runPeaks {ddPCRclust} | R Documentation

## Find the clusters using flowPeaks

### Description

Find the rain and assign it based on the distance to vector lines connecting the cluster centres.

### Usage

    runPeaks(file, sensitivity = 1, numOfMarkers, missingClusters = NULL,
      similarityParam = 0.95, distanceParam = 0.2)

### Arguments

`file` | The input data. More specifically, a data frame with two dimensions, each dimension representing the intensity for one color channel.
`sensitivity` | A number between 0.1 and 2 determining sensitivity of the initial clustering, e.g. the number of clusters. A higher value means more clusters are being found. Standard is 1.
`numOfMarkers` | The number of primary clusters that are expected according the experiment set up.
`missingClusters` | A vector containing the number of primary clusters, which are missing in this dataset according to the template.
`similarityParam` | If the distance of a droplet between two or more clusters is very similar, it will not be counted for either. The standard it 0.95, i.e. at least 95% similarity. A sensible value lies between 0 and 1, where 0 means none of the 'rain' droplets will be counted and 1 means all droplets will be counted.
`distanceParam` | When assigning rain between two clusters, typically the bottom 20% are assigned to the lower cluster and the remaining 80% to the higher cluster. This parameter changes the ratio, i.e. a value of 0.1 would assign only 10% to the lower cluster.

### Value

`data` | The original input data minus the removed events (for plotting)
`counts` | The droplet count for each cluster.
`firstClusters` | The position of the primary clusters.
`partition` | The cluster numbers as a CLUE partition (see clue package for more information).

### Examples

    # Run the flowPeaks based approach
    exampleFiles <- list.files(paste0(find.package('ddPCRclust'), '/extdata'), full.names = TRUE)
    file <- read.csv(exampleFiles[3])
    peaksResult <- runPeaks(file = file, numOfMarkers = 4)

    # Plot the results
    library(ggplot2)
    p <- ggplot(data = peaksResult$data, mapping = aes(x = Ch2.Amplitude, y = Ch1.Amplitude))
    p <- p + geom_point(aes(color = factor(Cluster)), size = .5, na.rm = TRUE) +
         ggtitle('flowPeaks example')+theme_bw() + theme(legend.position='none')
    p

[Package ddPCRclust version 1.8.0 Index]