Description Usage Arguments Value Examples
Filter cells using either k-means or Dirichlet process means clustering of sparsity metrics
1 2 3 4 5 6 7 8 9 | filterCells(
sparsity.mat,
rse.obj,
cluster.method = c("kmeans", "dpmeans"),
clusters = NULL,
tol = 0.1,
plot.data = FALSE,
invert = FALSE
)
|
sparsity.mat |
A matrix of summarized sparsity measures |
rse.obj |
The unfiltered RangedSummarizedExperiment object |
cluster.method |
Clustering method to use (default: kmeans) |
clusters |
How many clusters to generate; if NULL it will autopick the cluster number (default: NULL) |
tol |
The tolerance or minimum difference in fraction of between cluster sum of squares over total for k-means auto-picking cluster number (default: 0.1) |
plot.data |
Whether to plot the data |
invert |
Invert which cluster is used to filter |
A filtered RangedSummarizedExperiment object and/or plot of the filtered data
1 | # FIXME: add example
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.