Nothing
library(monocle)
library(HSMMSingleCell)
context("plot_ordering_genes functions properly")
data(HSMM_expr_matrix)
data(HSMM_gene_annotation)
data(HSMM_sample_sheet)
pd <- new("AnnotatedDataFrame", data = HSMM_sample_sheet)
fd <- new("AnnotatedDataFrame", data = HSMM_gene_annotation)
HSMM <- newCellDataSet(as.matrix(HSMM_expr_matrix),
phenoData = pd,
featureData = fd,
lowerDetectionLimit=0.1,
expressionFamily=tobit(Lower=0.1))
rpc_matrix <- relative2abs(HSMM, method = "num_genes")
HSMM <- newCellDataSet(as(as.matrix(rpc_matrix), "sparseMatrix"),
phenoData = pd,
featureData = fd,
lowerDetectionLimit=0.5,
expressionFamily=negbinomial.size())
HSMM <- estimateSizeFactors(HSMM)
HSMM <- estimateDispersions(HSMM)
HSMM <- detectGenes(HSMM, min_expr = 0.1)
HSMM <- HSMM[,pData(HSMM)$Total_mRNAs < 1e6]
cth <- newCellTypeHierarchy()
MYF5_id <- row.names(subset(fData(HSMM), gene_short_name == "MYF5"))
ANPEP_id <- row.names(subset(fData(HSMM), gene_short_name == "ANPEP"))
cth <- newCellTypeHierarchy()
cth <- addCellType(cth, "Myoblast", classify_func=function(x) {x[MYF5_id,] >= 1})
cth <- addCellType(cth, "Fibroblast", classify_func=function(x)
{x[MYF5_id,] < 1 & x[ANPEP_id,] > 1})
HSMM <- classifyCells(HSMM, cth, 0.1)
disp_table <- dispersionTable(HSMM)
unsup_clustering_genes <- subset(disp_table, mean_expression >= 0.1)
HSMM <- setOrderingFilter(HSMM, unsup_clustering_genes$gene_id)
test_that("plot_ordering_genes functions properly under normal conditions",
expect_error(plot_ordering_genes(HSMM), NA))
test89 <- class(plot_ordering_genes(HSMM))[2]
test_that("plot_ordering_genes returns ggplot", expect_equal(test89, "ggplot"))
test_that("plot_ordering_genes fails if CellDataSet is not passed through",
expect_error(plot_ordering_genes(7), "Error input object is not of type 'CellDataSet'"))
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