DA-seq Step 1 & Step 2: select DA cells

Step 1: compute a multiscale score measure for each cell of its k-nearest-neighborhood for multiple values of k. Step 2: train a logistic regression classifier based on the multiscale score measure and retain cells that may reside in DA regions.

getDAcells(X, cell.labels, labels.1, labels.2, k.vector = NULL,
  save.knn = F, alpha = 0, k.folds = 10, n.runs = 5, n.rand = 2,
  pred.thres = NULL, do.plot = T, plot.embedding = NULL,
  size = 0.5)

Arguments

X	size N-by-p matrix, input merged dataset of interest after dimension reduction.
cell.labels	size N character vector, labels for each input cell
labels.1	character vector, label name(s) that represent condition 1
labels.2	character vector, label name(s) that represent condition 2
k.vector	vector, k values to create the score vector
save.knn	a logical value to indicate whether to save computed kNN result, default False
alpha	numeric, elasticnet mixing parameter passed to glmnet(), default 0 (Ridge)
k.folds	integer, number of data splits used in the neural network, default 10
n.runs	integer, number of times to run the neural network to get the predictions, default 5
n.rand	integer, number of random permutations to run, default 2
pred.thres	length-2 vector, top and bottom threshold on DA measure, default NULL, select significant DA cells based on permutation
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

Value

a list of results

da.ratio

score vector for each cell

da.pred

(mean) prediction from the logistic regression

da.up

index for DA cells more abundant in condition of labels.2

da.down

index for DA cells more abundant in condition of labels.1

pred.plot

ggplot object showing the predictions of logistic regression on plot.embedding

da.cells.plot

ggplot object highlighting cells of da.cell.idx on plot.embedding