runSam {ddPCRclust} | R Documentation |
Find the rain and assign it based on the distance to vector lines connecting the cluster centres.
runSam(file, sensitivity = 1, numOfMarkers, missingClusters = NULL, similarityParam = 0.95, distanceParam = 0.2)
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. |
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). |
# Run the SamSPECTRAL based approach exampleFiles <- list.files(paste0(find.package('ddPCRclust'), '/extdata'), full.names = TRUE) file <- read.csv(exampleFiles[3]) samResult <- runSam(file = file, numOfMarkers = 4) # Plot the results library(ggplot2) p <- ggplot(data = samResult$data, mapping = aes(x = Ch2.Amplitude, y = Ch1.Amplitude)) p <- p + geom_point(aes(color = factor(Cluster)), size = .5, na.rm = TRUE) + ggtitle('SamSPECTRAL example')+theme_bw() + theme(legend.position='none') p