getDAregion: DA-seq Step 3: get DA regions

In KlugerLab/DAseq: Detecting regions of differential abundance between scRNA-seq datasets

Description

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

Usage

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

cell.idx

index of cells used in DA calculation

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

KlugerLab/DAseq documentation built on April 13, 2021, 9:02 p.m.