p*, and compute the Bayesian Information Criterion (BIC) [20] of the cluster set for each *p* and the value that optimizes the BIC is chosen. Let *q* be the number of parameters in the model, then *q* = *c*(*p* + 2) when the model does not have intercept and *q* = *c*(*p* + 3) when the model has intercept. The BIC of the clustering model *M*_{
c
} is

*BIC* = -2 log *f*(*x*|*M*_{
c
}) + *q* log(*n*) (5)

where *f*(*x*|*M*_{
c
}) is the marginal likelihood of the model specified in Equation 4. We use the same *p* for clustering in each condition and the final merging of all conditions to ensure consistency of the overall clustering model.

We evaluated this clustering algorithm in three simulated data, and we demonstrated that this algorithm performs well in identifying the correct number of clusters as well as the correct cluster assignments [19]. These results are consistent with other evaluations, where we showed that the combination of a distance driven search with a Bayesian scoring metrics leads to correctly identifying clusters in an efficient way [26, 27]. The results were compared to a competing program STEM [18] which is also designed for clustering short temporal gene expression data.

### Cluster analysis across all experimental conditions

*initial results*(See Figure 2, part 1). We note that the analysis in each biological condition is conducted independently of the data measured in the other conditions, and this is equivalent to assuming that all genes respond "uniquely" to each biological condition. However, some of these initial clusters may contain genes with the same expression profile across different conditions and using this information may produce more robust results. Our conjecture is that we can find

*genes*that have a

*unique*expression

*profile*for a specific biological condition (GUP), and

*genes*that have a

*common*expression

*profile*across two or more experimental conditions (GCP). GCP are "commonly affected" by two or more conditions because they exhibit the same expression profiles that are assigned to the same cluster. On the other hand, GUP are "uniquely affected" by a specific condition and their profile will not be assigned to clusters containing the profiles of the same gene in other conditions. Biologically, GUP are those genes that one would target when looking for an expression profile that characterizes a particular experimental conditions. GCP, on the other hand, would be those genes with robust expression across two or more of the experimental conditions. To identify the GCP and GUP we recursively search for genes whose profile appears only once in a cluster as shown in part 2 of Figure 2. The overall conditional clustering works as follows:

- 1.
Cluster the gene expression profiles within each experimental condition.

- 2.
Use all the clusters generated in step 1 as input of a new cluster analysis across all conditions using the same algorithm. During this step we try the merging of these initial clusters using the marginal likelihood in Equation 4 as scoring metric until no merging can improve the likelihood.

- 3.
Identify the GCP and GUP in the clusters derived in step 2. If there are no GCP, so there are no clusters merging the profiles of the same gene in two or more conditions, go to step 4. If there are GCP go to step 5.

- 4.
There are no GCP. The resulting clusters are the final clusters for GUP.

- 5.
Remove those GCP from the analysis, but keep their cluster assignments. Re-cluster the remaining data containing GUP using our clustering algorithm.

- 6.
Identify the GCP and GUP in the clusters produced at step 5. If there are no GCP, go to step 7. If there are GCP go to step 5.

- 7.
Take the clusters containing the GCP removed during the iterations, try to merge these clusters of GCP to see if the merging could improve the marginal likelihood. The resulting clusters are the final clusters for GCP.

The results from this procedure give us two sets of clusters: clusters of GCP and clusters of GUP. The GCP clusters contain expression profiles for genes that have the same behavior in at least two experimental conditions. The GUP clusters have expression profiles of genes that have unique expression patterns in a particular experimental condition. We should notice that there are multiple conditions here and to choose the optimal polynomial order *p* we proceed as follows. First, for a fixed *p*, we perform the clustering in each condition, then we try to merge the clusters from all the conditions if that improves the likelihood, as mentioned above, and this is the initial results. The optimal *p* is selected by comparing BIC of the various initial results using *p* = 1, ..., *n* - 1.

### Evaluation

We conducted three simulation studies to evaluate the performance of the proposed Bayesian conditional clustering algorithm. The first two simulations examine the effects of sample size and variability on the accuracy of the cluster produced as initial results. The third simulation examines the effectiveness of the whole clustering algorithm in finding GCP and GUP. In all three simulations we generated normalized patterns.

### Simulations 1

#### Data

#### Metrics

We clustered the simulated data using the conditional clustering algorithm and used a statistic proposed by Rand [28] to evaluate the similarity between the results and the true cluster assignment. The rationale of this statistic is that, given two sets of clusters, the pairs of objects that are either assigned to the same cluster or split across clusters in both sets show similar cluster assignments, so the statistic is simply the proportion of pairs of objects that are assigned consistently in the two sets. We borrow the example used in [28] to describe this statistic more in details. Suppose we wish to cluster six objects, say *a, b, c, d, e, f*, and consider two ways of grouping them: group 1 consists of the two clusters (*a, b, c*), (*d, e, f*), and group 2 consists of the three clusters (*a, b*), (*c, d, e*), and (*f*). The six objects can be grouped into 15 possible pairs and we can label the elements of each of the 15 pairs as either "assigned to the same cluster" in both groups, "assigned to different clusters" in both groups, or mixed in the other cases, based on the cluster assignments in the two groups. For example, the pair *ab* is assigned to the same cluster in both groups, the pair *ac* is mixed because it is assigned to the same cluster in the first group but *a* and *c* are assigned to different clusters in the second group, and the pair *ad* is split across two clusters in both groups. In the two groups above the two pairs *ab, de* are assigned to the same clusters, and the seven pairs *ad, ae, af, bd, be, bf, cf* are split across clusters in both groups so that Rand statistic is 9/15 = 0.6.

#### Results

### Simulation 2

### Simulation 3

Cross classification table of the iterative clustering results for the simulated 700 genes.

Our Results Assignment | ||||
---|---|---|---|---|

GCP | GUP | Total | ||

True Assignment | GCP | 179 | 21 | 200 |

GUP | 15 | 485 | 500 | |

Total | 194 | 506 | 700 |

### Application

We applied our new clustering algorithm to the gene expression data from [21]. The experiment used cDNA microarrays to study the genomic expressions of human T cells in an experimental control condition, and in response to stimulations of CD3, CD28, their co-stimulation CD3/CD28, lectin phytohemagglutinin (PHA), and a combination of the calcium ionophore ionomycin and the phorbol ester phorbol 12-myristate 13-acetate (PMA/lo). In each of these conditions, the expression profile of human T cells was observed at 0, 1, 2, 6, 12, 24, 48 hours. We used the expression profiles analyzed in [21], but removed the profiles with missing data so that we analyzed the expression levels of 2,362 genes.

Biological categories identified by EASE. NS = not significant

Category analysis with EASE | Total genes in clusters | Common genes in clusters | Specific non-overlapping genes for each stimulation | ||
---|---|---|---|---|---|

All | Overlap | CD3/CD28 | PMA/lo | PHA | |

DNA binding | 0.03 | NS | 0.07 | NS | 0.009 |

TF and Immune-Hs | 0.03 | NS | 0.007 | NS | 0.06 |

TF-immune-DNA binding-Hs | NS | NS | 0.007 | NS | NS |

TF binding | 0.03 | NS | 0.048 | NS | NS |

DNA synthesis | e-5 | 0.0004 | 0.003 | 0.003 | 0.03 |

Chromatin | 0.01 | NS | 0.02 | 0.047 | 0.001 |

Cell cycle | e-23 | e-7 | e-13 | e-13 | e-17 |

Gene Ontology categories

ENRICHED CATEGORIES | GO CATEGORY: TFS AND IMMUNE |
---|---|

Categories of overlapping genes in ALL stimulations: DNA synthesis, cell cycle | None |

Categories of genes in ALL stimulations: DNA binding, TFs and immune, TF binding, DNA synthesis, cell cycle | CEBPG; CHUK; CXCR4; HIF1A; HLA-E; ICAM1; IL8; IRAK1; IRF2; NFATC1; NFKB2; POU2F2; RELB; STAT1; STAT5B; TBX21; TCF7; VDR; VIPR1; WT1 |

Categories of CD3/CD28 specific genes (overlapping genes were subtracted): TF and immune, DNA binding, TF binding, chromatin, DNA synthesis, DNA binding, RNA processing, cell cycle | CEBPG; CHUK; HIF1A; ICAM1; IL8; IRAK1; NFATC1; NFKB2; POU2F2; RELB; STAT1; STAT5B; TBX21; VDR; WT1 |

## Conclusion

Temporal microarray experiments across several experimental conditions are frequently conducted, and it is very important to recognize the specific features built in this design. In this paper we introduce a clustering technique that incorporates the experimental conditions in the analysis. More importantly, we develop an iterative procedure to distinguish, from a set of resulting clusters, clusters of common genes and unique genes. Our simulation study shows that this clustering algorithm can correctly identify the number of clusters, and find the common and unique genes with low error rate. The application of this technique to the analysis of data from [21] gives us a set of very interesting clusters biologically, and a new perspective to look at this data.

There are some limitations in this work that can stimulate further research. Other methods, for example bi-clustering, can be applied to cluster data measured in two conditions. Bi-clustering has the advantage of simultaneously clustering rows and columns, and it has been applied to co-clustering of genes and experimental conditions to find differentially expressed genes [30]. However, the current versions of bi-clustering do not take into account the temporal nature of the data. Nevertheless, it would be interesting to compare the approaches. Another limitation of this work is that we considered the situation in which the sampling frequency is the same across experimental conditions. It is not straightforward to generalize our approach to experiments comparing expression profiles measured with different sampling frequency. One possibility is to make the sampling frequencies homogeneous by adding the time points that are missing and use, for example, imputations to fill in the missing observations or Markov Chain Monte Carlo methods to estimate the Bayesian score.

## Availability and requirements

Project name: Bayesian conditional clustering of temporal expression profiles.

Project home page: http://people.bu.edu/sebas/condclust/index.htm.

Operating system: Microsoft Windows XP and Vista.

Programming language: R

License: GNU

## Declarations

### Acknowledgements

This research was supported by NIH grant NHGRI R01 HG003354-01A2. The authors thank Al Ozonoff, Gheorghe Doros and Mike LaValley for their suggestions to improve and evaluate the algorithm of conditional clustering.

## Authors’ Affiliations

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