tests/testthat/test-scalign.R

In quon-titative-biology/scAlign: An alignment and integration method for single cell genomics

context("scalign")

test_that("scAlign halts on no input", {
  ## Run scAlign with high_var_genes
  expect_error(scAlign(encoder.data="scale.data",
                       supervised='none',
                       run.encoder=TRUE,
                       run.decoder=TRUE,
                       log.dir=file.path(tempdir(), 'models','gene_input'),
                       device="CPU"))
})

# test_that("scAlign object creator halts on none SCE input", {
#
#   ## Input data, 1000 genes x 100 cells
#   data = matrix(sample.int(10000, 1000*100, TRUE), 1000, 100)
#   rownames(data) = paste0("gene", seq_len(1000))
#   colnames(data) = paste0("cell", seq_len(100))
#
#   age    = c(rep("young",50), rep("old",50))
#   labels = c(c(rep("type1",25), rep("type2",25)), c(rep("type1",25), rep("type2",25)))
#
#   ctrl.data = data[,which(age == "young")]
#   stim.data = data[,which(age == "old")]
#
#   ## Build the scAlign class object and compute PCs
#   expect_error(scAlignCreateObject(sce.objects = list("YOUNG"=ctrl.data,
#                                                       "OLD"=stim.data),
#                                    labels = list(cell_type[which(cell_age == "young")],
#                                                  cell_type[which(cell_age == "old")]),
#                                    pca.reduce = TRUE,
#                                    pcs.compute = 50,
#                                    cca.reduce = TRUE,
#                                    ccs.compute = 15,
#                                    project.name = "scAlign_Kowalcyzk_HSC"))
# })


# test_that("Alignment produces consistent results", {
#
#   library(scAlign)
#   library(SingleCellExperiment)
#   library(FNN)
#   library(ggplot2)
#
#   ## Load in cellbench data
#   data("cellbench", package = "scAlign", envir = environment())
#
#   ## Extract RNA mixture cell types
#   mix.types = unlist(lapply(strsplit(colnames(cellbench), "-"), "[[", 2))
#   ## Extract Platform
#   batch = c(rep("CEL", length(which(!grepl("sortseq", colnames(cellbench)) == TRUE))),
#             rep("SORT", length(which(grepl("sortseq", colnames(cellbench)) == TRUE))))
#
#   ## Create SCE objects to pass into scAlignCreateObject
#   youngMouseSCE <- SingleCellExperiment(
#       assays = list(scale.data = cellbench[,batch=='CEL'])
#   )
#
#   oldMouseSCE <- SingleCellExperiment(
#       assays = list(scale.data = cellbench[,batch=='SORT'])
#   )
#
#   ## Build the scAlign class object and compute PCs
#   scAlignCB = scAlignCreateObject(sce.objects = list("CEL"=youngMouseSCE,
#                                                      "SORT"=oldMouseSCE),
#                                    labels = list(mix.types[batch=='CEL'],
#                                                  mix.types[batch=='SORT']),
#                                    data.use="scale.data",
#                                    pca.reduce = FALSE,
#                                    cca.reduce = TRUE,
#                                    ccs.compute = 5,
#                                    project.name = "scAlign_cellbench")
#
#    ## Run scAlign with all_genes
#    scAlignCB = scAlign(scAlignCB,
#                        options=scAlignOptions(steps=500,
#                                               log.every=500,
#                                               norm=TRUE,
# 					      batch.norm.layer=TRUE,
#                                               early.stop=FALSE),
#                        encoder.data="scale.data",
#                        supervised='none',
#                        run.encoder=TRUE,
#                        run.decoder=FALSE,
#                        log.dir=file.path(tempdir(),'gene_input'),
#                        device="CPU")
#
#   aligned_data  = reducedDim(scAlignCB, "ALIGNED-GENE")
#   aligned_CEL = aligned_data[which(batch == "CEL"),]
#   aligned_SORT   = aligned_data[which(batch == "SORT"),]
#
#   class_res = knn(aligned_CEL, aligned_SORT, mix.types[which(batch == "CEL")], k=15)
#   class_acc = mean(as.character(class_res) == mix.types[which(batch == "SORT")])
#   expect_gte(class_acc, 0.0)
#
# })