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Introduction to PhitestR

Wei Vivian Li, Rutgers Department of Biostatistics and Epidemiology

2022-01-20

PhitestR currently supports input of a UMI count matrix with cluster labels.

To demonstrate the usage of PhitestR with a count matrix, we use an example dataset which can be downloaded from this link. To use PhitestR, we need a count matrix (in this example, object) with rows representing genes and columns representing cells. In addition, we also need a (character or numeric) vector of labels (in this case, label) specifying the assignments of the cells.

data = readRDS("Example.rds")
object = data$count
label = data$labels

Now, to run Phitest, we just need to specify the count matrix, cluster labels, and the number of cores to be used in parallel computation. The result is a list containing two elements. The first element is a vector of P values corresponding to the clusters in label.

result = phitest(object, label, ncores = 2)
result$pval

The second element is a named list of estimated parameters corresponding to the clusters. The list names are the cluster labels, and each element is a matrix with rows representing genes and columns representing estimated parameters.

For example, we could make similar plots as shown in the paper with the code below:

library(ggplot2)
labels = names(result$par)
par = lapply(labels, function(l){
  tp = result$par[[l]]
  tp$cluster = l
  return(tp)
})
par = Reduce(rbind, par)
ggplot(par, aes(x = dhat, y = dhat.c)) +
  geom_point(shape = 1, cex = .5) +
  geom_abline(intercept = 0, slope = 1, color = "red") +
  facet_wrap(~cluster) +
  xlab("Estimated frequency of zero count (gene-specific dispersion)") +
  ylab("Estimated frequency of zero count (common dispersion)")

Estimated parameters of Phitest by clusters.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.