tests/testthat/test-dimReduce.R
In epurdom/clusterCells: Compare Clusterings for Single-Cell Sequencing
context("Dimensionality Reduction")
test_that("makeReducedDims works as promised",{
# check for all objects that get expected
nDim<-3
true3<-abs(reducedDim(sceSimDataDimRed,"PCA")[,1:nDim])
#note: cc gives rownames to everything, so need to unname it
expect_silent(dr3<-makeReducedDims(ceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(unname(true3),unname(abs(reducedDim(dr3,"PCA"))))
expect_silent(dr3<-makeReducedDims(simData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
expect_silent(dr3<-makeReducedDims(seSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
expect_silent(dr3<-makeReducedDims(sceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
#check don't lose them if call on existing object
expect_silent(dr<-makeReducedDims(sceSimDataDimRed,reducedDims="PCA",maxDims=nDim))
expect_equal(reducedDims(sceSimDataDimRed),reducedDims(dr))
#check with maxDims<1 (picks 4 of dimensions apparently -- never checked was correct)
expect_silent(dr2<-makeReducedDims(cc,reducedDims="PCA",maxDims=0.5))
expect_equal(abs(reducedDim(dr2,"PCA")[,1:4]), abs(reducedDim(dr2,"PCA")))
})
test_that("Filter functions work as expected",{
#when there are no filters
expect_silent(filterNames(sceSimData))
expect_equal(clusterExperiment:::filterStats(sceSimData),rowData(sceSimData)[,"b",drop=FALSE])
expect_error(clusterExperiment:::filterStats(sceSimData,type="Filter1"),"None of the values of 'type' argument are valid filter names")
##Redo when no rowData.
testSe<-SummarizedExperiment(mat)
expect_equal(length(filterNames(testSe)),0)
expect_equal(ncol(clusterExperiment:::filterStats(testSe)),0)
expect_error(clusterExperiment:::filterStats(testSe,type="Filter1"),"There are no filter statistics saved for this object")
##Redo when no valid filters.
testSe<-SummarizedExperiment(mat,rowData=gData[,c("a","c")])
expect_equal(length(filterNames(testSe)),0)
expect_equal(ncol(clusterExperiment:::filterStats(testSe)),0)
expect_error(clusterExperiment:::filterStats(testSe,type="Filter1"),"None of the values of 'type' argument are valid filter names")
set.seed(352)
filter<-rnorm(nrow(sce))
filter2<-matrix(rnorm(2*nrow(sce)),ncol=2)
#tests on adding unnamed filters
#value type of replace:
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest,type="Filter1")<-filter)
expect_equal(colnames(clusterExperiment:::filterStats(sceTest)),c("b","Filter1"))
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest,type=c("Filter1","Filter2"))<-filter2)
expect_equal(colnames(clusterExperiment:::filterStats(sceTest)),c("b","Filter1","Filter2"))
#matrix type of replace:
sceTest<-sce
expect_error(clusterExperiment:::filterStats(sceTest)<-filter,"must give matrix of values with column names")
sceTest<-sce
expect_error(clusterExperiment:::filterStats(sceTest)<-filter2,"must give matrix of values with column names")
####Now work with existing filters
colnames(filter2)<-c("Filter1","Filter2")
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
expect_silent(filterNames(sceTest))
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(b=rowData(sce)[,"b"],filter2))
expect_equivalent(clusterExperiment:::filterStats(sceTest,type="Filter1"),DataFrame(filter2)[,"Filter1",drop=FALSE])
expect_error(clusterExperiment:::filterStats(sceTest,type="Myfilter"),"None of the values of 'type' argument are valid filter names")
expect_error(clusterExperiment:::filterStats(sceTest,type=1),"unable to find an inherited method")
##Check Replace with existing filters in place and check actually change them, etc.
sceTest<-SummarizedExperiment(assay(sce))
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
filter3<-filter2+1
expect_silent(clusterExperiment:::filterStats(sceTest) <- filter3)
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(filter3))
sceTest<-SummarizedExperiment(assay(sce))
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
filter4<-filter2+5
colnames(filter4)[2]<-"Myfilter"
expect_silent(clusterExperiment:::filterStats(sceTest) <- filter4)
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(filter4[,"Filter1",drop=FALSE],filter2[,"Filter2",drop=FALSE],filter4[,"Myfilter",drop=FALSE]))
#Need more checks about replacement, etc.
})
test_that("reduce and filter work with hdf5",{
expect_silent(filterNames(hdfSCE))
expect_silent(f1<-filterData(hdfSCE,filterStats="Filter1",cutoff=1))
expect_silent(f1<-filterData(hdfSCE,filterStats="Filter1",cutoff=1))
expect_silent(fs<-makeFilterStats(hdfSCE,filterStats="var"))
expect_silent(fs<-makeFilterStats(hdfSCE,filterStats=c("mean","var")))
expect_silent(out<-filterData(fs,filterStats=c("mean"),cutoff=1))
expect_silent(fs<-makeFilterStats(assay(hdfSCE),filterStats="var"))
expect_silent(defaultNDims(hdfObj,"PCA"))
#add pca to it
nDim<-3
expect_silent(dr3<-makeReducedDims(hdfObj,reducedDims="PCA",maxDims=nDim))
expect_equal(defaultNDims(dr3,"PCA"),3)
#test directly on hdf5
expect_silent(dr3<-makeReducedDims(assay(hdfObj),reducedDims="PCA",maxDims=nDim))
#test transformation -- need make CE object
expect_silent(clustNothing1 <- clusterSingle(hdfObj,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
expect_silent(transformation(clustNothing1)<-function(x){exp(x)})
expect_equal(exp(assay(clustNothing1)),unname(transformData(clustNothing1)))
})
test_that("filterData works as expected",{
###Cutoff filter
set.seed(352)
filter2<-matrix(rnorm(2*nrow(sceSimData)),ncol=2)
colnames(filter2)<-c("Filter1","Filter2")
expect_silent(clusterExperiment:::filterStats(sceSimData)<-filter2)
tf<-filter2[,"Filter1"]>1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-abs(filter2[,"Filter1"])>1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,absolute=TRUE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-abs(filter2[,"Filter1"])<1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,keepLarge=FALSE,absolute=TRUE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-filter2[,"Filter1"]<1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,keepLarge=FALSE,absolute=FALSE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
#percentile number filter
tf<-order(filter2[,"Filter1"],decreasing=TRUE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order(abs(filter2[,"Filter1"]),decreasing=TRUE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,absolute=TRUE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order(abs(filter2[,"Filter1"]),decreasing=FALSE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,keepLarge=FALSE,absolute=TRUE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order((filter2[,"Filter1"]),decreasing=FALSE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,keepLarge=FALSE,absolute=FALSE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
###Need to add test for percentile in (0,1)
})
test_that("getReducedData works as promised",{
expect_silent(clustNothingDimRed <- clusterSingle(sceSimDataDimRed,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
expect_silent(clustNothing <- clusterSingle(sceSimData,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
#dimReduce
expect_silent(out1<-getReducedData(clustNothing,reduceMethod="PCA"))
expect_true("PCA" %in% reducedDimNames(out1))
expect_equal(colData(out1),colData(clustNothing))
expect_equal(rownames(out1),rownames(clustNothing))
expect_equal(colnames(out1),colnames(clustNothing))
expect_equal(metadata(out1),metadata(clustNothing))
expect_warning(getReducedData(clustNothingDimRed,reduceMethod="PCA"),"will not add reduced dataset to object because already exists method with that name")
## FIXME:
#expect_silent(
out1<-getReducedData(clustNothing,
reduceMethod="PCA",reducedDimName="MyPCA")
#)
expect_true("MyPCA" %in% reducedDimNames(out1))
expect_false("PCA" %in% reducedDimNames(out1))
expect_warning(getReducedData(clustNothingDimRed,reduceMethod="PCA",nDim=200,reducedDimName="MyPCA",returnValue="list"),"requesting an existing dimensionality reduction but with greater number of dimensions than available")
expect_silent(out3<-getReducedData(clustNothingDimRed,reduceMethod="PCA",nDim=50,reducedDimName="MyPCA",returnValue="list"))
expect_equal(nrow(out3$dataMatrix),50)
#filters
expect_silent(out2<-getReducedData(clustNothing,reduceMethod="mad",nDim=50))
expect_true("mad_clusterSingle" %in% filterNames(out2))
expect_true("filteredBy_mad_clusterSingle" %in% reducedDimNames(out2))
expect_equal(ncol(reducedDim(out2,"filteredBy_mad_clusterSingle")),50)
expect_silent(out5<-getReducedData(clustNothing,reduceMethod="mad",filterIgnoresUnassigned=FALSE))
expect_true("mad" %in% filterNames(out5))
expect_true("filteredBy_mad" %in% reducedDimNames(out5))
})
test_that("makeFilterStats works as promised",{
expect_silent(fs<-makeFilterStats(mat,filterStats="var"))
expect_silent(fs<-makeFilterStats(mat,filterStats=c("mean","var")))
#does all:
expect_silent(fs<-makeFilterStats(mat))
for(ii in listBuiltInFilterStats()){
if(ii=="abscv"){
x<-abs(sqrt(apply(mat,1,var))/apply(mat,1,mean))
x<-unname(x)
}
else x<-(unname(apply(mat,1,ii)))
x<-DataFrame(x)
colnames(x)<-ii
expect_equivalent(clusterExperiment:::filterStats(fs,type=ii),x)
}
expect_silent(fs<-makeFilterStats(cc,filterStats="var"))
expect_silent(fs<-makeFilterStats(cc,filterStats=c("mean","var")))
expect_silent(fs<-makeFilterStats(cc))
expect_silent(fs<-makeFilterStats(cc,whichClusterIgnore="Cluster1"))
expect_silent(fs<-makeFilterStats(se))
expect_silent(fs<-makeFilterStats(se,filterStats="var"))
expect_silent(fs<-makeFilterStats(se,filterStats=c("mean","var")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var")))
expect_silent(fs<-makeFilterStats(fs,filterStats=c("var")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var")))
expect_silent(fs<-makeFilterStats(fs,filterStats=c("abscv")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var","abscv")))
expect_silent(fs<-makeFilterStats(sceSimData,filterStats="var"))
expect_silent(fs<-makeFilterStats(sceSimData,filterStats=c("mean","var")))
expect_silent(fs<-makeFilterStats(sceSimData))
###Check getting out correct ones.
contData<-simData[,1:20]
expect_silent(temp<-makeFilterStats(contData))
v<-apply(contData,1,var)
expect_equal(clusterExperiment:::filterStats(temp,type="var"),DataFrame("var"=v))
#should keep top 10
expect_silent(x<-filterData(temp,percentile=10,filterStats="var",keepLarge=TRUE,absolute=FALSE))
whTop<-order(v,decreasing=TRUE)[1:10]
expect_equal(DataFrame("var"=v[whTop]),clusterExperiment:::filterStats(x,type="var"))
expect_equal(contData[whTop,],unname(assay(x)))
###Check getting out correct ones with transformation (isCount=TRUE)
countData<-simCount[,1:20]
expect_silent(temp<-makeFilterStats(countData,isCount=TRUE))
v<-apply(log2(countData+1),1,var)
expect_equal(clusterExperiment:::filterStats(temp,type="var"),DataFrame("var"=v))
#should keep top 10
expect_silent(x<-filterData(temp,percentile=10,filterStats="var",keepLarge=TRUE,absolute=FALSE))
whTop<-order(v,decreasing=TRUE)[1:10]
expect_equal(DataFrame("var"=v[whTop]),clusterExperiment:::filterStats(x,type="var"))
expect_equal(log2(countData[whTop,]+1),unname(transformData(x,isCount=TRUE)))
})
test_that("defaultNDims works",{
nDim<-3
expect_silent(dr3<-makeReducedDims(ceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(defaultNDims(dr3,"PCA"),3)
expect_equal(defaultNDims(SingleCellExperiment(mat),"PCA"),min(dim(mat)))
expect_equal(defaultNDims(SingleCellExperiment(ceSimData),"PCA"),50)
})
epurdom/clusterCells documentation built on April 28, 2024, 8:14 p.m.
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context("Dimensionality Reduction")
test_that("makeReducedDims works as promised",{
# check for all objects that get expected
nDim<-3
true3<-abs(reducedDim(sceSimDataDimRed,"PCA")[,1:nDim])
#note: cc gives rownames to everything, so need to unname it
expect_silent(dr3<-makeReducedDims(ceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(unname(true3),unname(abs(reducedDim(dr3,"PCA"))))
expect_silent(dr3<-makeReducedDims(simData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
expect_silent(dr3<-makeReducedDims(seSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
expect_silent(dr3<-makeReducedDims(sceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(true3,abs(reducedDim(dr3,"PCA")))
#check don't lose them if call on existing object
expect_silent(dr<-makeReducedDims(sceSimDataDimRed,reducedDims="PCA",maxDims=nDim))
expect_equal(reducedDims(sceSimDataDimRed),reducedDims(dr))
#check with maxDims<1 (picks 4 of dimensions apparently -- never checked was correct)
expect_silent(dr2<-makeReducedDims(cc,reducedDims="PCA",maxDims=0.5))
expect_equal(abs(reducedDim(dr2,"PCA")[,1:4]), abs(reducedDim(dr2,"PCA")))
})
test_that("Filter functions work as expected",{
#when there are no filters
expect_silent(filterNames(sceSimData))
expect_equal(clusterExperiment:::filterStats(sceSimData),rowData(sceSimData)[,"b",drop=FALSE])
expect_error(clusterExperiment:::filterStats(sceSimData,type="Filter1"),"None of the values of 'type' argument are valid filter names")
##Redo when no rowData.
testSe<-SummarizedExperiment(mat)
expect_equal(length(filterNames(testSe)),0)
expect_equal(ncol(clusterExperiment:::filterStats(testSe)),0)
expect_error(clusterExperiment:::filterStats(testSe,type="Filter1"),"There are no filter statistics saved for this object")
##Redo when no valid filters.
testSe<-SummarizedExperiment(mat,rowData=gData[,c("a","c")])
expect_equal(length(filterNames(testSe)),0)
expect_equal(ncol(clusterExperiment:::filterStats(testSe)),0)
expect_error(clusterExperiment:::filterStats(testSe,type="Filter1"),"None of the values of 'type' argument are valid filter names")
set.seed(352)
filter<-rnorm(nrow(sce))
filter2<-matrix(rnorm(2*nrow(sce)),ncol=2)
#tests on adding unnamed filters
#value type of replace:
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest,type="Filter1")<-filter)
expect_equal(colnames(clusterExperiment:::filterStats(sceTest)),c("b","Filter1"))
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest,type=c("Filter1","Filter2"))<-filter2)
expect_equal(colnames(clusterExperiment:::filterStats(sceTest)),c("b","Filter1","Filter2"))
#matrix type of replace:
sceTest<-sce
expect_error(clusterExperiment:::filterStats(sceTest)<-filter,"must give matrix of values with column names")
sceTest<-sce
expect_error(clusterExperiment:::filterStats(sceTest)<-filter2,"must give matrix of values with column names")
####Now work with existing filters
colnames(filter2)<-c("Filter1","Filter2")
sceTest<-sce
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
expect_silent(filterNames(sceTest))
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(b=rowData(sce)[,"b"],filter2))
expect_equivalent(clusterExperiment:::filterStats(sceTest,type="Filter1"),DataFrame(filter2)[,"Filter1",drop=FALSE])
expect_error(clusterExperiment:::filterStats(sceTest,type="Myfilter"),"None of the values of 'type' argument are valid filter names")
expect_error(clusterExperiment:::filterStats(sceTest,type=1),"unable to find an inherited method")
##Check Replace with existing filters in place and check actually change them, etc.
sceTest<-SummarizedExperiment(assay(sce))
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
filter3<-filter2+1
expect_silent(clusterExperiment:::filterStats(sceTest) <- filter3)
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(filter3))
sceTest<-SummarizedExperiment(assay(sce))
expect_silent(clusterExperiment:::filterStats(sceTest)<-filter2)
filter4<-filter2+5
colnames(filter4)[2]<-"Myfilter"
expect_silent(clusterExperiment:::filterStats(sceTest) <- filter4)
expect_equivalent(clusterExperiment:::filterStats(sceTest),DataFrame(filter4[,"Filter1",drop=FALSE],filter2[,"Filter2",drop=FALSE],filter4[,"Myfilter",drop=FALSE]))
#Need more checks about replacement, etc.
})
test_that("reduce and filter work with hdf5",{
expect_silent(filterNames(hdfSCE))
expect_silent(f1<-filterData(hdfSCE,filterStats="Filter1",cutoff=1))
expect_silent(f1<-filterData(hdfSCE,filterStats="Filter1",cutoff=1))
expect_silent(fs<-makeFilterStats(hdfSCE,filterStats="var"))
expect_silent(fs<-makeFilterStats(hdfSCE,filterStats=c("mean","var")))
expect_silent(out<-filterData(fs,filterStats=c("mean"),cutoff=1))
expect_silent(fs<-makeFilterStats(assay(hdfSCE),filterStats="var"))
expect_silent(defaultNDims(hdfObj,"PCA"))
#add pca to it
nDim<-3
expect_silent(dr3<-makeReducedDims(hdfObj,reducedDims="PCA",maxDims=nDim))
expect_equal(defaultNDims(dr3,"PCA"),3)
#test directly on hdf5
expect_silent(dr3<-makeReducedDims(assay(hdfObj),reducedDims="PCA",maxDims=nDim))
#test transformation -- need make CE object
expect_silent(clustNothing1 <- clusterSingle(hdfObj,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
expect_silent(transformation(clustNothing1)<-function(x){exp(x)})
expect_equal(exp(assay(clustNothing1)),unname(transformData(clustNothing1)))
})
test_that("filterData works as expected",{
###Cutoff filter
set.seed(352)
filter2<-matrix(rnorm(2*nrow(sceSimData)),ncol=2)
colnames(filter2)<-c("Filter1","Filter2")
expect_silent(clusterExperiment:::filterStats(sceSimData)<-filter2)
tf<-filter2[,"Filter1"]>1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-abs(filter2[,"Filter1"])>1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,absolute=TRUE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-abs(filter2[,"Filter1"])<1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,keepLarge=FALSE,absolute=TRUE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-filter2[,"Filter1"]<1
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",cutoff=1,keepLarge=FALSE,absolute=FALSE))
expect_equal(NROW(f1),sum(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
#percentile number filter
tf<-order(filter2[,"Filter1"],decreasing=TRUE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order(abs(filter2[,"Filter1"]),decreasing=TRUE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,absolute=TRUE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order(abs(filter2[,"Filter1"]),decreasing=FALSE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,keepLarge=FALSE,absolute=TRUE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
tf<-order((filter2[,"Filter1"]),decreasing=FALSE)[1:10]
expect_silent(f1<-filterData(sceSimData,filterStats="Filter1",percentile=10,keepLarge=FALSE,absolute=FALSE))
expect_equal(NROW(f1),length(tf))
expect_equal(assay(f1),assay(sceSimData)[tf,])
###Need to add test for percentile in (0,1)
})
test_that("getReducedData works as promised",{
expect_silent(clustNothingDimRed <- clusterSingle(sceSimDataDimRed,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
expect_silent(clustNothing <- clusterSingle(sceSimData,
reduceMethod = "none",
mainClusterArgs=list( clusterArgs=list(k=3),clusterFunction=listBuiltInFunctions()[[1]]),
subsample=FALSE, sequential=FALSE, isCount=FALSE))
#dimReduce
expect_silent(out1<-getReducedData(clustNothing,reduceMethod="PCA"))
expect_true("PCA" %in% reducedDimNames(out1))
expect_equal(colData(out1),colData(clustNothing))
expect_equal(rownames(out1),rownames(clustNothing))
expect_equal(colnames(out1),colnames(clustNothing))
expect_equal(metadata(out1),metadata(clustNothing))
expect_warning(getReducedData(clustNothingDimRed,reduceMethod="PCA"),"will not add reduced dataset to object because already exists method with that name")
## FIXME:
#expect_silent(
out1<-getReducedData(clustNothing,
reduceMethod="PCA",reducedDimName="MyPCA")
#)
expect_true("MyPCA" %in% reducedDimNames(out1))
expect_false("PCA" %in% reducedDimNames(out1))
expect_warning(getReducedData(clustNothingDimRed,reduceMethod="PCA",nDim=200,reducedDimName="MyPCA",returnValue="list"),"requesting an existing dimensionality reduction but with greater number of dimensions than available")
expect_silent(out3<-getReducedData(clustNothingDimRed,reduceMethod="PCA",nDim=50,reducedDimName="MyPCA",returnValue="list"))
expect_equal(nrow(out3$dataMatrix),50)
#filters
expect_silent(out2<-getReducedData(clustNothing,reduceMethod="mad",nDim=50))
expect_true("mad_clusterSingle" %in% filterNames(out2))
expect_true("filteredBy_mad_clusterSingle" %in% reducedDimNames(out2))
expect_equal(ncol(reducedDim(out2,"filteredBy_mad_clusterSingle")),50)
expect_silent(out5<-getReducedData(clustNothing,reduceMethod="mad",filterIgnoresUnassigned=FALSE))
expect_true("mad" %in% filterNames(out5))
expect_true("filteredBy_mad" %in% reducedDimNames(out5))
})
test_that("makeFilterStats works as promised",{
expect_silent(fs<-makeFilterStats(mat,filterStats="var"))
expect_silent(fs<-makeFilterStats(mat,filterStats=c("mean","var")))
#does all:
expect_silent(fs<-makeFilterStats(mat))
for(ii in listBuiltInFilterStats()){
if(ii=="abscv"){
x<-abs(sqrt(apply(mat,1,var))/apply(mat,1,mean))
x<-unname(x)
}
else x<-(unname(apply(mat,1,ii)))
x<-DataFrame(x)
colnames(x)<-ii
expect_equivalent(clusterExperiment:::filterStats(fs,type=ii),x)
}
expect_silent(fs<-makeFilterStats(cc,filterStats="var"))
expect_silent(fs<-makeFilterStats(cc,filterStats=c("mean","var")))
expect_silent(fs<-makeFilterStats(cc))
expect_silent(fs<-makeFilterStats(cc,whichClusterIgnore="Cluster1"))
expect_silent(fs<-makeFilterStats(se))
expect_silent(fs<-makeFilterStats(se,filterStats="var"))
expect_silent(fs<-makeFilterStats(se,filterStats=c("mean","var")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var")))
expect_silent(fs<-makeFilterStats(fs,filterStats=c("var")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var")))
expect_silent(fs<-makeFilterStats(fs,filterStats=c("abscv")))
expect_equal(sort(filterNames(fs)),sort(c("b","mean","var","abscv")))
expect_silent(fs<-makeFilterStats(sceSimData,filterStats="var"))
expect_silent(fs<-makeFilterStats(sceSimData,filterStats=c("mean","var")))
expect_silent(fs<-makeFilterStats(sceSimData))
###Check getting out correct ones.
contData<-simData[,1:20]
expect_silent(temp<-makeFilterStats(contData))
v<-apply(contData,1,var)
expect_equal(clusterExperiment:::filterStats(temp,type="var"),DataFrame("var"=v))
#should keep top 10
expect_silent(x<-filterData(temp,percentile=10,filterStats="var",keepLarge=TRUE,absolute=FALSE))
whTop<-order(v,decreasing=TRUE)[1:10]
expect_equal(DataFrame("var"=v[whTop]),clusterExperiment:::filterStats(x,type="var"))
expect_equal(contData[whTop,],unname(assay(x)))
###Check getting out correct ones with transformation (isCount=TRUE)
countData<-simCount[,1:20]
expect_silent(temp<-makeFilterStats(countData,isCount=TRUE))
v<-apply(log2(countData+1),1,var)
expect_equal(clusterExperiment:::filterStats(temp,type="var"),DataFrame("var"=v))
#should keep top 10
expect_silent(x<-filterData(temp,percentile=10,filterStats="var",keepLarge=TRUE,absolute=FALSE))
whTop<-order(v,decreasing=TRUE)[1:10]
expect_equal(DataFrame("var"=v[whTop]),clusterExperiment:::filterStats(x,type="var"))
expect_equal(log2(countData[whTop,]+1),unname(transformData(x,isCount=TRUE)))
})
test_that("defaultNDims works",{
nDim<-3
expect_silent(dr3<-makeReducedDims(ceSimData,reducedDims="PCA",maxDims=nDim))
expect_equal(defaultNDims(dr3,"PCA"),3)
expect_equal(defaultNDims(SingleCellExperiment(mat),"PCA"),min(dim(mat)))
expect_equal(defaultNDims(SingleCellExperiment(ceSimData),"PCA"),50)
})
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