Description Usage Arguments Value Author(s) Examples

The findClusters function estimates the number of genes with similar temporal regulation and supports three different clustering algorithms: kmeans, dbscan and hierarchical clustering. Clustering is based on a PCA projection of the input data.

1 2 | ```
findClusters(peakdet, exprmat, maxclusters = 3, eps = 0.02,
clusters = 3, method = "kmeans")
``` |

`peakdet` |
A list returned by the peakDetection function. |

`exprmat` |
A numeric matrix with expression series data with variables as rownames. |

`maxclusters` |
Maximal number of clusters used for kmeans cluster estimation. |

`eps` |
Epsilon value used by the dbscan algorithm. |

`clusters` |
Number of clusters used for the cutree function of the hierarchical clustering. |

`method` |
A character string defining the clustering algorithm with options: c('kmeans', 'dbscan', 'hclust'). |

Returns a cluster assignment of each variable and the number of identified clusters.

David Lauenstein

1 2 3 4 5 6 7 8 9 10 | ```
# Example based on the heat-shock dataset
data(heat)
heat = as.matrix(heat)
# Define series
series <- c(37,40,41,42,43)
# Run the peak detection algorithm
peakdet <- peakDetection(heat, series, type ='rnaseq', actstrength = 1.5,
prominence = 1.3, minexpr = 5000)
# Cluster exploration using kmeans with a maximum of 4 clusters to be assigned
clusters <- findClusters(peakdet, heat, maxclusters = 4, method = 'kmeans')
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.