Cluster the DA cells retained from Step 1 and Step 2 of DA-seq to obtain spatially coherent DA regions.

getDAregion(X, da.cells, cell.labels, labels.1, labels.2,
  prune.SNN = 1/15, resolution = 0.05, group.singletons = F,
  min.cell = NULL, do.plot = T, plot.embedding = NULL, size = 0.5,
  do.label = F, ...)

Arguments

X

size N-by-p matrix, input merged dataset of interest after dimension reduction

da.cells

output from getDAcells() or updateDAcells()

cell.labels

size N vector, labels for each input cell

labels.1

vector, label name(s) that represent condition 1

labels.2

vector, label name(s) that represent condition 2

prune.SNN

parameter for Seurat function FindNeighbors(), default 1/15

resolution

parameter for Seurat function FindClusters(), default 0.05

group.singletons

parameter for Seurat function FindClusters(), default True

min.cell

integer, number of cells below which a DA region will be removed as outliers, default NULL, use minimum k value in k-vector

do.plot

a logical value to indicate whether to return ggplot objects showing the results, default True

plot.embedding

size N-by-2 matrix, 2D embedding for the cells

size

cell size to use in the plot, default 0.5

do.label

a logical value to indicate whether to label each DA region with text, default False

...

other parameters to pass to Seurat FindClusters()

Value

a list of results

da.region.label

DA region label for each cell from the whole dataset, '0' represents non-DA cells.

DA.stat

a table showing DA score and p-value for each DA region

da.region.plot

ggplot object showing DA regions on plot.embedding