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hierarchicalcluster.qmd
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hierarchicalcluster.qmd
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---
title: "Hierarchical clustering"
author: "Cox Lab"
format:
html:
toc: true
toc-depth: 4
toc-expand: false
number-sections: true
number-depth: 4
editor: source
date: today
bibliography: references.bib
---
# General
- **Type:** - Matrix Analysis
- **Heading:** - Clustering/PCA
- **Source code:** not public.
# Brief description
This activity performs hierarchical clustering of rows and/or columns and produces a visual heat map representation of the clustered matrix. Clustering can be performed with a choice of distances and linkages. This activity can also be used just to display your data in a heat map without performing clustering by deselecting row and column clustering.
```{=html}
<!-- This comment and the line above it must be preserved when editing this file!
The recommended sections are these, but they may be changed on a case by case basis.
===== Detailed description =====
===== Parameters =====
===== Theoretical background =====
===== Examples =====
Make changes only below this line! -->
```
# Parameters
## Row tree
If checked rows will be clustered and a tree (dendrogram) is generated (default: checked).
### Distance
Selected distance that will be used for the clustering process (default: Euclidean). The distance can be selected from a predefined list:
- Euclidean
- L1
- Maximum
- Lp
- Pearson correlation
- Spearman correlation
- Cosine
- Canberra
### Linkage
Selected clustering method that will be applied (default: Average). It can be selected from a predefined list:
- Average
- Complete
- Single
### Constraint
Selected constraint that should be preserved from the input data (default: None). The used constraint can be selected from a predefined list of constraints:
- None
- Preserve order
- Preserve order (periodic)
### Preprocess with k-means
Specifies, whether the data should be pre-processed using k-means before applying clustering and generating a heatmap (default: checked).
### Number of clusters
This parameter is just relevant, if the parameter "Preprocess with k-means" is checked. Defines the number of clusters that will be created by the k-means algorithm (default: 300).
## Column tree
If checked, columns will be clustered and a tree (dendrogram) is generated (default: checked).
### Distance
Selected distance that will be used for the clustering process (default: Euclidean). The distance can be selected from a predefined list:
- Euclidean
- L1
- Maximum
- Lp
- Pearson correlation
- Spearman correlation
### Linkage
Selected clustering method that will be applied (default: Average). It can be selected from a predefined list:
- Average
- Complete
- Single
### Constraint
Selected constraint that should be preserved from the input data (default: None). The used constraint can be selected from a predefined list of constraints:
- None
- Preserve order
- Preserve order (periodic)
- Preserve grouping
### Preprocess with k-means
Specifies, whether the data should be pre-processed using k-means before applying clustering and generating a heatmap (default: checked).
### Number of clusters
This parameter is just relevant, if the parameter "Preprocess with k-means" is checked. Defines the number of clusters that will be created by the k-means algorithm (default: 300).
## Which columns to use
List of all expression/numerical columns in the data set (default: all numerical columns; the expression columns are selected see parameter "Use for clustering").
## Use for clustering
Selected expression/numerical columns that should be used for the clustering (default: all expression columns are selected).
## Display in heat map but do not use for clustering
Selected expression/numerical columns that should be displayed in the output heat map, but are not used for the clustering (default: empty).
# Parameter window
