## ----eval=FALSE---------------------------------------------------------------
# #Install devtools:
# utils::install.packages('devtools')
# 
# #Install the package from GitHub:
# devtools::install_github('duct317/scDHA')
# #With manual and vignette:
# devtools::install_github('duct317/scDHA', build_manual = T, build_vignettes = T)
# #Or from CRAN:
# install.packages("scDHA")
# 
# #When the package is loaded, it will check for C++ libtorch
# library(scDHA)
# #libtorch can be installed using:
# torch::install_torch()

## ----eval=FALSE---------------------------------------------------------------
# if (!requireNamespace("mclust", quietly = TRUE)) install.packages("mclust")

## ----eval=FALSE---------------------------------------------------------------
# library(scDHA)
# #Load example data (Goolam dataset)
# data("Goolam")
# 
# #Get data matrix and label
# data <- t(Goolam$data); label <- as.character(Goolam$label)
# 
# #Log transform the data
# data <- log2(data + 1)

## ----eval=FALSE---------------------------------------------------------------
# #Generate clustering result, the input matrix has rows as samples and columns as genes
# result <- scDHA(data, seed = 1)
# 
# #The clustering result can be found here
# cluster <- result$cluster
# 
# #Calculate adjusted Rand Index using mclust package
# ari <- round(mclust::adjustedRandIndex(cluster,label), 2)
# print(paste0("ARI = ", ari))

## ----eval=FALSE---------------------------------------------------------------
# #Generate 2D representation, the input is the output from scDHA function
# result <- scDHA.vis(result, seed = 1)
# 
# #Plot the representation of the dataset, different colors represent different cell types
# plot(result$pred, col=factor(label), xlab = "scDHA1", ylab = "scDHA2")

## ----eval=FALSE---------------------------------------------------------------
# #Cell stage order in Goolam dataset
# cell.stages <- c("2cell", "4cell", "8cell", "16cell", "blast")
# 
# #Generate pseudo-time for each cell, the input is the output from scDHA function
# result <- scDHA.pt(result, start.point = 1, seed = 1)
# 
# #Calculate R-squared value representing correlation between inferred pseudo-time and cell stage order
# r2 <- round(cor(result$pt, as.numeric(factor(label, levels = cell.stages)))^2, digits = 2)
# 
# #Plot pseudo-temporal ordering of cells in Goolam dataset
# plot(result$pt, factor(label, levels = cell.stages), xlab= "Pseudo Time", ylab = "Cell Stages", xaxt="n", yaxt="n")
# axis(2, at=1:5,labels=cell.stages, las=2)
# text(x = 1, y = 4.5, labels = paste0("R2 = ", r2))

## ----eval=FALSE---------------------------------------------------------------
# #Split data into training and testing sets
# set.seed(1)
# idx <- sample.int(nrow(data), size = round(nrow(data)*0.75))
# 
# train.x <- data[idx, ]; train.y <- label[idx]
# test.x <- data[-idx, ]; test.y <- label[-idx]
# 
# #Predict the labels of cells in testing set, the input matrices have rows as samples and columns as genes
# prediction <- scDHA.class(train = train.x, train.label = train.y, test = test.x, seed = 1)
# 
# #Calculate accuracy of the predictions
# acc <- round(sum(test.y == prediction)/length(test.y), 2)
# print(paste0("Accuracy = ", acc))