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Microarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.
We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that k-means approach is more efficient to conserve gene associations.
More than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The EM_array approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.
Numerous genomes from species of the three kingdoms are now available [1,2]. A major current aim of biological research is to characterize the function of genes, for instance their cellular regulation pathways and implications in pathology [3-7]. High-throughput analyses (e.g., Microarrays) combined with statistical and bioinformatics data analyses are necessary to decipher such complex biological process [8,9]. Microarrays technologies allow the characterization of a whole-genome expression by measuring the relative transcript levels of thousand of genes in one experiment [10,11]. For instance, their relevancies were proved for the classification/identification of cancer subtype or diseases [12-17].
However, technical limitations or hazards (dust, scratches) lead to corrupted spots on microarray [18]. During the image analysis phase, corrupted or suspicious spots are filtered [11], generating missing data [18]. These missing values (MVs) disturb the gene clustering obtained by classical clustering methods, e.g., hierarchical clustering [19], k-means clustering [20], Kohonen Maps [21,22] or projection methods, e.g., Principal Component Analysis [23]. In practice, three different options can be considered. The first method leads to the elimination of genes, i.e., information loss [5]. The eliminated genes may be numerous and among them some may be essential for the analysis of the studied mechanism [24]. The second method corresponds to the replacement by zero [13]; it brings up a different problem in the analysis. Indeed, real data close to 0 will be confused with the MVs. Thus to limit skews related to the MVs, several methodologies using the values present in the data file to replace the MVs by estimated values have been developed [25].
The most classical method to estimate these values is the k-nearest neighbours approach (kNN), which computes the estimated value from the k closest expression profiles among the dataset [26]. This approach was applied to DNA chips by Troyanskaya and collaborators [27] and rapidly became one of the most popular methods. Since this pioneer study, more sophisticated approaches have been proposed, like Sequential kNN (SkNN) [28].
Simple statistical methods have been also proposed as the Row Mean [29]/Row Average method [28], or approaches based on the Expectation Maximisation algorithm (EM), e.g., EM_gene and EM_array [29]. Principle of least square (LS) has been also widely used, e.g., LSI_gene, LSI_array, LSI_combined and LSI_adaptative [29]. Kim and co-workers have extended the Least Square Imputation to Local Least Square Imputation (LLSI) [28]. However this method is only based on the similarity of genes for estimating the missing data. Others more sophisticated methods like the Bayesian Principal Component Analysis (BPCA) [30] combines a principal component regression, a Bayesian estimation and a variational Bayes (VB) algorithm.
The MVs replacement in microarrays data is a recent research field and numerous new and innovative methodologies are developed. We can noticed the work of Bar-Joseph et al. who described a model-based spline fitting method for time-series data [31] and Schliep et al. who used hidden Markov models for imputation [32]. Tuikkala and co-workers have investigated the interest to use GO annotation to increase the imputation accuracy of missing values [33] as Kim et al. [34]. Hu et al. and Jörnsten et al. have incorporated information from multiple reference microarray dataset to improve the estimation [35,36], while Gan co-workers takes into consideration the biological characteristics of the data [37]. Hua and Lai did not propose a new method, but assess the quality of imputation on the concordance of gene prioritization and estimation of true/false positives [38].
In addition we can list the following relevant methodologies applied in MVs replacement for microarray analysis: Support Vector Regression [39], Factor Analysis Regression [40], Ordinary Least Square Impute [41], Gaussian Mixture Clustering [42], LinCmb [43], Collateral Missing Value Estimation [44], Linear based model imputation [45], Dynamic Time Warping [46] or iterative kNN [47,48].
In a previous study, we estimated the influence of MVs on hierarchical clustering results and evaluated the effectiveness of kNN approach [49]. We observed that even a low rate of missing data can have important effects on the clusters obtain by hierarchical clustering methods. Recently, this phenomenon was confirmed by Wong and co-workers for other particular clustering methods [50].
Since our work, numerous replacement methods (see Table 1 and previous paragraphs) have been developed to estimate MVs for microarray data. Most of the time, the new approaches are only compared to kNN. In this study, we decided to evaluate the quality of MV imputations with all usable methods, and their influence on the quality of gene clustering. The present paper undertakes a large benchmark of MVs replacement methods to analyze the quality of the MVs evaluation according to experimental type (kinetic or not), percentage of MVs, gene expression levels and data source (Saccharomyces cerevisiae and human).
Table 1. Different missing values replacement methods.
General principle
Figure 1 shows the general principle of the analysis. From the initial gene expression datasets, the series of observations with missing values are eliminated to create a Reference matrix. Then simulated missing values are generated for a fixed τ percentage and are included in the Reference matrix. In a second step, these simulated missing values are imputed using the different available methods. Difference between the replaced values and the original true values is finally evaluated using the root mean square error (RMSE) (see Methods). In this work, we chose 5 microarray datasets, very different one from the other, i.e., coming from yeasts and human cells, and with or without kinetics (see Table 2). The idea was to have the broadest possible vision types of expression data [see Additional file 1 for more details [49,51-54]].
Figure 1. Principle of the method. The initial data matrix is analyzed. Each gene associated to at least one missing value (in pink) is excluded given a Reference matrix without any missing value. Then missing values are simulated (in red) with a fixed rate τ. This rate τ goes from 0.5% to 50% of missing values by step of 0.5%. 100 independent simulations are done each time. Missing values are then imputed (in blue) for each simulations by the selected methods. RMSE is computed between the estimated values of missing values and their true values.
Table 2. The different datasets used
Additional file 1. Dataset details.
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Our goals were also (i) to evaluate methods that experimental scientists could use without intervention, (ii) to select only published methods, and (iii) to analyse influence of the gene clusters. Indeed, some studies have been done to compare numerous methods, e.g., [55], but does not go through the clustering; while less frequent researches goes through the clustering, but test only a limited number of imputation methods as [56]. We so have searched all kinds of published imputation methods with available dedicated softwares or codes, whenever the Operating System, language or software. From this search, we selected 12 available replacement methods, which were compatible with high-throughput computation. Others methods had not been used due to the unavailability of the program despite the indication in the corresponding papers or to impossibility to modify the source code to used our microarrays data.
Error rate for each replacement method
Figure 2 shows the dispersion of expected and true values, for three given imputation methods. On one hand, kNN and EM_gene approaches exhibit a high dispersion between expected and true values; the correlations R equal respectively 0.33 and 0.32 (see Figures 2a and 2b). On the other hand EM_array approach presents a highly better agreement with a R value of 0.97 (see Figure 2c). Figure 3 shows the evolution of RMSE values for τ ranging between 0.5 and 50% using the two datasets GHeat and OS. These two examples are good illustrations of the different behaviours observed with the different replacement methods. Some have initial high RMSE values and remains quite consistent, while others have lower initial RMSE values but are very sensitive to an increased rate of MVs. Moreover, performances for the different methods appeared to be dependant of the used dataset.
Figure 2. Example of three methods. Distribution of predicted values (y-axis) in regards to true values (x-axis). Estimation of the missing values has been done (a) by kNN approach, (b) EM_gene and (c) EM_array. The dataset used is the Bohen set with τ values ranging from 0.5% to 50% of missing values with a step of 0.5. 10 independent simulations have been done for each τ value.
Figure 3. Missing value imputation. RMSE value for (a) GHeat subset and (b) for OS for rate of missing value going from 0.5% to 50% by step of 0.5%. (b) 100 independent simulations are done at each level.
EM_gene[29]: This method is always associated to very high RMSE values, which range in an interval
from 0.6 to 0.7 for a rate τ ranging from 0.5 to 3.0% (see Figure 3b) and decrease for values from 0.30 to 0.40. Such a curved profile is observed for
the datasets OS and GH2O2 (see Figure 3a). For the other dataset, RMSE increases as expected (see Figure 3a), but is always associated to high RMSE values.
kNN[27]: Its RMSE values for all six data files always range between 0.3 and 0.4. The increase
of τ only affects slightly the kNN approximation, at most 0.05 for the datasets B and OS. This constancy of RMSE values
implies that for high rates of missing data (more than 20% of missing data) the RMSE
values remain acceptable.
SkNN[28]: Despite the fact that SkNN is an improvement of kNN, their RMSE values are surprisingly always higher than the one of kNN (from 0.01 to 0.08). Only with the dataset B, SkNN performs slightly better than kNN (RMSE difference of 0.076).
LLSI[57]: The average RMSE values of LLSI ranges mainly from 0.34 to 0.41 for most of the dataset. Its performance could be
considered as median and its effectiveness is close to the LSI_gene method. Its RMSE values increase gradually with the increase of τ, i.e., 0.1 from 0.5 to 50% of missing data. It is the less efficient method based on least
square regressions. However for the dataset L, this method is the most powerful after
the LSIs methods (see below).
LSI_gene[29]: The effectiveness of LSI_gene is slightly affected by the increase in the percentage of missing data. For each data
file, the values of RMSE range between 0.3 and 0.4. These results are close to those
observed for methods LLSI and kNN, i.e., methods giving of the medium results ranging between the best (LSI_array) and the less efficient methods (EM_gene).
Row Mean[29] and Row Average [57]: Low RMSE values are observed for L (0.23) and B (0.28) datasets. Only for dataset
GHeat, the RMSE value is high (0.54). Strikingly this method shows equivalent and
or better results than more elaborated approaches.
BPCA[30]: For the OC, OS and GH2O2 datasets, and for τ comprises in the range 0.5 to 10-15% of missing data, BPCA appears to have one of the lowest RMSE values [see Additional file 2], only bypass by two other approaches. This method is powerful for low rates of missing
values. However it should be noted that the efficiency of BPCA is strongly reduced when the rate of missing data increases. This is particularly
notable in the case of the GHeat dataset. The values of the RMSE increases from 0.2
to 1.1 (see Figure 3a). For a τ value higher than 30%, BPCA performs worst than most of the imputation methods. This observation is less striking
for the other datasets. For B and OS datasets, RMSE values increase by a maximum of
0.1 for τ increasing from 0.5 to 50%. It is a good illustration of the dataset specificity
related to the quality of the imputation methods.
Additional file 2. RMSE of OS with BPCA imputing method. RMSE value for OS for rate of missing value going from 0.5% to 20% by step of 0.5% with the L dataset.
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- LSI_array, LSI_combined, LSI_adaptative and EM_array
Table 3. Pairwise comparison of imputation method.
The different datasets influence the quality of the imputation
Table 4 shows the average RMSE values for each imputation methods. They are given as the average of all the simulations ranging from τ = 0.5 to 50% (50,000 independent simulations per imputation method). This table highlights the differences that were observed between the datasets. Nonetheless, it allowed us to rank the methods in term of efficiency. Roughly, we could identify three groups: The first one comprise four methods (EM_array, LSI_array, LSI_combined and LSI_adaptative) for which small RMSE values were always observed (EM_array always exhibited the best performances); (2) the second group comprised 4 methods, i.e., BPCA, Row Mean, LSI_gene and LLSI; (3) and finally the third group, which can be considered as the last group, comprised three methods, i.e., kNN, SkNN and EM_gene.
Table 4. Mean RMSE value for the different datasets
Notably, this order depends on the dataset, but still the changes are often limited. For instance, EM_gene performs better than kNN and SkNN for B dataset, but does not perform better than the others. Strong changes could be noted for OS that allows SkNN to be better than LLSI and LSI_gene. Nonetheless, it is mainly due to the poor quality of the estimation of these two methods with this dataset. For the L dataset, we observed that LLSI method performs well and remains better than other LSIs and EM_array methods. GHeat dataset that is associated to the highest average RMSE values has strong particularities as (i) kNN performs better than BPCA, Row Mean, LSI_gene and LLSI, and (ii) BPCA and Row Mean performs poorly compared to other methods, being only slightly better than EM_gene. Hence, it appears that GHeat is a more difficult dataset to impute.
Extreme values
The same methodology was followed to analyze the extreme values, i.e., 1% of the microarray measurements with the highest absolute values. They have major biological key role as they represent the highest variations in regards to the expression reference [see Additional file 3]. Figure 4 presents similar examples to these of Figure 3, but this time, only extreme values were used in the analysis. Thus, the percentage of missing values τ can be differently apprehend, i.e., τ = 10% corresponds to 10% of the extreme missing values, so 0.1% of the values of the dataset. At one exception, all the replacement methods decrease in effectiveness for the estimate of the extreme values. Performance of the methods also greatly depends on the used dataset and especially -in agreement with previous observation - in the case of the GHeat dataset. A description of the behaviour of each method is presented in Additional file 3. kNN [27] is the less powerful method in most of the case (see Figures 4a and 4b). Its average RMSE value is often 0.5 higher than the second poorest imputation method. Interestingly, in the case of the extreme values, SkNN improved greatly. EM_gene [29] remains one of the less powerful methods for the imputation of missing values. LLSI [57] method effectiveness remains similar compared to the other methods of its group. Row Mean [29] and Row Average [57] have RMSE values increased by 0.2 to 0.4 for the yeast dataset, which is correct in regards to other methods (see Figures 6). Their efficiencies are median compared to the other methods. BPCA [30] has a correct behaviour. But contrary to most of them, it is very sensitive to the datasets. LSI_gene [29] has the lowest RMSE values observed after EM_array, LSI_array, LSI_combined and LSI_adaptative. This result shows that LSIs, whatever the specificity of their implementations, are effective to impute the values missing.
Figure 4. Extreme values (representing 1% of the missing values). Evolution of RMSE according to τ ranging (a) from 0.5% to 30% of the extreme values for the Bohen dataset and (b) from 0.5% to 50% of the extreme values) for the Ogawa dataset.
Additional file 3. Extreme values. Distribution of the values observed in OS dataset. The extreme values are highlighted on each size of the histogram.
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EM_array method is again the most performing method (see previous section). Its RMSE values are almost identical to the ones previously computed. LSI_array, LSI_combined and LSI_adaptative are slightly less efficient than previously seen. Thus, the clustering we have proposed remains pertinent when only the extreme values are implicated. LSI_array, LSI_combined, LSI_adaptative and EM_array are always good, and the less efficient methods can be associated now to considerable RMSE values. Noticeably, kNN efficiency collapses and the influence of datasets on the imputation quality is sharpened.
Clustering in question
A critical point in the analysis of DNA data is the clustering of genes according to their expression values. Missing values have an important influence on the stability of the gene clusters [49,58]. Imputations of missing values have been used both to do hierarchical clustering (with seven different algorithms) and k-means [20] (see Methods).
Figure 5a shows the Cluster Pair Proportions (CPP, [49] see Methods section) of OS using hierarchical clustering with complete linkage, average linkage, McQuitty and Ward algorithm. CPP values of average linkage ranges between 78 and 68%, those of McQuitty between 58 and 45%, those of Ward between 57 and 35% and finally those of complete linkage between 50 and 41%. We obtain for the 7 hierarchical clustering algorithms the same behaviours than previously observed [49]: ranging from high CPP values for single linkage to low CPP values for Ward. This observation can be explained by the topology given by each algorithm, e.g., Ward gives well equilibrated clusters whereas single linkage creates few major clusters and numerous adjacent singletons.
Figure 5. CPP of hierarchical clustering approach algorithm. (a) with complete, average, ward and McQuitty algorithm for OS with kNN and (b) with Ward algorithm for Ogawa dataset for the different imputation methods.
For every hierarchical clustering methods the CPP values are different, but the general tendencies remain the same: (i) imputation of small rate τ of MVs has always a strong impact on the CPP values, and (ii) the CPP values slowly decreased with the increased of τ. Between 0.5 and 3% of MVs and the CPP values decrease by 1 to 3% per step of 0.5% of MVs. From τ equals 3.5 to 20% of MVs, the values of CPP decrease overall by 10%. For higher rate of MVs the decreasing of CPP is slower. This loss of stability is present in the case of the k-means method and for each type of hierarchical classification (except for the methods single linkage and centroid linkage, due to the building of the clusters).
Individual evaluation of the methods highlights the lack of efficiency of the EM_gene imputation method; it obtains always the lowest CPP values, i.e., 1.37 to 5.34% less than other approaches. At the opposite, EM_array, LSI_array, LSI_combined and LSI_adaptative are associated to the highest CPP values. In the case of the methods with a median efficiency, e.g., Row_Mean, their CPP values could be assigned as median compared to the values of the other methods. Figure 5b shows the particular example of OS dataset. CPP values of BPCA (average value equals 42.6%) are close to the most powerful methods (42.8% for the four methods). Moreover, in the classical range of τ less than 20%, it is the best. As seen in Table 4, BPCA is one of the best approaches for this dataset. Hence, common trends can be found between the quality of the imputation method and the gene cluster stability.
In addition, evaluation of imputation methods shows that the cluster quality depends on the dataset. For instance, with the dataset OS, imputation of missing values with kNN method gives an average CPP value (for the Ward algorithm) that equals 42.9%, while the average CPP values for all the other methods only equals 40.6% whereas its RMSE value is one of highest (see Table 4). The CPP differences are mainly bellow 5%. These results show that an improvement has been obtained since last study. Nonetheless, no new approaches had drastically improved the quality of the clustering. Interestingly, k-means approach had similar tendencies, underlining that this low improvement is not due to hierarchical clustering.
Another question is the comparison between hierarchical clustering algorithms and k-means. Nonetheless, comparison only between hierarchical clustering algorithms is already a difficult task. Comparison with k-means is so more difficult. Indeed, the use of the same number of clusters to compare the hierarchical clustering algorithms with k-means can leads to a wrong conclusion. Indeed, for an equivalent number of clusters, most of the CPP values of k-means are lower than CPP values obtained with hierarchical clustering algorithms. However, it is only due to the dispersion of observations within the clusters obtained by k-means approach. Thus, to have an unbiased comparison, the dispersion of genes within cluster between k-means and hierarchical clustering algorithms must be computed. It had been done, as previously described [49]. Following this approach, Ward and complete linkages were defined as the best approaches to assess an unbiased comparison. They have both CPP values lower than k-means CPP values. The differences were often higher than 5% underlining the interest of k-means approach to cluster gene expression profiles.
Distribution of the observations
When index CPP is calculated, only one group is taken into account. To go further, we used another index, named CPPf that allows to take into account the five closer groups, and to check the pairs of genes remaining joint partners. The values of CPPf are higher than those of the CPP, e.g., 20% for the Ward. Methods associated to high CPP values have also high CPPf values, while methods with low CPP values have also a lower CPPf values. These weak variations shows that often a part of the observations, not associated to the original cluster could be find in its vicinity. These results are entirely in agreement with our previous results [49]. It shows here that the novel imputation methods have not permit to get closer related genes with better improvement.
The analysis of associations could also take into account the non-associations. For this purpose, Clustering Agreement Ratio (CAR, see Methods section) has been used which considers both associated and non-associated genes. CAR values are higher than the one of the CPP due to the calculation of the pairs of genes remaining dissociated. Indeed, it is more probable than the genes are dissociated than associated according to the number of treated genes and the number of generated groups. For the OS dataset, the highest values of the index CAR concerns Ward classification and are ranging between 88.2 and 91.2%. For the GHeat dataset, it ranges between 91.0 and 94.1%. Complete linkage, average linkage and McQuitty have lower CAR values (80%). For k-means classification, the values are higher 1 to 2% compared to Ward classification, 10% better than McQuitty and Complete linkage and 13% to average linkage. This results underlines that K-means allows so a better stability of gene clusters.
Since our previous analysis [49], numerous new MVs imputation methods have been proposed. Some appeared to be true improvements in regards to the computation of RMSE. In particular, EM_array is clearly the most efficient methods we tested. For τ < 35%, it is the best imputation method for 60% of the values, and for τ > 35%, in 80%. This feature was confirmed by the analysis of extreme values. LSI_array, LSI_combined and LSI_adaptative follow closely the efficiency of EM_array. We have unsuccessfully tried to combine these four different methods to improve the RMSE values. No combination performs better than EM_array.
We can underline four interesting points:
i. As expected, the imputation quality is greatly affected by the rate of missing data, but surprisingly it is also related to the kind of data. BPCA is a perfect illustration. For non-kinetic human dataset, MVs estimations were correct, whereas for the GHeat dataset the error rate appeared to be more important.
ii. The efficiency of Row_Mean (and Row_Average) is surprisingly good in regards to the simplicity of the methodology used (with the exception of GHeat dataset).
iii. Even if kNN is the most popular imputation method; it is one of the less efficient, compared to other methods tested in this study. It is particularly striking when analyzing the extreme values. SkNN is an improvement of kNN method, but we observed that RMSE values of SkNN were not better than ones of kNN. It could be due to the use of non-optimal number of neighbours (k), as for kNN. It must be noticed we used kopt defined by [27], this choice has a direct impact on the imputation values.
Extreme values are the ones that are the most valuable for the experiments. The imputation of extreme value missing data shows that -except for EM_array- the effectiveness of all the methods is affected.
Our results are so in good accordance with the results of Brock and co-workers [55] who found that methods from Bo and co-workers [29], Kim and co-workers [57] and Oba and co-workers [30] are highly competitive. However, they consider "that no method is uniformly superior in all datasets" [29]. Our results are simpler to summarize as we observe -thanks to our distance criteria- a grading between the effectiveness of the methods. LLSI of Kim and co-workers [57] has a correct behavior for all datasets while BPCA of Oba and co-workers [30] is strongly dependant of the dataset. At the opposite, the methods implemented by Bo and co-workers [29] remain the most efficient in all cases. Moreover, some implemented methods of Bo and co-workers [29] have not been tested by [55], but are the most efficient. All these results are reinforced by the analyses of extreme value imputations.
An important point must be not forgotten, we have, as the other authors, e.g., [24,55,56], used the entire dataset, i.e., no specific selection of interesting profile gene had been done. It could have importance in terms of quality of the imputation values and consequence on the clustering.
A strong assumption of the microarray data analysis is that genes with similar expression profiles are likely to be co-regulated and thus involved in the same or similar biological processes. Different types of clustering and classification methods have been applied to microarray data, e. g., some classical as k-means clustering [20], self-organizing maps [21,22,59], hierarchical clustering [19,60], Self Organizing Tree Algorithm [61-63], and some dedicated approaches as DSF_Clust [64], re-sampling based tight clustering [65], cluster affinity search technique [66], multivariate Gaussian mixtures [67], model-based clustering algorithms [68,69], clustering of change patterns using Fourier coefficients [70], Nearest Neighbor Networks [71], Fuzzy clustering by local Approximation of membership [72] or Multi-Dimensional Scaling [73].
Given one particular dataset, different clustering algorithms are very likely to generate different clusters [74]. This is true when large-scale gene expression data from microarrays are analyzed [58,75,76]. Comparison of different clusters even obtained with the same classification approach is still a difficult task [see Additional file 4[69,77-79]]. Thus, to assess the relevance of missing value imputation methods, we observed the behaviours of different hierarchical clustering methods and k-means clustering using CPP, CPPf [49] and newly introduce CAR index. Results follow exactly the observations done on RMSE values (see previous section). Only one method seems ambiguous: kNN. Indeed, its CPP and CPPf are higher than expected. It is mainly due to the selection of the genes in the different datasets. We have decided at the beginning to not discard any genes, i. e., we have absolute no a priori. Thus very flat profiles have been conserved and empower kNN that prefers to predict values closer to zero than the other methods (see Figure 4 of [49]). It generates clusters with lot of zero, these clusters are so stable. For the majority of the methods, the order of effectiveness of the methods for the maintenance of stability within the groups between various classifications is identical. Combination of CPP, CPPf and CAR index underlines the interest of k-means clustering in regards to hierarchical clustering methods. For comparable clusters, k-means gives better values.
Additional file 4. Comparing clustering algorithms.
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Wang and co-workers does not found a strong difference between the three imputation methods they used, i.e., kNN, BPCA and LLS, in the classification performance [24]. The only comparable extensive study has been done by Tuikkala and co-workers [56], they have focussed interestingly on the GO term class and use k-means. They have tested six different methods with less simulation per missing value rates and less missing value rates. But, the important point is they have not tested the methods found the most efficient by our approach. We also slightly disagree with their conclusion about the quality of BPCA [56]. It can be easily understand as only a very limited number of clusters has been tested (5 clusters); in our case, we have supervised the choice of cluster numbers (see Method section), leading to a higher number of clusters. This higher number is so more sensitive to the quality of clustering. It must be noticed we have used Euclidean distance and not Pearson correlation, it was mainly to (i) stay consistent with our previous research, and (ii) as we have not filtered the data, Pearson correlation could have aggregated very different profiles. As the time computation was very important, it was not possible to test the two possibilities.
The DNA microarrays generate high volume of data. However they have some technical skews. Microarrays studies must take into account the important problem of missing values for the validity of biological results. Numerous methods exist to replace them, but no systematic and drastic comparisons have been performed before our present work. In this study, we have done more than 6.000.000 independent simulations, to assess the quality of these imputation methods. Figure 6 summarizes the results of our assessment. The method EM_array, LSI_array, LSI_combined and LSI_adaptative are the most performing methods. BPCA is very effective when the rate of missing values is lower than 15%, i.e., for classical experiments. The values estimated by the Row_Mean are quite correct in regards to the simplicity of the approach. kNN (and SkNN) does not give impressive results, it is an important conclusion for a method used by numerous scientists. The methods LSI_gene and EM_gene are not effective but they are to be tested with data files made up of little of genes and a great number of experiments. These conclusions are to be taken carefully because the quality of the imputations depends on the used datasets.
Figure 6. Summary of the comparison.
A major disadvantage of numerous methods is their accessibilities. We have tested here only a part of the methods as some are unavailable and others had not worked properly. Some methods used here could not be used easily by a non-specialist. It could be interesting so to have implementation of all the different methods in a useful manner with the standardized input and output file format. In the second time, graphic interfaces for the methods could be helpful. These remarks are particularly relevant in regards to recent papers that proposed novel approaches as SLLSimpute [80] or interesting comparison [55,56] that do not compare with the methods that had been considered as the most efficient in this study.
We used 5 data sets for the analysis [see Additional file 1]; they were mainly coming from the SMD database [81]. The first one, named 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 [51]. The second one corresponds to various environmental stress responses in S. cerevisiae [52]. This set, named 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. Bohen and co-workers have analyzed the patterns of gene expression in human follicular lymphomas and the interest of treatment by rituximab [53]. This dataset is composed of N = 16.523 genes and n = 16 experimental conditions. The last dataset has been obtained by Lucau-Danila, Lelandais and co-workers [54]. To precisely describe the very early genomic response developed by yeast to accommodate a chemical stress, they performed a time course analyses of the yeast gene expression which follows the addition of the antimitotic drug benomyl. The dataset is a kinetic that comprised N = 5.621 genes for n = 6 kinetic time (30 seconds, 2, 4, 10, 20 and 40 minutes).
Datasets refinement: missing values enumeration
From the original datasets, 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 have been noted GHeat and GH2O2. They correspond to specific stress conditions as described previously. GHeat and GH2O2 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 smaller sets corresponding to 1/7 of OS, GS, GHeat and GH2O2. Principles are described in [49]. For the dataset of Bohen et al. (noted B), we have done the same protocol and used a subset representing 1/7 of B, i.e., N = 861 genes. For the dataset of Lucau-Danila et al.: [54], 11.4% of the genes have at least one missing values. The dataset with no missing values (noted L) was so composed of N = 4645 genes.
Missing values generation
From the sets without MVs, we introduced a rate τ of genes containing MVs (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. It must be notices that contrary to our previous work, each gene could contain more than one MV [49].
Replacement methods
The different packages have been downloaded from the authors' websites (see Table 1). kNN has been computed using the well-known KNNimpute developed by Troyanskaya and co-workers [27]. The determined kopt value is associated with a minimal global error rate as defined by Troyanskaya and co-workers [27]. BPCA was used without its graphical interface [30] as for the Bo et al. package (Java) [29]. For LLSI and Row_Average, we have modified the original Matlab code to use our own microarray datasets [57]. SkNN was performed with R software [28].
Hierarchical Clustering
The hierarchical clustering (HC) algorithm allows the construction of a dendogram of nested clusters based on proximity information [19]. The HC have been performed using the "hclust" package in R software [82]. Seven hierarchical clustering algorithms have been tested: average linkage, complete linkage, median linkage, McQuitty, centroid linkage, single linkage and Ward minimum variance [83].
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 MVs:
v and w are two distinct vectors and m is the number of MVs between the two vectors. Thus, (vi - wi) is not computed if vi and/or wi 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 (see Figure 1), we have analysed the co-associated genes of an original dataset (without MVs) compared to these genes location in a set with MVs. A similar approach has been used by Meunier et al. on proteomic data [84].
Hence, we realized in a first step the clustering with the data sets without MV by each aggregative clustering algorithm. The results obtained by these first analyses are denoted reference clustering (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 clustering (GC).We compared the resulting clusters defined in RC and GC and 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 of [49] and Additional file 5 for more details).
Additional file 5. Details of CPP and CPPf.
Format: DOC Size: 45KB Download file
This file can be viewed with: Microsoft Word Viewer
Clustering Agreement Ratio (CAR)
The Clustering Agreement Ration (CAR) is the concordance index measuring the proportion of genes pairs, either belonging to a same cluster (resp. different clusters) in the reference clustering (RC) and found again in a same cluster (resp. different clusters) in the clustering (GC) obtained without or after replacing the MVs.
The index CAR is defined by the following equation:
where and specify the co-presence of two genes in a same cluster, i.e., they take the value 1 when the genes i and j belong to a same cluster in the clustering RC and GC respectively. The numbers of pairs in G genes is G.(G - 1)/2. The first term of the numerator corresponds to the co-presence of the pair (i, j) in a same cluster for RC and GC, and, the second term the co-absence of this pair in a same cluster.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MC done all the computational and analysis works. AdB wrote the paper, conceived of the study and carried out the MVs generation. AM and GL participated in the design of the study and coordination. All authors read and approved the final manuscript.
We would like to thanks all the scientists who have deposited their experiments and make them freely available to the scientific community. In the same way, we would like to thanks all the scientists who have developed and distributed missing value replacement methods. This work was supported by grants from the Ministère de la Recherche, from French Institute for Health and Medical Research (INSERM), Université Paris Diderot - Paris 7, Institut National de Transfusion Sanguine (INTS) and Genopole®. Clustering Agreement Ration (CAR) was proposed by late Pr. Serge Hazout.
Liolios K, Tavernarakis N, Hugenholtz P, Kyrpides NC: The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide.
Nucleic Acids Res 2006, (34 Database):D332-334. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Bernal A, Ear U, Kyrpides N: Genomes OnLine Database (GOLD): a monitor of genome projects world-wide.
Nucleic Acids Res 2001, 29(1):126-127. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Muro S, Takemasa I, Oba S, Matoba R, Ueno N, Maruyama C, Yamashita R, Sekimoto M, Yamamoto H, Nakamori S, et al.: Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data.
Genome Biol 2003, 4(3):R21. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Perou CM, Sorlie T, Eisen MB, Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al.: Molecular portraits of human breast tumours.
Nature 2000, 406(6797):747-752. PubMed Abstract | Publisher Full Text
Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.
Bioinformatics 2005, 21(5):631-643. PubMed Abstract | Publisher Full Text
Imai K, Kawai M, Tada M, Nagase T, Ohara O, Koga H: Temporal change in mKIAA gene expression during the early stage of retinoic acid-induced neurite outgrowth.
Gene 2005, 364:114-122. PubMed Abstract | Publisher Full Text
Raab RM: Incorporating genome-scale tools for studying energy homeostasis.
Nutr Metab (Lond) 2006, 3:40. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Fellenberg K, Busold CH, Witt O, Bauer A, Beckmann B, Hauser NC, Frohme M, Winter S, Dippon J, Hoheisel JD: Systematic interpretation of microarray data using experiment annotations.
BMC Genomics 2006, 7:319. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Hoheisel JD: Microarray technology: beyond transcript profiling and genotype analysis.
Nat Rev Genet 2006, 7(3):200-210. PubMed Abstract | Publisher Full Text
DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale.
Science 1997, 278(5338):680-686. PubMed Abstract | Publisher Full Text
Clarke JD, Zhu T: Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives.
Plant J 2006, 45(4):630-650. PubMed Abstract | Publisher Full Text
Zhang W, Rekaya R, Bertrand K: A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer.
Bioinformatics 2006, 22(3):317-325. PubMed Abstract | Publisher Full Text
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.
Nature 2000, 403(6769):503-511. PubMed Abstract | Publisher Full Text
Pham T, Wells C, Crane D: Analysis of microarray gene expression data.
Current Bioinformatics 2006, 1(1):37-53. Publisher Full Text
Asyali MH, Colak D, Demirkaya O, Inan MS: Gene expression profile classification: A review.
Current Bioinformatics 2006, 1(1):55-73. Publisher Full Text
Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, et al.: Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma.
Cancer Res 2004, 64(19):6883-6891. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Gruzdz A, Ihnatowicz A, Slezak D: Interactive gene clustering - a case study of breast cancer microarray data.
Inf Syst Front 2006, 8:21-27. Publisher Full Text
Schuchhardt J, Beule D, Malik A, Wolski E, Eickhoff H, Lehrach H, Herzel H: Normalization strategies for cDNA microarrays.
Nucleic Acids Res 2000, 28(10):E47. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Hartigan JA, Wong MA: k-means.
Applied Statistics 1979, 28:100-115. Publisher Full Text
Kohonen T: Self-organized formation of topologically correct feature maps.
Biol Cybern 1982, 43:59-69. Publisher Full Text
Kohonen T: Self-Organizing Maps. 3rd edition. Springer; 2001.
Mardia K, Kent J, Bibby J: Multivariate Analysis. Academic Press; 1979.
Wang D, Lv Y, Guo Z, Li X, Li Y, Zhu J, Yang D, Xu J, Wang C, Rao S, et al.: Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules.
Bioinformatics 2006, 22(23):2883-2889. PubMed Abstract | Publisher Full Text
Grużdź A, Ihnatowicz A, Ślęzak D: Gene Expression Clustering: Dealing with the Missing Values.
Intelligent Information Processing and Web Mining 2005, 521.
Fix E, Hodges J: Discriminatory analysis, nonparametric discrimination: Consistency properties. In Technical Report 4, USAF School of Aviation Medicine. Randolph Field, Texas; 1951.
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays.
Bioinformatics 2001, 17(6):520-525. PubMed Abstract | Publisher Full Text
Kim KY, Kim BJ, Yi GS: Reuse of imputed data in microarray analysis increases imputation efficiency.
BMC Bioinformatics 2004, 5:160. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Bo TH, Dysvik B, Jonassen I: LSimpute: accurate estimation of missing values in microarray data with least squares methods.
Nucleic Acids Res 2004, 32(3):e34. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Oba S, Sato MA, Takemasa I, Monden M, Matsubara K, Ishii S: A Bayesian missing value estimation method for gene expression profile data.
Bioinformatics 2003, 19(16):2088-2096. PubMed Abstract | Publisher Full Text
Bar-Joseph Z, Gerber GK, Gifford DK, Jaakkola TS, Simon I: Continuous representations of time-series gene expression data.
J Comput Biol 2003, 10(3-4):341-356. PubMed Abstract | Publisher Full Text
Schliep A, Schonhuth A, Steinhoff C: Using hidden Markov models to analyze gene expression time course data.
Bioinformatics 2003, 19(Suppl 1):i255-263. PubMed Abstract | Publisher Full Text
Tuikkala J, Elo L, Nevalainen OS, Aittokallio T: Improving missing value estimation in microarray data with gene ontology.
Bioinformatics 2006, 22(5):566-572. PubMed Abstract | Publisher Full Text
Kim DW, Lee KY, Lee KH, Lee D: Towards clustering of incomplete microarray data without the use of imputation.
Bioinformatics 2007, 23(1):107-113. PubMed Abstract | Publisher Full Text
Hu J, Li H, Waterman MS, Zhou XJ: Integrative missing value estimation for microarray data.
BMC Bioinformatics 2006, 7:449. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Jornsten R, Ouyang M, Wang HY: A meta-data based method for DNA microarray imputation.
BMC Bioinformatics 2007, 8:109. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Gan X, Liew AW, Yan H: Microarray missing data imputation based on a set theoretic framework and biological knowledge.
Nucleic Acids Res 2006, 34(5):1608-1619. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Hua D, Lai Y: An ensemble approach to microarray data-based gene prioritization after missing value imputation.
Bioinformatics 2007, 23(6):747-754. PubMed Abstract | Publisher Full Text
Wang X, Li A, Jiang Z, Feng H: Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme.
BMC Bioinformatics 2006, 7:32. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Feten G, Almoy T, Aastveit AH: Prediction of missing values in microarray and use of mixed models to evaluate the predictors.
Stat Appl Genet Mol Biol 2005, 4:Article10. PubMed Abstract | Publisher Full Text
Nguyen DV, Wang N, Carroll RJ: Evaluation of Missing Value Estimation for Microarray Data.
Ouyang M, Welsh WJ, Georgopoulos P: Gaussian mixture clustering and imputation of microarray data.
Bioinformatics 2004, 20(6):917-923. PubMed Abstract | Publisher Full Text
Jornsten R, Wang HY, Welsh WJ, Ouyang M: DNA microarray data imputation and significance analysis of differential expression.
Bioinformatics 2005, 21(22):4155-4161. PubMed Abstract | Publisher Full Text
Sehgal MS, Gondal I, Dooley LS: Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.
Bioinformatics 2005, 21(10):2417-2423. PubMed Abstract | Publisher Full Text
Scheel I, Aldrin M, Glad IK, Sorum R, Lyng H, Frigessi A: The influence of missing value imputation on detection of differentially expressed genes from microarray data.
Bioinformatics 2005, 21(23):4272-4279. PubMed Abstract | Publisher Full Text
Tsiporkova E, Boeva V: Two-pass imputation algorithm for missing value estimation in gene expression time series.
J Bioinform Comput Biol 2007, 5(5):1005-1022. PubMed Abstract | Publisher Full Text
Bras LP, Menezes JC: Dealing with gene expression missing data.
Syst Biol (Stevenage) 2006, 153(3):105-119. PubMed Abstract
Bras LP, Menezes JC: Improving cluster-based missing value estimation of DNA microarray data.
Biomol Eng 2007, 24(2):273-282. PubMed Abstract | Publisher Full Text
de Brevern AG, Hazout S, Malpertuy A: Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering.
BMC Bioinformatics 2004, 5:114. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Wong DS, Wong FK, Wood GR: A multi-stage approach to clustering and imputation of gene expression profiles.
Bioinformatics 2007, 23(8):998-1005. PubMed Abstract | Publisher Full Text
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(12):4309-4321. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
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(12):4241-4257. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
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(4):1926-1930. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Lucau-Danila A, Lelandais G, Kozovska Z, Tanty V, Delaveau T, Devaux F, Jacq C: Early expression of yeast genes affected by chemical stress.
Mol Cell Biol 2005, 25(5):1860-1868. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Brock GN, Shaffer JR, Blakesley RE, Lotz MJ, Tseng GC: Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.
BMC Bioinformatics 2008, 9:12. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Tuikkala J, Elo LL, Nevalainen OS, Aittokallio T: Missing value imputation improves clustering and interpretation of gene expression microarray data.
BMC Bioinformatics 2008, 9:202. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Kim H, Golub GH, Park H: Missing value estimation for DNA microarray gene expression data: local least squares imputation.
Bioinformatics 2005, 21(2):187-198. PubMed Abstract | Publisher Full Text
Cox B, Kislinger T, Emili A: Integrating gene and protein expression data: pattern analysis and profile mining.
Methods 2005, 35(3):303-314. PubMed Abstract | Publisher Full Text
Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.
Proc Natl Acad Sci USA 1999, 96(6):2907-2912. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns.
Proc Natl Acad Sci USA 1998, 95(25):14863-14868. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Herrero J, Valencia A, Dopazo J: A hierarchical unsupervised growing neural network for clustering gene expression patterns.
Bioinformatics 2001, 17(2):126-136. PubMed Abstract | Publisher Full Text
Dopazo J, Carazo JM: Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree.
J Mol Evol 1997, 44(2):226-233. PubMed Abstract | Publisher Full Text
Yin L, Huang CH, Ni J: Clustering of gene expression data: performance and similarity analysis.
BMC Bioinformatics 2006, 7(Suppl 4):S19. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Fu X, Teng L, Li Y, Chen W, Mao Y, Shen IF, Xie Y: Finding dominant sets in microarray data.
Front Biosci 2005, 10:3068-3077. PubMed Abstract | Publisher Full Text
Tseng GC, Wong WH: Tight clustering: a resampling-based approach for identifying stable and tight patterns in data.
Biometrics 2005, 61(1):10-16. PubMed Abstract | Publisher Full Text
Ben-Dor A, Shamir R, Yakhini Z: Clustering gene expression patterns.
J Comput Biol 1999, 6(3-4):281-297. PubMed Abstract | Publisher Full Text
Qu Y, Xu S: Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.
Bioinformatics 2004, 20(12):1905-1913. PubMed Abstract | Publisher Full Text
Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL: Model-based clustering and data transformations for gene expression data.
Bioinformatics 2001, 17(10):977-987. PubMed Abstract | Publisher Full Text
Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression data.
Bioinformatics 2001, 17(4):309-318. PubMed Abstract | Publisher Full Text
Kim J, Kim H: Clustering of Change Patterns Using Fourier Coefficients.
Huttenhower C, Flamholz AI, Landis JN, Sahi S, Myers CL, Olszewski KL, Hibbs MA, Siemers NO, Troyanskaya OG, Coller HA: Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.
BMC Bioinformatics 2007, 8:250. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Fu L, Medico E: FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data.
BMC Bioinformatics 2007, 8:3. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Lelandais G, Vincens P, Badel-Chagnon A, Vialette S, Jacq C, Hazout S: Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms.
Bioinformatics 2006, 22(11):1359-1366. PubMed Abstract | Publisher Full Text
Datta S, Datta S: Evaluation of clustering algorithms for gene expression data.
BMC Bioinformatics 2006, 7(Suppl 4):S17. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus.
Nat Rev Genet 2006, 7(1):55-65. PubMed Abstract | Publisher Full Text
Handl J, Knowles J, Kell DB: Computational cluster validation in post-genomic data analysis.
Bioinformatics 2005, 21(15):3201-3212. PubMed Abstract | Publisher Full Text
Wu LF, Hughes TR, Davierwala AP, Robinson MD, Stoughton R, Altschuler SJ: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters.
Nat Genet 2002, 31(3):255-265. PubMed Abstract | Publisher Full Text
Thalamuthu A, Mukhopadhyay I, Zheng X, Tseng GC: Evaluation and comparison of gene clustering methods in microarray analysis.
Bioinformatics 2006, 22(19):2405-2412. PubMed Abstract | Publisher Full Text
Swift S, Tucker A, Vinciotti V, Martin N, Orengo C, Liu X, Kellam P: Consensus clustering and functional interpretation of gene-expression data.
Genome Biol 2004, 5(11):R94. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
Zhang X, Song X, Wang H, Zhang H: Sequential local least squares imputation estimating missing value of microarray data.
Comput Biol Med 2008, 38:1112-1120. PubMed Abstract | Publisher Full Text
Gollub J, Ball CA, Binkley G, Demeter J, Finkelstein DB, Hebert JM, Hernandez-Boussard T, Jin H, Kaloper M, Matese JC, et al.: The Stanford Microarray Database: data access and quality assessment tools.
Nucleic Acids Res 2003, 31(1):94-96. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
Ihaka R, Gentleman R: R: a language for data analysis and graphics.
J Comput Graph Stat 1996, 5:299-314. Publisher Full Text
Quackenbush J: Computational analysis of microarray data.
Nat Rev Genet 2001, 2(6):418-427. PubMed Abstract | Publisher Full Text
Meunier B, Dumas E, Piec I, Bechet D, Hebraud M, Hocquette JF: Assessment of hierarchical clustering methodologies for proteomic data mining.
J Proteome Res 2007, 6(1):358-366. PubMed Abstract | Publisher Full Text


text:
FORT WAYNE — Churubusco was defeated at the hands of two-time defending IHSAA Class 2A State Champion Fort Wayne Bishop Luers Friday.
The Knights took the opening kickoff and drove down the field to score in the first three minutes of the game.
Jaylon Smith capped the drive off with a seven-yard touchdown run to give the Knights a 7-0 lead.
It was the first of Smith's three touchdowns in the contest.
With 7:24 left in the second quarter, a one-yard TD run by Smith made it a 14-0 ball game.
The Columbia City Saturday Basketball League came to a close last weekend.

subset: commoncrawl


text:
Spec Ops: The Line - PChttp://www.gameinformer.com/games/spec_ops_the_line/b/pc/atom.aspxCommunity Server2011-11-22T09:01:00ZMultiplayer Trailer Pits Damned Against Exiles/games/spec_ops_the_line/b/pc/archive/2012/04/20/multiplayer-trailer-pits-damned-against-exiles.aspx2012-04-20T14:16:46Z2012-04-20T14:16:46Z

Up until now, 2k Games and Yager Entertainment have done their best to show us how they're showing the horrors of war in Spec Ops: The Line. Since the game also has a multiplayer component, now they're in the somewhat awkward position of having to convince everyone that it's also kind of fun. A new trailer highlights the game's two multiplayer factions, the Damned and the Exiled, and how they can use sand to their advantage.

The Line is set in a sandstorm-ravaged Dubai. The so-called Damned are soldiers who are standing behind Col. John Konrad, who was ordered to evacuate the city. The Exiles are former soldiers who have rebelled against his orders. As you can see in the trailer below, both factions can upgrade their abilities and use sand to overwhelm their enemies. Players will be able to unleash avalanches and waves of the gritty stuff to dominate multiplayer matches.

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Spec Ops: The Line is set to release on PlayStation 3, Xbox 360, and PC on June 26.

GIJeffhttp://www.gameinformer.com/members/GIJeff/default.aspxLatest Trailer Shows A Gritty Take On War/games/spec_ops_the_line/b/pc/archive/2012/04/05/latest-trailer-shows-a-gritty-take-on-war.aspx2012-04-05T13:38:52Z2012-04-05T13:38:52Z

From the outset, Spec Ops: The Line has been emphasizing its take on the horrors of war through frank depictions of torture and other carnage. Its latest trailer doesn't shy away from that sort of grim spectacle, either.

The game is set in a sandstorm-ravaged Dubai and focuses on the aftermath from those natural disasters. If the trailer below is any indication, you can abandon any hopes of seeing a lot of flag-waving, jingoistic patriotism.

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Keep an eye open for the game on PlayStation 3, Xbox 360, and PC on June 26. To learn more about Spec Ops: The Line's narrative setup, check out its story trailer.

GIJeffhttp://www.gameinformer.com/members/GIJeff/default.aspxSpec Ops: The Line Shoots Out New Screens/games/spec_ops_the_line/b/pc/archive/2012/02/06/spec-ops-the-line-shoots-out-new-screens.aspx2012-02-06T21:40:17Z2012-02-06T21:40:17Z

When Spec Ops got delayed, it seemed like its new release window was far in the distant future. However, we're approaching the targeted timeframe for 2K's military shooter, and these screens showcase how development is progressing.

The concept art and screens below highlight the action along with the setting of Dubai, and there are also a lot of shots clearly meant to convey the horror of war. Not exactly something I like looking at in my free time, but you go right ahead.

For more on Spec Ops' setting, watch this trailer. If you want extended gameplay footage, this video does the job.

GIJoehttp://www.gameinformer.com/members/GIJoe/default.aspxSpec Ops: The Line Extended Gameplay Footage/games/spec_ops_the_line/b/pc/archive/2011/12/06/spec-ops-the-line-extended-gameplay-footage.aspx2011-12-06T14:00:00Z2011-12-06T14:00:00Z

This collection of scenes from Spec Ops: The Line shows off the beginning of the game and some of the more interesting mechanics.

You'll see Walker using sand to take out enemies, commanding his troops to attack, and even get a glimpse at the sandstorms and how much they affect combat. There's a full seven and a half minutes of gameplay in total, which does include some minor spoilers, so avoid this if you want to play The Line free of pre-conceptions.

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GIKylehttp://www.gameinformer.com/members/GIKyle/default.aspxCheck Out Spec Ops: The Line's Unsettling New Trailer And Screens/games/spec_ops_the_line/b/pc/archive/2011/11/22/check-out-spec-ops-the-line-39-s-unsettling-new-trailer.aspx2011-11-22T15:01:00Z2011-11-22T15:01:00Z

2K Games has released a new trailer for its upcoming military shooter Spec Ops: The Line, and what it lacks in gameplay footage it more than makes up for with disturbing visuals. The clip takes images of American pop culture and juxtaposes them with scenes of violence and horror.

Spec Ops: The Line is set in a ruined Dubai, with the player character U.S. Delta Force soldier Captain Walker tasked with rescuing a squad of U.S. Infantry. Why Dubai? Funny you should ask.

"With all of its grandeur and opulence, Dubai provides the perfect backdrop to play out Spec Ops: The Line's deep and complex narrative," says said Christoph Hartmann, president of 2K. "This bold endeavor harnesses 2K's pedigree of creating fascinating stories, settings, and worlds to create an aggressive narrative and environment that you won't find in other games on the market. Wartime narratives evoke emotion like few things else in our culture. Players will fight through this hostile environment in Spec Ops: The Line, creating a raw and unique video game experience."

2K promises more than a typical military shooter, with a story that blurs the lines between the typical "good guys" versus "bad guys" that we've grown accustomed to seeing. We'll learn more in the coming year. In the meantime, check out the trailer below.

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GIJeffhttp://www.gameinformer.com/members/GIJeff/default.aspx


text:
Budgeting Basics - Budget Bootcamp
To plan for the short- and long-term, you'll need to have a rudimentary understanding of your finances. In this section, we'll give you the kick-start you need to begin your budget bootcamp.
Know Your After-Tax Monthly Income
Everyone knows his/her hourly wage or monthly salary, but not many people can tell you how much they take home after taxes. It's important to know this number because taxes significantly reduce your paycheck, especially if you have to pay state and/or local taxes.
The easiest way to find out how much you really make is to look at your last pay stub, but if you don't have one handy, you can calculate your take-home pay using an online calculator like the ones found at PaycheckCity.com. Or, if you want to understand the formula that the Internal Revenue Service (IRS) uses, check out the federal tax rate schedules.
A comprehensive understanding of taxes isn't necessary to keep a good budget, but if you're curious, here's how income tax works:
Let\'s suppose you\'re filing as a single person and your salary is $40,000 a year. According to the IRS 2007 federal tax rate schedules, your marginal tax rate is 25%. In this scenario, your first $7,825 is taxed at 10%, the next $24,025 is taxed at 15% and the remaining $8,150 is taxed at 25%. On top of these federal taxes, you\'ll also pay Social Security at a rate of 6.2% on earnings up to a certain threshold ($97,500 in 2008) and Medicare at a rate of 1.45% on all earnings, along with any state or local taxes. (For related reading, see Cut Your Tax Bill With Permanent Life Insurance.)
Ensure That Necessities Fall Within After-Tax Income
Once you know how much you really make each month, start by listing all of your necessary expenses, like rent and utilities, and making sure their total cost is less than your take-home pay. If it isn't, can you cut back somehow? Housing expenses take up the largest chunk of most people's budgets, so that's the best place to think about reducing your expenses if you're having trouble making ends meet. If you can't reduce the cost of your necessities, it's time to figure out how to get a higher-paying job, which may involve a longer-term strategy like obtaining a vocational or college degree. (To learn more about investing in your education, read Invest In Yourself With a College Education.)
If you can, consider savings as one of your necessities. This will make you more likely to save because you'll think of it as a necessary "expense" rather than an optional one. As the saying goes, "pay yourself first." This will help keep you from running up your credit when the unexpected occurs, and it can help you achieve your life goals faster.
Some Required Expenses Don't Occur Monthly
Costs like car insurance and visits to the doctor may not be monthly charges for you, but rather expenses you pay only once or twice a year. Nonetheless, you have to pay them, so don't forget to factor them into your necessities. One way to do this is to make a list of all your expenses that only occur a few times a year, add up their total cost, divide it by 12, and add the result to your required monthly savings. This will ensure that you have enough cash on hand to pay these bills when they are due.
Check Your Breathing Room
Hopefully, you still have some money left over after meeting your basic necessities. Make sure you know what this amount is so you won't exceed it. If you're not satisfied with how much fun money you have, use the ideas in the last section to increase your cash flow. Since not having as much fun money as you'd like isn't as dire of a situation as not being able to pay for food or shelter, you can consider short-term strategies to increase your income, like acquiring a temporary second job or selling some possessions you no longer use.
Some Discretionary Expenses Also Aren't Monthly Costs
Expenses like gym memberships, vacations and gifts are not actually required expenses and they may not be the same price each month. So, if you want to be able to afford them, you should again make a list of all these optional expenses that only occur a few times a year, add up their total cost, divide that sum by 12 and add the result to your optional monthly savings. This way, you won't be tempted to go into debt or have to skip out on these items.
Calculate Long-Term Costs for Necessities and Discretionary Spending
To determine whether your spending in a certain category is really worth it to you, think of it in terms of yearly rather than monthly cost. Maybe $1,700 a month for a luxury apartment seems like an acceptable price, but how about $20,400 a year? And that's after tax. If that luxury apartment is in
Now let's move on to part 5, where we'll share some tips for managing and staying within your budget.
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These manic episodes, however, only punctuate a life that is most fundamentally pathetic.
He even went so far as to punctuate the scoop with an exclamation point!
At each point, the audience was eager to punctuate his rhetoric with cheers and applause.
1630s, "to point out," from Medieval Latin punctuatus, past participle of punctuare, from Latin punctus (see point (n.)). Meaning in reference to text, "to have pauses or stops indicated," is from 1818, probably a back-formation from punctuation. Hence, "interrupted at intervals" (1833). Related: Punctuated; punctuating.


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The Ballarini Rialto 10-inch Nonstick Aluminum Fry Pan makes preparing your family's healthy meals easier. Ballarini's patented ThermoPoint ™ indicator turns red when the pan is ready to use. When the pan has cooled enough for safe handling, the ThermPoint indicator returns to green. Made of heavy-gauge forged aluminum, this Ballarini Rialto nonstick fry pan has high heat resistance, which means you can heat foods to 480 degrees F without adding liquids--in other words, you can grill in this fry pan. The aluminum construction also ensures even heating, whether you're frying or searing food. The ergonomic handle is designed to stay cool to 320 degrees F for safe handling. Ballarini's 10" fry pan uses their durable ceramic- and titanium-infused PFOA-free Keravis ™ triple-layer nonstick coating for easy food release. This makes it possible to use less oil and fat during cooking for healthier meals. This nonstick aluminum fry pan is dishwasher safe for quick and easy cleanup.
- Patented ThermoPoint heat indicator
- Heavy-gauge, forged aluminum construction for even heating
- Comfortable ergonomic handles stay cool to 320 degrees F
- Triple-layer Keravis nonstick coating for easy food release
- PFOA-free nonstick coating infused with ceramic and titanium for durability
- Dishwasher-safe aluminum fry pan
- Four-year warranty


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Civil Air Patrol cadets earn awards by completing rigorous physical test, comprehensive leadership and aerospace exams, and they must complete an encampment. Cadets also learn how to lead, study Air Force perspectives on leadership, including principles of officer ship, and the responsibilities of command.
Cadets Receive Prestigious Awards
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Polignano a Mare, Italy Youth Hostels - At B&B Grottone each room has a private bathroom, hairdryer, mini desktop, lcd-tv and air conditioning.
Polignano a Mare, Italy Youth Hostels - Lâ€Ostello di nuova costruzione, ubicato a pochi minuti dal centro è immerso tra i colori dei fiori, il
Find a Polignano a Mare, Italy hostel and save money on discount lodging. Hosteling in Polignano a Mare, Italy offers a different travel experience than staying at traditional accommodations. Youth hostels allow you to meet new people and learn from other international and worldly travelers. Most hostels are conveniently located in the center of the action close to public transportation, including buses and rails. Staying at a Polignano a Mare, Italy hostel is a great option whether you are backpacking or traveling on a budget . Browse our hostels and plan your Polignano a Mare, Italy vacation today!


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Originally written on Fox Sports | Last updated 7/16/13
Kentucky coach John Calipari will reveal some of the philosophy behind his championship program in a book he's writing for release next year. Calipari's tome is titled, Players First: Success From The Inside Out, co-written with New York Times contributing writer and author Michael Sokolove. The coach said Tuesday via Twitter that the book will give him ''a chance to go in-depth about how we do things at Kentucky and why we do what we do.'' After rebuffing previous offers to write a book on how he has built the Wildcats' program, Calipari tweeted that ''this one kind of hit home.'' The Penguin Press is publishing Players First and has an excerpt available on its web site (http://thepenguinpress.com ). It will be Calipari's fourth book and first since 2010.
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Infinite Convergence Study Finds More Than 1 in 3 Mobile Phone Owners Subscribe to Mobile Messages from Businesses
Study also finds 61 percent of mobile phone owners say official updates from employers are most valuable information to receive from businesses via mobile messaging
Chicago – May 20, 2013 – Infinite Convergence Solutions, a next-generation wireless messaging and mobility solutions provider for carriers and enterprises, today releases the results of its 2013 Mobile Messaging Trends study. The study surveyed more than 500 mobile phone owners on their preferences and habits while communicating with businesses both as consumers and employees.
The study found 36 percent of mobile phone owners subscribe to receive mobile messages from businesses; of those subscribers, 90 percent opt to receive mobile messages from one to five businesses.
"The number of mobile and smartphone owners is steadily increasing, and as a result consumers are using these devices as their mainstream means of all communication," said Anurag Lal, CEO of Infinite Convergence. "SMS and MMS messaging is the most ubiquitous and convenient form of communication and a platform businesses must leverage to reach consumers."
Mobile messaging's effectiveness is demonstrated in the amount of time it takes mobile phone owners to open a message. According to the study, 91 percent of mobile phone owners open a message within 15 minutes of seeing a notification.
Messaging for Business Purposes
The study also examined mobile message usage in a corporate environment and found 45 percent of mobile phone owners use mobile messaging for business correspondence. Communication among colleagues is the most popular use, and 61 percent of respondents who send mobile messages for business purposes say official information from an employer is the most valuable information to receive.
Delivery assurance is a common concern with mobile messaging in a business setting. Seventy percent of mobile phone owners do not text with their company, clients or colleagues because of lack of a paper trail to guarantee delivery and receipt. Security is also a concern with 40 percent of respondents hesitant to text for business purposes because of worries about message security.
"Mobile phone owners need to know their message is safely being delivered only to the people or businesses it's intended for," said Lal. "Guaranteed confidentiality, security and delivery reliability builds trust among the consumer, business and mobility provider, increasing recipients' confidence in mobile messaging for enterprise communication."
Consumer Mobile Messaging
The study also uncovered insight on mobile messaging preferences in select industries:
Banking & financial institutions: One in three respondents would communicate financial or banking activity and updates via mobile message.
Healthcare: One in three respondents would share medical information or illness symptoms with their doctor via mobile message.
Travel & hospitality: One in two respondents would make or confirm travel plans via mobile message, and one in four would purchase a flight or book a hotel via mobile message.
Retail: Of the 64 percent of respondents who don't currently subscribe to text messages from businesses, 38 percent would subscribe if offered a deal, special savings or secret sale notification, providing retailers with ample opportunity to engage customers on the device they use most, according to Lal. In fact, 26 percent of respondents rated special offers or promotions as the most valuable type of information to receive from businesses via text.
"Consumers are comfortable with mobile messaging because of its convenience," said Lal. "Mobile messaging can be used across industries and in many different businesses cases, but enterprises must be cautious to only communicate value-added information."
Mobile Messaging Opportunities for Businesses
Businesses have an opportunity to reach mobile phone owners who do not currently communicate with companies via mobile messaging, according to the study. Forty-eight percent of mobile phone owners who do not subscribe to receive mobile messages from businesses would opt in if messages were customized to their specific needs or preferences, signaling a need for businesses to shift from sending mass messages to more personalized information.
"Consumers want tailored information and experiences," said Lal. "Understanding who the consumer is and how to forge a connection is crucial to push consumers to act on the mobile messages they receive."
Infinite Convergence will unveil its findings at CTIA 2013 on Tuesday, May 21, where the company is exhibiting in booth 3657. As a member of CTIA – The Wireless Association, Infinite Convergence plays an active role in this international non-profit organization that has represented the wireless communications industry since 1984. For more information about CTIA, visit www.ctia.org.
The Infinite Convergence 2013 Mobile Messaging Trends Study Methodology
Infinite Convergence Solutions completed this study to uncover trends and provide enterprises with an improved understanding of mobile messaging habits and preferences. Last year, Infinite Convergence launched its Enterprise Messaging Service (EMS), a cloud-based messaging platform specifically designed for enterprises to securely exchange information with customers, employees and business partners globally. For more information about EMS, visit infinite-convergence.com/ems.
The study surveyed 547 consumers on their mobile messaging habits and behaviors. The study asked questions across a variety of topics, including mobile message frequency, communication with businesses and mobile message subscriptions. Findings were evaluated based on demographic qualifiers, including gender, age and smartphone ownership. Response percentages were further evaluated on a quantitative scale to assess actionable metrics for enterprises.
About Infinite Convergence
Formed in 2010 as a result of an alliance between Infinite Computer Solutions (BSE: 533154|NSE: INFINITE) and Motorola (now Nokia Siemens Networks), Infinite Convergence provides innovative messaging and mobility solutions and next-generation wireless communication technologies to mobile operators and enterprises. Currently supporting more than 130 million subscribers globally, Infinite Convergence offers a complete range of scalable Enterprise Messaging Services, Rich Communication Suite, Converged Messaging, Public Safety Messaging, SMS, MMS, and Gateway solutions for businesses and Tier 1 wireless operators globally. Headquartered outside Chicago, Infinite Convergence employs 200 people in the USA, India and Singapore. Infinite Convergence Solution is a wholly owned subsidiary of Infinite Computer Solutions. For more information, visit www.infinite-convergen
Public Relations Contact
Emily Johnson
Walker Sands Communications


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The Big TakedownThe Big Takedown (1994) by Dave Koch
This page has been viewed 19 times this month, and 275 times total.
- Traditional Animation
- WildStorm Productions, Nelvana Limited
- Distributed by: FUNimation Entertainment
- Cartoon Characters: Dockwell, Void, Zealot, Voodoo, Marlowe, WarBlade, Maul, Grifter, Spartan, Helspont.
- Voice Actors: Dennis Akayama, Janet-Laine Green, Roscoe Handford, Ruth Marshall, Sean McCann, Dean McDermott, Paul Mota, Colin O'Meara, Rod Wilson; Full Voice Cast ...
- Directed By Bob Smith.
- Produced By Hasmi Giakoumis, Patrick Loubert, Michael Hirsh, Jim Lee, Toper Taylor, Stephen Hodgins, Patricia R. Burns.
- Written By Bob Forward, David Wise.
- Music: Ray Parker, Tom Szczesniak.
- Editor: Richard Bond, Rob Kirkpatrick.
- Originally Released on November 12, 1994.
- Originally Aired on CBS-TV (USA).
- Running Time: 30 minutes.
- Season: 1
- Color
- Canada
Cartoon Summary:
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Based on one of the hottest comic book titles around, this series is about an extraordinary superhero team called WildC.A.T.s: Covert Action Teams. Led by billionaire industrialist Jacob Marlowe,... (more)
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Photographs from 1969 of America's War on DrugsJuxtapoz // Tuesday, January 07, 2014
With Colorado legalizing recreational Marijuana this year, we take a look back at the October 1969, LIFE cover story titled, "MARIJUANA: At Least 12 Million American Have Now Tried It. Are penalties too severe? Should it be legalized?" Fast forward 45 years and we are only now beginning to see real changes in the War on Drugs. 'Across 10 full pages, intermingling opinion, photography and reportage, LIFE took a hard look at pot smoking in the U.S., but waded deep into the debate -- already heated then -- of whether or not the country's Draconian marijuana statues were doing more harm than good.'
Bizarre Sculptures by Liu XueJuxtapoz // Tuesday, January 07, 2014
Combining human and animal elements, Chinese sculptor Liu Xue creates strange, realistic, hybrid creatures. Whether its a man with tiny bat wings, a half-woman, half-chicken, or a fat frog-legged man pondering his predicament, you'll find it here...
Alex Lukas "Prints & Photographs; Copies & Concrete" @ Breeze Block GalleryJuxtapoz // Tuesday, January 07, 2014
Alex Lukas will be opening a solo exhibition, "prints & Photographs; Copies & Concrete," at Breeze Block Gallery in Portland, Oregon this Thursday, January 9th, 2014. Lukas will focus on the involvement of printmaking practices in his ongoing investigation and recontextualization of the familiar American landscape...
New Work from Sagaki KeitaIllustration // Tuesday, January 07, 2014
An illustration favorite, Sagaki Keita has recently updated his portfolio with a few new works featuring his maddeningly intricate doodle-infested renderings of huge and notable works of art and architecture. This is art that combines the high and low, the part and particle, contemporary and classic. Spend some time with these pieces, they're well worth a long look!
Martin Woutisseth's 'Twin Peaks Illustrated'Illustration // Monday, January 06, 2014
Twin Peaks is one of Martin Woutisseth's favorite TV series. In his re-creation of the television show, Martin needed to illustrate 33 characters. He speaks about the richness of the Twin Peaks universe, in terms of symbolism and detail, and wanted to be sure that nothing was lost in translation.
New Neon: Light, Paint & Photography @ Bedford Gallery, Walnut CreekJuxtapoz // Monday, January 06, 2014
There is a great show open right now in the East Bay of the San Francisco Bay Area called New Neon: Light, Paint, and Photography at the Bedford Gallery in Walnut Creek, featuring a roster of California and international artists that work in and around the medium of neon light and paint. Not only is our very own contributing writer Kristin Farr in the exhibition, but Juxtapoz favorites like Richard Colman, Kill Pixie, Patrick Martinez, Amir Fallah and otheres all made their way into the great show. It is open through February 23, 2014.
Kota Ezawa "Panorama" @ Christopher Grimes Gallery, Santa MonicaJuxtapoz // Monday, January 06, 2014
We have long been huge fans of Kota Ezawa's lightbox and painting works, and love that kicking off the new year, the artist will have a new series of works, Panorama, on display at Christopher Grimes Gallery in Santa Monica, California. "Ezawa's work references images in popular culture, cinema, television and art history. Through such media as digital animation, slide projections, paper cut-outs, watercolor, light boxes, and collage, he recontextualizes and distills these iconic images to their most essential forms, communicating the symbolic power they have on the collective psyche."
Hyperreal Noodle Sculptures by Seung Yul OhJuxtapoz // Monday, January 06, 2014
One of our favorite pieces of art from 2013's Art Basel Hong Kong was the outside, interactive sculpture, "Periphery", by Seung Yul Oh, on display in the main hall of the Fair. We were happy to see this great project also pop up on the web in recent days, the New Zealand-based, Seoul-born artist's series of hyperreal, resin sculptures of various Korean noodle dishes. Hanging over 12-feet above a gallery floor, Oh not only created the noodles to suspend in air, but the meats, vegetables, and eggs all are hand-crafted and appear edible.
Sheet Music Art by Mike LemanskiJuxtapoz // Monday, January 06, 2014
Mike Lemanski (b. 1986) hails from Huddersfield, UK and graduated from the Highlands School or Art and the Bradford School of Arts. Since then, he has been a successful freelance illustrator creating posters, album covers, book and periodical covers and a gorgeous and playfully intricate series of sheet music illustrations.
City DNA Paintings by Lu XinjianJuxtapoz // Monday, January 06, 2014
Based on aerial views from Google Earth, Chinese artist Lu Xinjian creates abstract paintings of major cities across the world in his series City DNA. What appears to be a mess of abstract shapes and colors, soon becomes a coherent view of the subject city, allowing the city's nuances and unique characteristics to shine through.


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Painting the Seasons with Americana Multi-Surface Acrylics
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Homemade Christmas Wreaths: The Base
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The Base
Classic Styrofoam or Wire Wreath Frame- This one is pretty self-explanatory. A Styrofoam ring is cheap, and, with pins, easily allows for affixing items to the wreath. Wire frames are also available from crafts stores for a few dollars.
Plastic Platter Rings- Recycle those plastic rings that shrimp and other food platters come on (with the cut out in the middle for dip) to make homemade wreaths. Just cut out more of the center portion, leaving a few inch wide ring.
Hula-hoop- A hula-hoop is the perfect base for a large wreath, such as for bay windows.
Craft or Floral Wire- Sturdy craft or floral wire can be used to make many wreaths, such as floral or beaded wreaths.
Clothes Hanger- A clothes hanger can be bent into a circle for a convenient wreath ring.
Flexible vines- Bend flexible natural branches or vines (ivy, grape, honeysuckle) into wreaths and secure with wire. You can also purchase plain grapevine wreaths at garden centers to then embellish.
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Learn How to Make Christmas Wreaths here
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Stay comfortable in all your rides. The Ariat® Ladies' Scout Zip Paddock Boot with 4LR™ footbed technology cushions, stabilizes and wicks moisture. This durable paddock boot has a full-grain leather upper, twin gore panels and a Duratread™ outsole for traction.
Ariat® Ladies' Terrain H2O Boots just got better in a new waterproof version! These boots are great for riding, mucking, running or walking. Go straight from barn work to the saddle. These boots are extremely durable and also make a great hiking boot.
The Ladies' Ariat® Terrain Lace Boot is a sporty, full-grain leather paddock boot with ATS® Technology for superior support while riding, running or walking. Weighing only 12 ounces, this practical riding boot has an EVA™ midsole, composite-fiber forked shank, moisture-wicking footbed and Duratread™ outsole.
The Ariat® Men's Terrain Lace Boot provides comfortable support for riding, hiking, walking or running. Made of full-grain leather, this men's boot weighs only 12 ounces and offers fatigue-fighting stability with Ariat's ATS® footbed technology, a composite-fiber forked shank, a moisture-wicking footbed, EVA™ midsole and Duratread™ outsole.
One of Ariat's most popular riding boots is now available in a zip style! The Ariat® Terrain Zip H2O Boot is easy-on, easy-off. Made with a waterproof, oiled full-grain leather upper, waterproof membrane and moisture-wicking lining. ATS™ technology and EVA™ midsole support ensures all-day comfort.
The Ariat® Ladies' Tioga Lace Paddock Boot is built for comfort with a moisture-wicking lining, flexion panel over the instep and padded top collar. This paddock boot has ATS® footbed technology to cushion, stabilize and support as you walk or ride.
Kids can be carefree at the barn in Ariat® Children's Breeze Paddock Boots. These children's paddock boots are made of washable, extra-soft full-grain leather that cleans up fast and keeps feet comfortable. Features include a padded collar, breathable, moisture-wicking lining, Duratread™ outsole with self-cleaning tread and zipper for easy on/off.
Practical, comfortable, lightweight and rugged, the Ladies' Blundstone 500 Paddock Boot is at home in the stable, paddock or stirrup. Made with Weather Sealed™ oil-tanned leather that sheds water, this boot retains breathability for comfort. Pull straps allow for easy on and twin gore panels plus a roomy toe keep the foot comfortable all day.
Top-level riders make the Ladies' Blundstone Cuthbertson paddock boot a top choice! This durable pull-on paddock boot is styled with an oblique toe and built on a narrow last to suit a woman's foot.


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Texans notes: Ankle not expected to sideline Johnson
JOHN McCLAIN
, Copyright 2010 Houston Chronicle |
November 2, 2010
Photo By Brett Coomer/ChronicleTexans wide receiver Andre Johnson had seven catches for 106 yards and a touchdown Monday night against the Colts.
Receiver Andre Johnson, who limped off the field during the Texans' last drive at Indianapolis, aggravated the high ankle sprained that has plagued him early in the season. He should be ready for Sunday's game against the Chargers.
"He tweaked it a little on the touchdown (28-yard catch), but he came back to the sideline, said he was OK and went back in and did it again late in the game," coach Gary Kubiak said. "Hopefully, we can get through a few weeks with him being fine and not continuing to tweak it.
"But we don't feel like there is anything worse being done to it."
Brown, but not out
Left tackle Duane Brown, who was playing for the first time since serving his four-game suspension, was beaten for two sacks by defensive end Dwight Freeney.
Coach Gary Kubiak never thought about replacing Brown with Rashad Butler, who started during his suspension.
"I never thought of making a change," Kubiak said. "Duane's our guy, and Duane did a lot of good things in the game, too. He fought one of the all-time great players. (Freeney) has been doing this to a lot of people for many, many years, and (playing in Indianapolis) makes it even tougher.
"We've got a lot of confidence in Duane. We've put him in some tough situations. That tells you how much confidence we have in him. He can play better, and we can also help him do better by what we're doing, too."
Stepping up
Defensive end Mark Anderson, who signed with the Texans three weeks ago after Chicago waived him, recorded the team's only sack.
"He'll definitely get more time," coach Gary Kubiak said. "He played a lot more. He's a lot more comfortable with what we're doing. (Assistant head coach/offensive line) Bill (Kollar) did a good job with the rotation, and he will definitely step it up."