#### DMCA

## Extracting binary signals from microarray time-course data (2007)

Venue: | Nucleic Acids Res |

Citations: | 6 - 1 self |

### Citations

2883 | Cluster analysis and display of genome-wide expression patterns
- Eisen, Spellman, et al.
- 1998
(Show Context)
Citation Context ... 6, 8, 10, 12, 16, 20, 24 and 36 h. The data for all of the 5,289 genes with no missing time points were used. The time course was analyzed using hierarchical clustering (8), SAM (2), EDGE (25), STEM =-=(12)-=- and StepMiner. There is a more detailed discussion, with examples, in the Supplementary Data S1, including figures showing the results of each program on the above mentioned data set. A side-by-side ... |

802 |
Statistical significance for genomewide studies
- Storey, Tibshirani
- 2003
(Show Context)
Citation Context ...ng tools, StepMiner and four other widely used publicly available programs were run on the same publicly available microarray time course, tracing the response of fibroblasts to the addition of serum =-=(31,32)-=-. The time course consists of 13 arrays, taken at the time 0, 1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24 and 36 h. The data for all of the 5,289 genes with no missing time points were used. The time course ... |

607 |
Gene Ontology: tool for the unification of biology
- Ashburner, Ball, et al.
- 2000
(Show Context)
Citation Context ...the dendrogram, appeared to consist of genes with temporal behavior related to the diauxic shift. In the original article, the sets of genes in the selected clusters were examined using GO-TermFinder =-=(5)-=- to identify GO annotations of genes that are enriched. The article lists several GO annotations that had extremely small P-values according to GO-TermFinder. Many of the annotations are obviously rel... |

474 | Missing values estimation methods for DNA microarrays. - Troyanskaya, Cantor, et al. - 2001 |

286 | WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic acids research, 33(suppl 2), - Zhang, Kirov, et al. - 2005 |

274 | Java Treeview–extensible visualization of microarray data. - Saldanha - 2004 |

173 |
Significance analysis of time course microarray experiments.
- Storey, Xiao, et al.
- 2005
(Show Context)
Citation Context ...grades the confidence in the results for that gene. However, in practice, it is probably better to fill in missing data points using one of a variety of existing imputation algorithms for microarrays =-=(33)-=-. Optimizing time course experiments for StepMiner Simulations suggest several guidelines for experimental design that can lead to more meaningful results with StepMiner. There should be enough time p... |

152 | The Stanford Microarray Database: data access and quality assessment tools. - Gollub, Ball, et al. - 2003 |

150 | Aligning gene expression time series with time warping algorithms.
- Aach, Church
- 2001
(Show Context)
Citation Context ...average number of significant genes in the random permutations are computed. The ratio of the average number of significant genes to the original number of significant genes is an estimate of the FDR =-=(2)-=-. The FDR can be adjusted by setting the P-value threshold used in the matching algorithm. RESULTS Analysis of simulated data The algorithm was evaluated on simulated time course microarray data with ... |

136 | H: Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 2003
- Luan, Li
(Show Context)
Citation Context ...ed on matching models of gene behavior to time-course data. For example, the models could be piecewise linear models(16), rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) =-=(19,20)-=-, differential equations(21), Bayesian models (22), or Boolean models (23). StepMiner is also a model-based method, but the oneand two-step patterns are different from the models of other methods. The... |

129 | Analyzing time series gene expression data
- BAR-JOSEPH
- 2004
(Show Context)
Citation Context ...o metabolism. The GO annotations and FDR-corrected P-values for the clusters reported in Brauer et al. were recomputed with the latest yeast gene annotations from the Gene Ontology Consortium website =-=(6)-=-. To compare with the results of Brauer et al., Table 1 shows the GO annotations from that article that had low P-values, and shows the corresponding P-values from the StepMiner groups. The annotation... |

95 | Continuous representations of time-series gene expression data.
- Bar-Joseph, Gerber, et al.
- 2003
(Show Context)
Citation Context ...ken at the time 0, 1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24 and 36 h. The data for all of the 5,289 genes with no missing time points were used. The time course was analyzed using hierarchical clustering =-=(8)-=-, SAM (2), EDGE (25), STEM (12) and StepMiner. There is a more detailed discussion, with examples, in the Supplementary Data S1, including figures showing the results of each program on the above ment... |

91 | Clustering short time series gene expression data - ERNST, NAU, et al. - 2005 |

88 | Using hidden Markov models to analyze gene expression time course data - Schliep, Schonhuth, et al. - 2003 |

76 | Binary analysis and optimization-based normalization of gene expression data, Bioinformatics 18(4):555–565
- Shmulevich, Zhang
- 2002
(Show Context)
Citation Context ...ng tools, StepMiner and four other widely used publicly available programs were run on the same publicly available microarray time course, tracing the response of fibroblasts to the addition of serum =-=(31,32)-=-. The time course consists of 13 arrays, taken at the time 0, 1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24 and 36 h. The data for all of the 5,289 genes with no missing time points were used. The time course ... |

71 | A multivariate empirical Bayes statistic for replicated microarray time course data. - Tai, Speed - 2006 |

68 | Analysis techniques for microarray time-series data,” - Filkov, Skiena, et al. - 2001 |

60 | Statistical tests for identifying differentially expressed genes in time-course microarray experiments. - Park, Yi, et al. - 2003 |

51 |
ErmineJ: tool for functional analysis of gene expression data sets
- Lee
- 2005
(Show Context)
Citation Context ...temporal profile. Many tools are based on matching models of gene behavior to time-course data. For example, the models could be piecewise linear models(16), rising/falling (17), transition intervals =-=(18)-=- or hidden Markov models (HMMs) (19,20), differential equations(21), Bayesian models (22), or Boolean models (23). StepMiner is also a model-based method, but the oneand two-step patterns are differen... |

51 |
A data-driven clustering method for time course gene expression data. Nucleic Acids Res
- Ma, Castillo-Davis, et al.
- 2006
(Show Context)
Citation Context ...ehavior to time-course data. For example, the models could be piecewise linear models(16), rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) (19,20), differential equations=-=(21)-=-, Bayesian models (22), or Boolean models (23). StepMiner is also a model-based method, but the oneand two-step patterns are different from the models of other methods. The transition interval method ... |

48 | Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. - Bar-Joseph, Gerber, et al. - 2003 |

27 |
A practical false discovery rate approach to identifying patterns of differential expression in microarray data.
- Grant, Liu, et al.
- 2005
(Show Context)
Citation Context ...ion of step-wise changes in the gene expression temporal profile. Many tools are based on matching models of gene behavior to time-course data. For example, the models could be piecewise linear models=-=(16)-=-, rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) (19,20), differential equations(21), Bayesian models (22), or Boolean models (23). StepMiner is also a model-based metho... |

23 | Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures - Brauer, Saldanha, et al. - 2005 |

20 |
DnaA coordinates replication initiation and cell cycle transcription in Caulobacter crescentus
- Hottes, Shapiro, et al.
- 2005
(Show Context)
Citation Context ...ges in the gene expression temporal profile. Many tools are based on matching models of gene behavior to time-course data. For example, the models could be piecewise linear models(16), rising/falling =-=(17)-=-, transition intervals (18) or hidden Markov models (HMMs) (19,20), differential equations(21), Bayesian models (22), or Boolean models (23). StepMiner is also a model-based method, but the oneand two... |

18 | Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development - Sasik, Iranfar, et al. - 2002 |

17 |
From the cover: gene set enrichment analysis: a knowledge-based approach for interpreting genomewide expression profiles
- Subramanian
(Show Context)
Citation Context ...e-step genes that rise at time 3 could be merged with the two-step genes that rise at time 3). The sets can be placed in a specific order for visualization in a heat map using a tool such as TreeView =-=(34)-=-. First, genes are categorized by the direction of change and number of steps into five generic gene sets: ‘up’, ‘down’, ‘up then down’, and ‘down then up’ and ‘other’. The one-step sets are further s... |

15 | Clustering of unevenly sampled gene expression time-series data. Fuzzy Sets and Systems 49–66
- Moller, Klawonn, et al.
- 2004
(Show Context)
Citation Context ... data. For example, the models could be piecewise linear models(16), rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) (19,20), differential equations(21), Bayesian models =-=(22)-=-, or Boolean models (23). StepMiner is also a model-based method, but the oneand two-step patterns are different from the models of other methods. The transition interval method from Hottes et al. (18... |

15 | Identifying genes from up–down properties of microarray expression series, Bioinformatics 21 - Willbrand, Radvanyi, et al. - 2005 |

12 | The graphical query language: a tool for analysis of gene expression time-courses - Costa, Schönhuth, et al. - 2005 |

11 |
A multi-step approach to time series analysis and gene expression clustering,
- Amato, Ciaramella, et al.
- 2006
(Show Context)
Citation Context ...etabolism shifts abruptly to oxidative metabolism. RNA samples were collected approximately every 15 min and measured with microarrays. An analysis of the results was published in 2005 [Brauer et al. =-=(3)-=-]. In that article, the data were analyzed using hierarchical clustering by gene [Gollub et al. (4)]. Of the many clusters generated, the authors picked seven clusters that had fairly high correlation... |

10 | Time ordering of gene co-expression
- Leng, Müller
- 2006
(Show Context)
Citation Context ...ed on matching models of gene behavior to time-course data. For example, the models could be piecewise linear models(16), rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) =-=(19,20)-=-, differential equations(21), Bayesian models (22), or Boolean models (23). StepMiner is also a model-based method, but the oneand two-step patterns are different from the models of other methods. The... |

8 |
Discussion: Multivariate adaptive regression splines
- Owen
- 1991
(Show Context)
Citation Context ...models could be piecewise linear models(16), rising/falling (17), transition intervals (18) or hidden Markov models (HMMs) (19,20), differential equations(21), Bayesian models (22), or Boolean models =-=(23)-=-. StepMiner is also a model-based method, but the oneand two-step patterns are different from the models of other methods. The transition interval method from Hottes et al. (18) is perhaps the most si... |

5 |
Remembrance of experiments past: analyzing time course datasets to discover complex temporal invariants. NYU-CS-TR858
- Antoniotti, Ramakrishnan, et al.
- 2005
(Show Context)
Citation Context ...5 min and measured with microarrays. An analysis of the results was published in 2005 [Brauer et al. (3)]. In that article, the data were analyzed using hierarchical clustering by gene [Gollub et al. =-=(4)-=-]. Of the many clusters generated, the authors picked seven clusters that had fairly high correlations and that, by visual inspection of the dendrogram, appeared to consist of genes with temporal beha... |

4 |
From the Cover: Cluster analysis of gene expression dynamics.
- Ramoni, Sebastiani, et al.
- 2002
(Show Context)
Citation Context ...1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24 and 36 h. The data for all of the 5,289 genes with no missing time points were used. The time course was analyzed using hierarchical clustering (8), SAM (2), EDGE =-=(25)-=-, STEM (12) and StepMiner. There is a more detailed discussion, with examples, in the Supplementary Data S1, including figures showing the results of each program on the above mentioned data set. A si... |

3 | de Rijn,M. et al. (2004) Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds - Chang, Sneddon, et al. |

3 |
Module networks: identifying regulatorymodules and their condition-specific regulators from gene expression data
- Segal, Shapira, et al.
- 2003
(Show Context)
Citation Context ...ustifiable measures of confidence. Other methods for analyzing time courses are not easily categorized, including identification of differentially expressed genes (24–28) and alignment of time series =-=(29,30)-=-. It is unclear how these methods could be used to identify the direction and times of expression level transitions. For a more concrete view of the differences among tools, StepMiner and four other w... |

3 |
GO-TermFinder. The Comprehensive Perl Archive Network
- Sherlock
- 2003
(Show Context)
Citation Context ...ustifiable measures of confidence. Other methods for analyzing time courses are not easily categorized, including identification of differentially expressed genes (24–28) and alignment of time series =-=(29,30)-=-. It is unclear how these methods could be used to identify the direction and times of expression level transitions. For a more concrete view of the differences among tools, StepMiner and four other w... |