tests/testthat/test_classes.R

In MarioniLab/miloR: Differential neighbourhood abundance testing on a graph

context("Testing class instantiation and methods")
library(miloR)

### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(miloR)

set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covariance matrix of each block, say 50 eigenvalues?
block1.eigens <- sapply(1:n.dim, FUN=function(X) rexp(n=1, rate=abs(runif(n=1, min=0, max=50))))
block1.eigens <- block1.eigens[order(block1.eigens)]
block1.p <- qr.Q(qr(matrix(rnorm(block1.cells^2, mean=4, sd=0.01), block1.cells)))
block1.sigma <- crossprod(block1.p, block1.p*block1.eigens)
block1.gex <- abs(rmvnorm(n=r.n, mean=rnorm(n=block1.cells, mean=2, sd=0.01), sigma=block1.sigma))


block2.cells <- 400
# select a set of eigen values for the covariance matrix of each block, say 50 eigenvalues?
block2.eigens <- sapply(1:n.dim, FUN=function(X) rexp(n=1, rate=abs(runif(n=1, min=0, max=50))))
block2.eigens <- block2.eigens[order(block2.eigens)]
block2.p <- qr.Q(qr(matrix(rnorm(block2.cells^2, mean=4, sd=0.01), block2.cells)))
block2.sigma <- crossprod(block2.p, block2.p*block2.eigens)
block2.gex <- abs(rmvnorm(n=r.n, mean=rnorm(n=block2.cells, mean=4, sd=0.01), sigma=block2.sigma))


block3.cells <- 200
# select a set of eigen values for the covariance matrix of each block, say 50 eigenvalues?
block3.eigens <- sapply(1:n.dim, FUN=function(X) rexp(n=1, rate=abs(runif(n=1, min=0, max=50))))
block3.eigens <- block3.eigens[order(block3.eigens)]
block3.p <- qr.Q(qr(matrix(rnorm(block3.cells^2, mean=4, sd=0.01), block3.cells)))
block3.sigma <- crossprod(block3.p, block3.p*block3.eigens)
block3.gex <- abs(rmvnorm(n=r.n, mean=rnorm(n=block3.cells, mean=5, sd=0.01), sigma=block3.sigma))

sim1.gex <- do.call(cbind, list("b1"=block1.gex, "b2"=block2.gex, "b3"=block3.gex))
colnames(sim1.gex) <- paste0("Cell", 1:ncol(sim1.gex))
rownames(sim1.gex) <- paste0("Gene", 1:nrow(sim1.gex))
sim1.pca <- prcomp_irlba(t(sim1.gex), n=50, scale.=TRUE, center=TRUE)

set.seed(42)
block1.cond <- rep("A", block1.cells)
block1.a <- sample(1:block1.cells, size=floor(block1.cells*0.9))
block1.b <- setdiff(1:block1.cells, block1.a)
block1.cond[block1.b] <- "B"

block2.cond <- rep("A", block2.cells)
block2.a <- sample(1:block2.cells, size=floor(block2.cells*0.05))
block2.b <- setdiff(1:block2.cells, block2.a)
block2.cond[block2.b] <- "B"

block3.cond <- rep("A", block3.cells)
block3.a <- sample(1:block3.cells, size=floor(block3.cells*0.5))
block3.b <- setdiff(1:block3.cells, block3.a)
block3.cond[block3.b] <- "B"

meta.df <- data.frame("Block"=c(rep("B1", block1.cells), rep("B2", block2.cells), rep("B3", block3.cells)),
                      "Condition"=c(block1.cond, block2.cond, block3.cond),
                      "Replicate"=c(rep("R1", floor(block1.cells*0.33)), rep("R2", floor(block1.cells*0.33)),
                                    rep("R3", block1.cells-(2*floor(block1.cells*0.33))),
                                    rep("R1", floor(block2.cells*0.33)), rep("R2", floor(block2.cells*0.33)),
                                    rep("R3", block2.cells-(2*floor(block2.cells*0.33))),
                                    rep("R1", floor(block3.cells*0.33)), rep("R2", floor(block3.cells*0.33)),
                                    rep("R3", block3.cells-(2*floor(block3.cells*0.33)))))
colnames(meta.df) <- c("Block", "Condition", "Replicate")
# define a "sample" as teh combination of condition and replicate
meta.df$Sample <- paste(meta.df$Condition, meta.df$Replicate, sep="_")
meta.df$Vertex <- c(1:nrow(meta.df))

sim1.sce <- SingleCellExperiment(assays=list(logcounts=sim1.gex),
                                 reducedDims=list("PCA"=sim1.pca$x))
sim1.mylo <- Milo(sim1.sce)
attr(reducedDim(sim1.mylo, "PCA"), "rotation") <- sim1.pca$rotation

test_that("Milo inherits from SingleCellExperiment", {
    expect_is(sim1.mylo, "SingleCellExperiment")
})

test_that("Milo can instantiate an empty object", {
    empty.mylo <- Milo()
    expect_equal(ncol(empty.mylo), 0)
    expect_equal(nrow(empty.mylo), 0)
})

test_that("Milo getters working as expected", {
    # graph retrieval with an empty graph should give a warning
    expect_warning(graph(sim1.mylo), "Graph not set")

    sim1.mylo <- buildGraph(sim1.mylo, k=21, d=30)
    expect_type(graph(sim1.mylo), "list")

    sim1.mylo <- makeNhoods(sim1.mylo, k=21, prop=0.1, refined=TRUE,
                                    d=30,
                                    reduced_dims="PCA")
    expect_s4_class(nhoods(sim1.mylo), "sparseMatrix")

    sim1.mylo <- calcNhoodDistance(sim1.mylo, d=30)
    expect_equal(class(nhoodDistances(sim1.mylo)), "list")

    sim1.mylo <- countCells(sim1.mylo, samples="Sample", meta.data=meta.df)
    expect_s4_class(nhoodCounts(sim1.mylo), "Matrix")

    # check concordant dimensions for nhoods
    expect_identical(ncol(nhoods(sim1.mylo)), nrow(nhoodCounts(sim1.mylo)))

    sim1.mylo <- calcNhoodExpression(sim1.mylo)
    expect_identical(ncol(nhoodExpression(sim1.mylo)), nrow(nhoodCounts(sim1.mylo)))
    expect_identical(nrow(nhoodExpression(sim1.mylo)), nrow(sim1.mylo))
})


test_that("Milo setters working as expected", {
    graph(sim1.mylo) <- list()
    expect_identical(graph(sim1.mylo), list())

    nhoodDistances(sim1.mylo) <- list()
    expect_equal(class(nhoodDistances(sim1.mylo)), "list")

    nhoods(sim1.mylo) <- Matrix(0L, sparse = TRUE)
    expect_identical(nhoods(sim1.mylo), Matrix(0L, sparse = TRUE))

    nhoodCounts(sim1.mylo) <- matrix(0L, ncol=ncol(nhoodCounts(sim1.mylo)),
                                     nrow=nrow(nhoodCounts(sim1.mylo)))
    expect_equal(sum(rowSums(nhoodCounts(sim1.mylo))), 0)

    nhoodExpression(sim1.mylo) <- matrix(0L, ncol=length(nhoodIndex(sim1.mylo)),
                                         nrow=nrow(sim1.mylo))
    expect_equal(sum(rowSums(nhoodExpression(sim1.mylo))), 0)

    # loadings were set on Milo object at instantiation
    nhoodReducedDim(sim1.mylo) <- list()
    expect_identical(nhoodReducedDim(sim1.mylo), list())
})