Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering
- Alexandre G de Brevern^{1}Email author,
- Serge Hazout^{1} and
- Alain Malpertuy^{2}
DOI: 10.1186/1471-2105-5-114
© de Brevern et al; licensee BioMed Central Ltd. 2004
Received: 04 May 2004
Accepted: 23 August 2004
Published: 23 August 2004
Abstract
Background
Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs.
Results
In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression.
Conclusion
The presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation.
Background
The genome projects have increased our knowledge of genomic sequences for several organisms. Taking advantage of this knowledge, the microarrays technologies allow the characterization of a whole-genome expression by showing the relative transcript levels of thousand of genes in one experiment [1]. Numerous applications were developed apart gene expression analysis like single nucleotide polymorphism (SNP) and genotyping [2, 3], diagnosis [4] and comparative genomics [5] analysis. Particularly, the transcriptome analysis provides insight into gene regulations and functions. To help the characterization of relevant information in microarray data, specific computational tools are needed. The identification of co-expressed genes is commonly performed with unsupervised approaches, such as clustering methods with the Hierarchical Clustering (HC) [6], the k-means [7] and the Self-Organizing Map (SOM) [8, 9], or with projection methods such as the Principal Component Analysis (PCA) [10] and Independent Component Analysis (ICA) [11].
Among these techniques, the HC approach is a widely held method to group genes sharing similar expression levels under different experimental conditions [12]. The HC is performed from the distance matrix between genes computed from the microarray data, i.e. the gene expression levels for various experimental conditions. Different aggregation methods can be used for the construction of the dendogram generally leading to different tree topologies and a fortiori to various cluster definitions [13]. For instance, the single-linkage algorithm is based to the concept of joining the two closest objects (i.e. genes) of two clusters to create a new cluster. Thus the single-linkage clusters contain numerous members and are branched in high-dimensional space. The resulting clusters are affected by the chaining phenomenon (i.e. the observations are added to the tail of the biggest cluster). In the complete-linkage algorithm, the distance between clusters is defined as the distance between the most distant pair of objects (i.e. genes). This method gives compact clusters. The average-linkage algorithm is based on the mean similarity of the observations to all the members of the cluster. Yeung and co-workers [14] showed that single-linkage hierarchical clustering is inappropriate to analyze microarray data. Gibbons and Roth [15] showed by using gene ontology that single-and average-linkage algorithms produce worse results than random. In addition these authors conclude that the complete-linkage method is the only appropriate HC method to analyze microarrays experiments.
The microarrays experiments frequently contain some missing values. The missing values are part of the experimental errors due to the spotting conditions (e.g. spotting buffer, temperature, relative humidity...) and hybridization (e.g. dust on the slide...) [16, 17]. The users commonly discard suspicious dots during the images analysis step. Thus, the resulting data matrix contains missing values (MVs) which may disturb the gene clustering obtained by the classical clustering methods, e.g. HC, SOM, or projection methods, e.g. PCA. To limit the effects of MVs in the clustering analyses, different strategies have been proposed: (i) the genes containing MVs are removed, (ii) the MVs are replaced by a constant (usually zero), or (iii) the MVs are re-estimated on the basis of the whole gene expression data. Few estimation techniques have been applied to such data. The k-nearest neighbors approach (kNN) computes the estimated value from the k closest expression profiles among the dataset. Troyanskaya and co-workers showed that the weighted kNN approach, with k = 15, is the most accurate method to estimate MVs in microarray data compared to replacement by zero, row average, or Singular Value Decomposition [18]. A recent work proposes Bayesian principal component analysis to deal with MVs [19]. In the same way, Zhou and co-workers [20] have used a Bayesian gene selection to estimate the MVs with linear and non-linear regression. However, the kNN approach is the most popular approach for estimating the MVs.
To explore the incidence of MVs in gene clustering, we first assessed the proportion of MVs in different sets of public data from Saccharomyces cerevisiae and human. Observing that MVs are widely present in the expression data, we then analyzed the effects of MVs on the results of a hierarchical clustering (HC) according to the chosen clustering algorithm. In the same way, we evaluated the impact of MVs replacement and estimation in the gene cluster definition by using a hierarchical clustering method.
Results and Discussion
Missing values overview
Missing values. Examples of missing value occurrences in different datasets from the yeast S. cerevisiae and human microarrays experiments. The year of publication, the studied organism, the number of genes on the DNA chip, the number of experimental conditions, the percentage of MVs and the percentage of genes with missing values are indicated.
Authors | Ref | Organism | Number of genes | Number of experimental conditions | Missing Values percentage (%) | genes with missing values (%) |
---|---|---|---|---|---|---|
Bohen et al. (2003) | [21] | human | 16523 | 16 | 7.6 | 63.6 |
Sorlie et al. (2003) | [22] | human | 552 | 122 | 6.2 | 94.7 |
Garber et al .(2001) | [23] | human | 918 | 73 | 2.4 | 72.7 |
Alizadeh et al .(2000) | [24] | human | 4026 | 96 | 5.3 | 78.8 |
Gasch et al. (2000) | [25] | yeast | 6153 | 178 | 3.0 | 87.7 |
Ogawa et al. (2000) | [26] | yeast | 6013 | 8 | 0.8 | 3.8 |
Ferrea et al. (1999) | [27] | yeast | 5769 | 4 | 10.6 | 30.9 |
Spellman et al. (1998). | [28] | yeast | 6178 | 77 | 5.9 | 91.8 |
Main steps of the analysis
Moreover, two other experiments were (v) designed and (vi) evaluated: clustering with MVs estimated by the kNN approach, and clustering with MVs replaced by zero.
Experimental sets
As we work on the impact of MVs in gene clustering, we need at first biological datasets without MVs. These sets have been extracted from Ogawa set [26] (noted OS) and Gasch set [25] (noted GS). OS and GS have been chosen because they contain few MVs and, after filtering the number of genes remains important, (ca. 6000). The original Ogawa set contained 6013 genes with 230 genes having MVs. The elimination of the genes with MVs (i.e. 3.8% of the genes) leads to a set with 5783 genes. For the GS, the number of MVs is more important and some experimental conditions have more than 50% of MVs. So we have limited the final number of selected experimental conditions from 178 to 42 (see section Methods), it allows to conserve 5843 genes, i.e. only 310 genes are not analyzed, representing 5.0% of all the genes.
Moreover, we have defined two smaller sets, GS_{H2O2} and GS_{HEAT}, from GS corresponding respectively to H_{2}O_{2} and heat shock experimental conditions.
To assess the influence of size of the datasets and the number of observations (genes), we have generated smaller sets corresponding to a ratio 1/n (n = 2, 3, ..., 7) of the initial OS, GS, GS_{H2O2} and GS_{HEAT} gene content (see section Methods).
Example of clustering disturbance caused by missing values introduction
Number of clusters
Influence of the replacement method on the Conserved Pairs Proportion (CPP). The numbers of clusters defined per metric are indicated (K^{ algo }). The CPP and CPP_{ f }values are given for the (1/7) subset of Ogawa [26] with missing values introduced (a) in the entire subsets and (b) only in extreme values (values > 1.5 or < -1.5). The CPP was computed by using the seven algorithms on the data without replacing the MVs (MV), after replacing the MVs by the kNN method (kNN) with the k_{ opt }values (see Table 3), or, after replacing the MVs by zero (zero). For kNN and zero values, the CPP and CPP_{ f }(with f = 5) values are given in (%). The percent of gain for all the simulations is indicated (+/-), i.e. the proportion of simulations with a better CPP value than the CPP value of MV. The values given in this table are the average values for MV in the range [1%; 50%] of genes with one MV per genes.
(a) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
CPP | CPP _{f} | ||||||||||
MV | kNN | zero | MV | kNN | zero | ||||||
Algorithm | K ^{ algo } | (%) | (%) | (+/-) | (%) | (+/-) | (%) | (%) | (+/-) | (%) | (+/-) |
single | 175 | 96.4 | 98.3 | 99.3 | 98.2 | 99.6 | 98.8 | 99.1 | 53.7 | 99.0 | 47.8 |
centroid | 175 | 95.3 | 96.0 | 74.8 | 95.7 | 64.1 | 97.7 | 97.9 | 57.6 | 97.8 | 53.3 |
average | 83 | 73.2 | 75.3 | 66.3 | 74.8 | 62.0 | 92.0 | 92.5 | 54.4 | 93.0 | 57.6 |
median | 165 | 70.1 | 71.5 | 55.2 | 72.2 | 54.4 | 85.2 | 86.0 | 52.8 | 86.2 | 52.1 |
mcquitty | 48 | 58.2 | 59.1 | 54.5 | 58.6 | 50.1 | 74.9 | 75.3 | 51.9 | 75.1 | 49.5 |
Ward | 19 | 51.0 | 52.4 | 62.4 | 50.7 | 49.1 | 71.8 | 73.3 | 60.2 | 71.5 | 49.1 |
complete | 36 | 47.9 | 49.2 | 60.4 | 48.4 | 51.9 | 71.9 | 72.7 | 53.7 | 72.1 | 51.6 |
(b) | |||||||||||
CPP | CPP _{f} | ||||||||||
MV | kNN | zero | MV | kNN | zero | ||||||
Algorithm | K ^{ algo } | (%) | (%) | (+/-) | (%) | (+/-) | (%) | (%) | (+/-) | (%) | (+/-) |
single | 175 | 97.9 | 98.4 | 98.5 | 98.7 | 98.3 | 98.8 | 99.3 | 86.9 | 99.7 | 76.6 |
centroid | 175 | 96.5 | 96.7 | 64.8 | 96.8 | 60.6 | 98.5 | 98.5 | 55.9 | 97.7 | 32.8 |
average | 83 | 81.4 | 81.0 | 45.1 | 83.3 | 66.1 | 93.9 | 94.5 | 57.0 | 94.9 | 61.3 |
median | 165 | 75.0 | 74.2 | 45.7 | 74.2 | 47.9 | 87.0 | 86.5 | 50.3 | 85.1 | 45.4 |
mcquitty | 48 | 71.6 | 71.3 | 48.1 | 71.9 | 51.0 | 83.1 | 83.2 | 49.6 | 84.6 | 58.2 |
Ward | 19 | 63.8 | 62.4 | 42.1 | 60.8 | 29.9 | 83.0 | 81.1 | 37.1 | 79.8 | 27.0 |
complete | 36 | 56.5 | 55.2 | 44.5 | 56.0 | 47.2 | 75.7 | 74.6 | 46.1 | 74.6 | 45.0 |
Index "Conserved Pairs Proportion (CPP)"
Similar results are observed for all the sets OS, GS, GS_{H2O2} and GS_{HEAT} and all the generated sets from 1/2 to 1/7. These last results show that the quality of the gene clustering is not disturbed by the reduction of the number of genes.
It must be clearly noted that to limit the effect of the topology of each algorithm, we have fixed that the 10 most populated clusters must represent 80% of the genes. The number of the most populated clusters is fixed at 10 due to the Ward linkage method that gives a very limited number of clusters. Then, the percentage of genes belonging to the 10 most populated clusters have been tested ranging from 70% to 90% (data not shown). For example with a percentage equal to 90%, the CPP values of single-and the centroid-linkage methods remain too stable to observe a clear decrease as seen in Figure 3. The choice of 80% allows to analyze the precise decrease of the different CPP values and to compare the different aggregation methods.
k-Nearest Neighbor (kNN)
kNN approach: The error rates according to the number of genes. The sets of Ogawa (OS, [26]) and Gasch (GS, GS_{HEAT} and GS_{H2O2}, [25]) have been cut into subsets representing from (1/2) to (1/7) of the original sets. For each of the subsets, the optimal value for k (k_{ opt }), i.e. the number of neighbours used in kNN approach, has been computed. It was chosen as the number which minimizes the error rate. In regards to each subset is given the number of genes (nb genes).
OS | GS | GS_{HEAT} | GS_{H2O2} | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
subsets | nb genes | error rate | k _{ opt } | nb genes | error rate | k _{ opt } | nb genes | error rate | k _{ opt } | nb genes | error rate | k _{ opt } |
(1/7) | 827 | 0.190 | 14 | 815 | 0.292 | 18 | 523 | 0.183 | 12 | 717 | 0.143 | 14 |
(1/6) | 968 | 0.186 | 20 | 955 | 0.276 | 8 | 608 | 0.199 | 15 | 837 | 0.155 | 11 |
(1/5) | 1159 | 0.187 | 20 | 1141 | 0.283 | 23 | 731 | 0.195 | 22 | 1003 | 0.155 | 17 |
(1/4) | 1448 | 0.180 | 22 | 1427 | 0.270 | 26 | 913 | 0.199 | 28 | 1254 | 0.146 | 13 |
(1/3) | 1929 | 0.180 | 23 | 1903 | 0.274 | 8 | 1215 | 0.187 | 23 | 1669 | 0.139 | 15 |
(1/2) | 2892 | 0.174 | 30 | 2853 | 0.255 | 7 | 1822 | 0.187 | 8 | 2504 | 0.135 | 16 |
set | 5783 | 0.177 | 39 | 5705 | 0.255 | 8 | 3643 | 0.186 | 10 | 5007 | 0.135 | 17 |
Improvements of CPP with kNN approach and zero-value replacements
The CPP was computed for the seven agglomeration methods. We have compared the HC results obtained with the reference sets and the generated sets without replacement of MVs, with kNN replacement or with zero-value replacement methods. Table 2a shows that the kNN and zero-value replacements both improved the mean CPP whatever the clustering method used, except for the Ward method with the zero value replacement. The kNN approach is the most relevant method to replace MVs. In 55.2% to 66.3% of the simulations, kNN is better and globally gives a mean increase of the CPP within the range [0.7; 2.1]. The centroid-and single-linkage methods have better increase in 74.8% and 99.3% of the simulations respectively due to their particular topologies. The zero value replacement is clearly less efficient.
Nevertheless, as a slight variation can displace one gene into a close cluster, we have characterized another index named CPP_{ f }to consider the f closest clusters of the selected cluster. This index is similar to the previous one and takes into account that the genes may be relocated in close clusters (see section Methods). It allows the evaluation of the topology conservation. We used f = 5. We observed that the co-associated genes in the reference sets are often displaced to close clusters in the simulated sets. As observed for the CPP, the kNN approach improves the CPP_{ f }for all the clustering methods in 51.9% to 60.2% of the simulations within the range [0.2; 1.5]. Due to their high initial CPP values (97.7% and 98.8%), single-and centroid-linkage methods do not have a gain as previously observed for the CPP.
Similar results are obtained with the other sets with slight variations. For example, GS_{H2O2} have CPP and CPP_{ f }close to the one of OS. Conversely, the CPP and CPP_{ f }of GS_{HEAT} are better than the ones of OS and GS_{H2O2} for the complete-linkage and Ward methods, but lower for the others. The GS set has higher CPP and CPP_{ f }for single-likage to mcquitty method due to a lower influence of the MVs in a vector with a higher number of experimental conditions. Nevertheless, the complete-linkage and Ward linkage still remain at a very low CPP (close to 50%).
Moreover, we have tested the influence of the number of MVs per gene by introducing more than one missing value per gene. We obtained similar results showing less than 0.2% of variations of the CPP values. In addition we have tested the consequences of using k values different of k_{ opt }values in the range [k_{ opt }-10 ; k_{ opt }+10] and we observed a decrease of CPP within the range [1%; 5%] (data not shown).
Extreme values
We have followed the same methodology to analyze the extreme values, i.e. values superior to 1.5 and lower than -1.5. Table 2b summarizes the results of the OS (1/7). The CPP values are superior to the CPP values obtained previously, because only the genes with important variations have MVs and are members of small clusters. We observe that MVs replacement has little effect. Indeed, the kNN and zero – value replacement cannot restore a correct distribution (cf. Figure 4). However, the CPP_{ f }shows that the kNN is better than the replacement by zero, allowing a better topology preservation. Same results are observed for the other sets (data not shown).
Conclusions
MVs are a common trait of microarrays experiments. Few works had been reported about MVs replacements [18–20] and none analyse their influence in the clustering of microarrays data. In our study, we showed that MVs significantly biased the hierarchical clustering. In addition, we observed that the effects of MVs are correlated to the chosen clustering method. The single linkage-method is the most stable due to the building of cluster of large size and numerous small clusters and singletons. At the opposite, the Ward and complete-linkage methods create well distributed population of clusters inducing a higher sensitivity to MVs. The topology of the dendogram is highly disturbed by transferring genes in distant clusters.
We showed that the kNN replacement method was the most efficient approach to compensate the MVs effects compared to the classical replacement by zero. The k_{ opt }depends on the sample size. It is important to keep in mind that the MVs corresponding to extreme values are difficult to estimate with the kNN method. The impact of their approximation upon the clustering is significant. Hence, new approaches like the Bayesian Principal Component Analysis (BPCA) may overcome this problem. In a recent work Liu and co-workers suggest to potentially eliminate the incomplete series of data by using robust Singular Values Decomposition [29]. In addition, our work showed clearly the need of evaluation of the data quality and statistical measurements as noted by Tilstone [30].
Contrary to Yeung and co-workers [14] and Gibbons and Roth [15], we have defined for each type of hierarchical clustering algorithm (algo) a specific number of clusters (K^{ algo }). This point is one of the main difficulties noted by Yeung and co-workers [31] to evaluate the clustering methods as the topology generated are different. The comparison of the different aggregative clustering algorithms remains constrained by the topology (e.g. 175 clusters defined in our study for single-linkage compared to 19 clusters for Ward method). All these results are in accordance with the results of Nikkilä and co-workers [32] which show a hieratic problem of topology preservation in hierarchical clustering. Recent methods like SOTA [33, 34] or Growing SOM (Self-Organizing Maps) [35] have combined a hierarchical clustering visualization with the preservation of the topology allowed by the SOM. Our future works will address the definition of a most robust clustering method.
Methods
Data sets
We used 8 public data sets from the SMD database ([36]; see Table 1). Two sets were used for a thorough analysis. The first one (Ogawa set) was initially composed of N = 6013 genes and n = 8 experimental conditions about the phosphate accumulation and the polyphosphate metabolism of the yeast Saccharomyces cerevisiae [26]. The second one corresponds to various environmental stress responses in S. cerevisiae [25]. This set (Gasch set) contains N = 6153 genes and n = 178 experimental conditions. Due to the diversity of conditions in this set, we focused on two experimental subsets corresponding to heat shock and H2O2 osmotic shock respectively.
Data sets refinement: missing values enumeration
To evaluate the incidence of MVs on hierarchical clustering, we built complete datasets without MVs. All the genes containing at least one missing value were eliminated from the Ogawa set (noted OS). The resulting OS set contains N = 5783 genes and n = 8 experimental conditions. The second set without MVs was taken from Gasch et al. and called GS. The experimental conditions (column) containing more than 80 MVs were removed. The resulting GS matrix contains N = 5843 genes and n = 42 experimental conditions. Two subsets were generated from GS and has been noted GS_{HEAT} and GS_{H2O2}. They correspond to specific stress conditions as described previously. GS_{HEAT} and GS_{H2O2}contain respectively N = 3643 genes with n = 8 experimental conditions and N = 5007 genes with n = 10 experimental conditions.
To test the influence of the matrix size, i.e. the number of genes, we built six smaller sets corresponding to 1/2, 1/3, 1/4, 1/5, 1/6 and 1/7 of OS, GS, GS_{HEAT} and GS_{H2O2}. To obtain representative subsets, we can not use a random generation which can bias the results. So, we searched for each subset the series of genes which reflect at best all the genes of the complete set. First, the distance matrices between all the genes were computed. Then, we performed an iterative process by: (i) computing the sum of the distances for each possible t-uplets (t= 2 to 7) of the set, (ii) choosing the t genes which have the minimal distance, (iii) selecting 1 representative gene upon the t selected genes, this gene is chosen as the closest to the barycenter of the cluster, (iv) eliminating from the process the t genes. Step (i) to step (iv) are repeated until all the genes are used. All the representative genes constitute the subset (1/t). This procedure allows one to reduce the redundancy of similar genes and to maintain approximately a number of gene clusters constant.
Missing values generation
From the sets without MVs, we introduced a rate τ of genes containing one MV (τ = 1 to 50.0%), these MVs are randomly drawn. Each random simulation is generated at least 100 times per experiment to ensure a correct sampling.
Replacement of MVs by the kNN method
To fulfill v_{ i }, a missing value i for a given expression vector v (i.e. a gene), with the kNN method, the k vectors w corresponding to the k most nearest vectors to v (without taking the i th elements of the w-vectors into account) are searched. The missing value v_{ i }is then estimated by a weighted value of the k retained w_{ i }values.
The similar vectors are identified by calculating the Euclidean distance d between the vector v and every vector w. The k minimal distances d(v, w) are selected and the estimated value is computed as follow [18, 19]:
In the weighted kNN, s(v, w^{ t }) = 1 / d(v, w^{ t }), this similarity measure s(v, w^{ t }) is deduced from the distance d(v, w^{ t }) between v and its neighbors w. In equation (1), more a vector w close to v, more it contributes to the estimation of the missing value. The kNN approach has no theoretical criterion to select the optimal k value (k_{ opt }) [19]. We have estimated for each subset the corresponding k_{ opt }using the sets without MVs to ensure a minimal bias in the comparison of the hierarchical clustering results. The determined k_{ opt }value is associated with a minimal global error rate as defined by Troyanskaya and co-workers [18].
Hierarchical Clustering
The hierarchical clustering (HC) algorithm allows the construction of a dendogram of nested clusters based on proximity information [6]. The HC have been performed using the hclust package in R software [37]. Seven hierarchical clustering algorithms have been tested: average-linkage, complete-linkage, median-linkage, mcquitty, centroid-linkage, single-linkage and Ward minimum variance [13].
The distance matrix between all the vectors (i.e. genes) is calculated by using an external module written in C language. We used the normalized Euclidean distance d* to take account of the MV:
v and w are two distinct vectors and m is the number of MVs between the two vectors. Thus, (v_{ i }- w_{ i }) is not computed if v_{ i }and/or w_{ i }is a missing value
An index for clustering results comparison: Conserved Pairs Proportion (CPP)
To assess the influence of missing data rates and different replacement methods into clustering results, we have analysed the co-associated genes of an original dataset (without MVs) compared to these genes location in a set with MVs.
Hence, we realized in a first step the clusterings with the data sets without MV by each aggregative clustering algorithm. The results obtained by these first analyses are denoted reference clusterings (RC). In a second step, we generated MVs in data. The MVs are replaced by using the different replacement methods. Then we performed the hierarchical clustering for each new set. The results obtained by these second analyses are denoted generated clusterings (GC). We compared the resulting clusters defined in RC and GC. We assessed the divergence by using an index named Conserved Pair Proportions (CPP). The CPP is the maximal proportion of genes belonging to two clusters, one from the RC and the other one from the GC (cf. Figure 1).
- i)
For each reference clustering based on a given clustering algorithm (algo), we defined K^{ algo }, the number of clusters. As every type of hierarchical clustering algorithm gives a particular topology, we cannot use the same number of clusters to compare each aggregative method. So, we defined K^{ algo }such as its 10 most important cluster must represent 80% of the genes. For this purpose, we defined K^{ init }, an important initial number of clusters (equals to 500), and counted the number of occurrences associated to the 10 most populated clusters. Then we diminished K^{ init }by one unit and counted again. We stopped the process when the 10 most important clusters represent 80% of the occurrences (K^{ algo }= K^{ init }). We denote by the j^{ th }cluster for a given clustering algorithm with j = {1, ..., K^{ algo }}. The clusters are associated with their corresponding gene list .
ii) Three hierarchical clusterings are performed after generating MVs in proportion τ in the data, the first one without replacing data – in this case, the normalized Euclidean distance (Eq.2) is used -, the second one after estimating the missing data by the kNN method (Eq.1), and the third one after replacing the missing data by zero. For each resulting tree, K^{ algo }clusters are defined. The clusters are associated with their corresponding gene list , with j' = {1, ..., K^{ algo }}.
iii) Finally, to estimate the CPP index, we searched for each cluster the closest cluster. For each clustering algorithm (algo), the corresponding cluster is selected as the maximum number of genes from the gene list found in . Then, the Conserved Pairs Proportion (CPP) is computed as follow for one simulation:
In addition, a variation of the tree topology may induce a CPP-variation. If the remaining genes of a cluster are in the direct neighbour clusters, the use of CPP can bias the analysis. Thus, we characterized the CPP_{ f }ratio to consider the f closest clusters of the cluster. The computation of CPP_{ f }ratio is based on the previous ratio and corresponds to the f clusters which are the closest to the winning cluster. From a selected , the upper node of the dendrogram is examined. If the number of clusters linked to this node is inferior to f, the upper node is selected. This process is performed until the number of clusters is inferior or equals to f. The last node tested (i.e. with the number of clusters inferior or equal to f) is used to compute the CPP_{ f }ratio.
Declarations
Acknowledgements
The authors want to express their appreciation to Cristina Benros for her kind help in the course of this research. The authors thank their colleagues of Equipe de Bioinformatique Génomique et Moléculaire for fruitful discussions and Eric Molle and David Polverari from Atragene Bioinformatics for their technical assistance. This work was supported by grants from the Ministry for Research, Institute for Health and Medical Care (INSERM) and Genopole^{®}. AdB was supported by the Fondation de la Recherche Médicale.
Authors’ Affiliations
References
- DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control gene expression on a genomic scale. Science 1997, 278: 680–686. 10.1126/science.278.5338.680View ArticlePubMedGoogle Scholar
- Cho RJ, Mindrinos M, Richards DR, Sapolsky RJ, Anderson M, Drenkard E, Dewdney J, Reuber TL, Stammers M, Federspiel N, Theologis A, Yang WH, Hubbell E, Au M, Chung EY, Lashkari D, Lemieux B, Dean C, Lipshutz RJ, Ausubel FM, Davis RW, Oefner PJ: Genome-wide mapping with biallelic markers in Arabidopsis thaliana . Nature Genet 2003, 23: 203–207.Google Scholar
- Borevitz JO, Liang D, Plouffe D, Chang HS, Zhu T, Weigel D, Berry CC, Winzeler E, Chory J: Large-scale identification of single-feature polymorphisms in complex genomes. Genome Res 2003, 13: 513–523. 10.1101/gr.541303PubMed CentralView ArticlePubMedGoogle Scholar
- Liu CH, Ma WL, Shi R, Ou YQ, Zhang B, Zheng WL: Possibility of using DNA chip technology for diagnosis of human papillomavirus. J Biochem Mol Biol 2003, 36: 349–353.View ArticlePubMedGoogle Scholar
- Locke DP, Segraves R, Carbone L, Archidiacono N, Albertson DG, Pinkel D, Eichler EE: Large-scale variation among human and great ape genomes determined by array comparative genomic hybridization. Genome Res 2003, 13: 347–357. 10.1101/gr.1003303PubMed CentralView ArticlePubMedGoogle Scholar
- Everitt B: Cluster Analysis,. Heinemann Educ 1974.Google Scholar
- Hartigan JA, Wong MA: A k-means clustering algorithm,. Applied Statistics 1979, 28: 100–108.View ArticleGoogle Scholar
- Kohonen T: Self-organized formation of topologically correct feature maps. Biol Cybern 1982, 43: 59–69.View ArticleGoogle Scholar
- Kohonen T: Self-Organizing Maps,. 3 Edition Springer Verlag 2001.View ArticleGoogle Scholar
- Mardia KV, Kent JT, Bibby JM: Multivariate Analysis. Academic Press 1979.Google Scholar
- Lee S-I, Batzogolou S: Application of Independent Component Analysis to microarrays. Genome Biology 2003, 4: R76. 10.1186/gb-2003-4-11-r76PubMed CentralView ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95: 14863–14868. 10.1073/pnas.95.25.14863PubMed CentralView ArticlePubMedGoogle Scholar
- Quackenbush J: Computational analysis of microarray data. Nature genetics 2001, 418: 418–427.View ArticleGoogle Scholar
- Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression data. Bioinformatics 2001, 17: 309–318. 10.1093/bioinformatics/17.4.309View ArticlePubMedGoogle Scholar
- Gibbons FD, Roth FP: Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res 2002, 12: 1574–1581. 10.1101/gr.397002PubMed CentralView ArticlePubMedGoogle Scholar
- Schuchhardt J, Beule A, Malik A, Wolski H, Eickoff H, Lehrarch H, Herzel H: Normalization strategies for cDNA microarrays. Nucl Acids Res 2000, 28: E47. 10.1093/nar/28.10.e47PubMed CentralView ArticlePubMedGoogle Scholar
- Tu Y, Stolovitzky G, Klein U: Quantitative noise analysis for gene expression microarray experiments. Proc Natl Acad Sci USA 2002, 99: 14031–14036. 10.1073/pnas.222164199PubMed CentralView ArticlePubMedGoogle Scholar
- Troyanskaya OG, Cantor M, Sherlock G, Brown PO, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17: 520–525. 10.1093/bioinformatics/17.6.520View ArticlePubMedGoogle Scholar
- Oba S, Sato M-A, Takemasa I, Monden M, Matsubara K-I, Ishii S: A Bayesian missing value estimation method for gene expression profile data,. Bioinformatics 2003, 19: 2088–2096. 10.1093/bioinformatics/btg287View ArticlePubMedGoogle Scholar
- Zhou X, Wang X, Dougherty ER: Missing-value estimation using linear and non-linear regression with Bayesian gene selection. Bioinformatics 2003, 19: 2302–2307. 10.1093/bioinformatics/btg323View ArticlePubMedGoogle Scholar
- Bohen SP, Troyanskaya OG, Alter O, Warnke R, Botstein D, Brown PO, Levy R: Variation in gene expression patterns in follicular lymphoma and the response to rituximab. Proc Natl Acad Sci USA 2003, 100: 1926–1930. 10.1073/pnas.0437875100PubMed CentralView ArticlePubMedGoogle Scholar
- Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lonning PE, Brown PO, Borresen-Dale AL, Botstein D: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003, 100: 8418–8423. 10.1073/pnas.0932692100PubMed CentralView ArticlePubMedGoogle Scholar
- Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, van de Rijn M, Rosen GD, Perou CM, Whyte RI, Altman RB, Brown PO, Botstein D, Petersen I: Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA 2001, 98: 13784–13789. 10.1073/pnas.241500798PubMed CentralView ArticlePubMedGoogle Scholar
- Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403: 503–511. 10.1038/35000501View ArticlePubMedGoogle Scholar
- Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 2000, 11: 4241–4257.PubMed CentralView ArticlePubMedGoogle Scholar
- Ogawa N, DeRisi J, Brown PO: New components of a system for phosphate accumulation and polyphosphate metabolism in Saccharomyces cerevisiae revealed by genomic expression analysis. Mol Biol Cell 2000, 11: 4309–4321.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferea TL, Botstein D, Brown PO, Rosenzweig RF: Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci USA 1999, 96: 9721–9726. 10.1073/pnas.96.17.9721PubMed CentralView ArticlePubMedGoogle Scholar
- Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9: 3273–3297.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu L, Hawkins DM, Ghosh S, Young SS: Robust singular value decomposition analysis of microarray data. Proc Natl Acad Sci USA 2003, 100: 13167–13172. 10.1073/pnas.1733249100PubMed CentralView ArticlePubMedGoogle Scholar
- Tilstone C: Vital Statistics. Nature 2003, 424: 610–612. 10.1038/424610aView ArticlePubMedGoogle Scholar
- Yeung KY, Medvedovic M, Bumgarner RE: Clustering gene-expression data with repeated measurements. Genome Biology 2003, 4: R34. 10.1186/gb-2003-4-5-r34PubMed CentralView ArticlePubMedGoogle Scholar
- Nikkilä J, Törönen P, Kaski S, Venna J, Castren E, Wong G: Analysis and visualization of gene expression data using Self-Organizing Maps. Neural Networks 2002, 15: 953–966. 10.1016/S0893-6080(02)00070-9View ArticlePubMedGoogle Scholar
- Herrero J, Valencia A, Dopazo J: A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 2001, 17: 126–136. 10.1093/bioinformatics/17.2.126View ArticlePubMedGoogle Scholar
- Herrero J, Dopazo J: Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns. J Proteome Res 2002, 1: 467–470. 10.1021/pr025521vView ArticlePubMedGoogle Scholar
- Hsu A, Tang SL, Halgamuge S: An unsupervised hierarchical dynamic self-organising approach to cancer class discovery and marker gene identification in microarray data. Bioinformatics 2003, 19: 2131–2140. 10.1093/bioinformatics/btg296View ArticlePubMedGoogle Scholar
- Gollub J, Ball CA, Binkley G, Demeter J, Finkelstein DB, Hebert JM, Hernandez-Boussard T, Jin H, Kaloper M, Matese JC, Schroeder M, Brown PO, Botstein D, Sherlock G: The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res 2003, 31: 94–96. 10.1093/nar/gkg078PubMed CentralView ArticlePubMedGoogle Scholar
- Ihaka R, Gentleman R: A Language for Data Analysis and Graphics. J Comput Graph Stat 1996, 5: 299–314.Google Scholar
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