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

In JunZhao1990/DAseq: Detecting regions of differential abundance between scRNA-seq datasets

Description

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.

Usage

getDAcells(X, cell.labels, labels.1, labels.2, k.vector, k.folds = 10,
  n.runs = 10, pred.thres = c(0.05, 0.95), do.plot = T,
  plot.embedding = NULL, size = 0.5, python.use = "/usr/bin/python",
  GPU = "")

Arguments

X	size N-by-p matrix, input merged dataset of interest after dimension reduction
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
k.vector	vector, k values to create the score vector
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 10
pred.thres	length-2 vector, top and bottom threshold on the predictions from the logistic classification, default c(0.05,0.95)
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
python.use	character string, the Python to use, default "/usr/bin/python"
GPU	which GPU to use, default ”, using CPU

Value

a list of results

da.ratio

score vector for each cell

da.pred

(mean) prediction from the neural network

da.cell.id

index for DA cells

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

JunZhao1990/DAseq documentation built on April 30, 2020, 10:10 a.m.