update default

library(RColorBrewer)

library(scales)

library(tclust)

library(Seurat)

# library(Seurat)

#' @param labels.2 vector, label name(s) that represent condition 2

#' @param k.vector vector, k values to create the score vector

#' @param k.folds integer, number of data splits used in the neural network, default 10

#' @param n.runs integer, number of times to run the neural network to get the predictions, default 1

#' @param n.runs integer, number of times to run the neural network to get the predictions, default 10

#' @param pred.thres length-2 vector, top and bottom threshold on the predictions from the neural network, default c(0.05,0.95)

#' @param do.plot a logical value to indicate whether to return ggplot objects showing the results, default True

#' @param plot.embedding size N-by-2 matrix, 2D embedding for the cells

getDAcells <- function(

  X, cell.labels, labels.1, labels.2, k.vector,

  k.folds = 10, n.runs = 1, pred.thres = c(0.05,0.95),

  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", source.code = "./DA_logit.py", GPU = ""

){

STGmarkerFinder <- function(

  X, cell.idx, da.region.label,

  da.regions.to.run = NULL, 

  lambda = 1, n.runs = 5, return.model = F, 

  lambda = 1.5, n.runs = 5, return.model = F, 

  python.use = "/usr/bin/python", source.code = "./DA_STG.py", GPU = ""

){

  # set Python