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")
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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')
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