Difference between revisions of "Hierarchical Clustering"
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Note - hierarchical clustering is memory intensive. With the default memory settings (see [[FAQ#Q._How_do_I_increase_the_amount_of_memory_available_to_Java_to_run_geWorkbench.3F | here]] to change), clustering more than about 2000 markers is not recommended. | Note - hierarchical clustering is memory intensive. With the default memory settings (see [[FAQ#Q._How_do_I_increase_the_amount_of_memory_available_to_Java_to_run_geWorkbench.3F | here]] to change), clustering more than about 2000 markers is not recommended. | ||
| + | |||
| + | ==Parameters== | ||
| + | |||
| + | |||
| + | |||
| + | ===Clustering Method=== | ||
| + | ====Single Linkage==== | ||
| + | ====Average Linkage==== | ||
| + | ====Total Linkage==== | ||
| + | |||
| + | |||
| + | ====Clustering Dimension==== | ||
| + | ====Marker==== | ||
| + | ====Microarray==== | ||
| + | ====Both==== | ||
| + | |||
| + | ===Clustering Metric=== | ||
| + | ====Euclidean==== | ||
| + | ====Pearson's==== | ||
| + | ====Spearman's==== | ||
| + | |||
| + | ===All Arrays=== | ||
| + | |||
| + | ===All Markers=== | ||
| + | |||
| + | ==Example== | ||
| + | ===Running the calculation=== | ||
| + | |||
| + | This example will take off with the set of markers produced in the [[Tutorial_-_ANOVA | ANOVA]] example. Please follow the steps for that example to produce the starting marker set, or just create/select another set of markers of your own. | ||
| + | |||
| + | 1. If following the ANOVA example, activate the set of markers labeled "Significant Genes [1786]" ( which contains 1786 markers). | ||
| + | |||
| + | |||
| + | [[Image:T_HC_set_activation.png]] | ||
| + | |||
| + | |||
| + | 2. Set the parameters as shown in the following figure. | ||
| + | |||
| + | |||
| + | [[Image:T_HC_setup.png]] | ||
| + | |||
| + | * Clustering methods: Average Linkage. | ||
| + | * Clustering Dimension: Marker. | ||
| + | * Clustering Metric: Euclidean. | ||
| + | |||
| + | 3. Click '''Analyze'''. | ||
| + | |||
| + | 4. A progress bar will be visible during the calculation. | ||
[[Image:T_HC_computing.png]] | [[Image:T_HC_computing.png]] | ||
| + | |||
| + | |||
| + | The results are placed in the Project Folders component and labeled "Hierarchical Clustering", and can be displayed in the Dendrogram component. | ||
| + | |||
| + | |||
| + | ===Displaying results in the Dendrogram component=== | ||
| + | |||
[[Image:T_HC_Dendrogram_add-to-set.png]] | [[Image:T_HC_Dendrogram_add-to-set.png]] | ||
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[[Image:T_HC_MarkerSets-ClusterTree.png]] | [[Image:T_HC_MarkerSets-ClusterTree.png]] | ||
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Revision as of 18:57, 28 July 2009
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Contents
Overview
geWorkbench implements its own code for agglomerative hierarchical clustering.
Note - hierarchical clustering is memory intensive. With the default memory settings (see here to change), clustering more than about 2000 markers is not recommended.
Parameters
Clustering Method
Single Linkage
Average Linkage
Total Linkage
Clustering Dimension
Marker
Microarray
Both
Clustering Metric
Euclidean
Pearson's
Spearman's
All Arrays
All Markers
Example
Running the calculation
This example will take off with the set of markers produced in the ANOVA example. Please follow the steps for that example to produce the starting marker set, or just create/select another set of markers of your own.
1. If following the ANOVA example, activate the set of markers labeled "Significant Genes [1786]" ( which contains 1786 markers).
2. Set the parameters as shown in the following figure.
- Clustering methods: Average Linkage.
- Clustering Dimension: Marker.
- Clustering Metric: Euclidean.
3. Click Analyze.
4. A progress bar will be visible during the calculation.
The results are placed in the Project Folders component and labeled "Hierarchical Clustering", and can be displayed in the Dendrogram component.
Displaying results in the Dendrogram component
