exec/testPhyloFun.R
In groupschoof/PhyloFun: Annotates Proteins with Gene Ontology (GO) Terms
require( PhyloFun )
# Initialize test data:
#######################
# Test tree is midpoint rooted!
phylo.tree <- read.tree(project.file.path( 'test_tree.newick'))
fl <- file(project.file.path('test_annotations_2.tbl'),"r")
annotation.matrix <- unserialize(fl)
close(fl)
# Test roundBranchLengths
print("Testing roundBranchLengths(...)")
res.roundBranchLengths <- roundBranchLengths( phylo.tree )
exp.roundBranchLengths <- c( 0.01, 0.90, 1.17, 0.13, 0.13, 0.11, 0.62, 0.75,
0.16, 0.27, 0.31, 0.16, 0.10, 0.20, 0.59, 0.23,
0.07, 0.14, 0.67, 0.57 )
# print( phylo.tree$edge.length )
# print( res.roundBranchLengths$edge.length )
checkEquals( res.roundBranchLengths$edge.length, exp.roundBranchLengths )
# Test cumulativeBranchLengthsToRoot
print("Testing cumulativeBranchLengthsToRoot(...)")
res.cumulativeBranchLengthsToRoot <- cumulativeBranchLengthsToRoot( 2, phylo.tree )
exp.cumulativeBranchLengthsToRoot <- 1.17788
checkEquals( res.cumulativeBranchLengthsToRoot, exp.cumulativeBranchLengthsToRoot )
res.cumulativeBranchLengthsToRoot <- cumulativeBranchLengthsToRoot( 7, phylo.tree )
checkEquals( res.cumulativeBranchLengthsToRoot, exp.cumulativeBranchLengthsToRoot )
# Test maxDistanceToRoot
print("Testing maxDistanceToRoot(...)")
res.maxDistanceToRoot <- maxDistanceToRoot( phylo.tree )
exp.maxDistanceToRoot <- 1.17788
checkEquals( res.maxDistanceToRoot, exp.maxDistanceToRoot )
# Test mostAppropriateAnnotation
print("Testing mostAppropriateAnnotation(...)")
preds <- setNames( c( 0.5, 0.5, 0.5 ),
c( 'GO:0009267 & GO:0036170 & GO:0036180 & GO:0071216', 'unknown',
'GO:0009267 & GO:0036170' )
)
res.mostAppropriateAnnotation <- mostAppropriateAnnotation( preds )
exp.mostAppropriateAnnotation <- c( 'GO:0009267', 'GO:0036170', 'GO:0036180', 'GO:0071216' )
checkEquals( res.mostAppropriateAnnotation, exp.mostAppropriateAnnotation )
# Some PhyloFun results to be arguments for tesed methods:
f <- file( project.file.path( 'test_phyloFun_serialized_result.bin' ), 'r' )
phylo.fun.rslt <- unserialize( f )
close( f )
# Test highScoringAnnotations
print("Testing highScoringAnnotations(...)")
res.highScoringAnnotations <- highScoringAnnotations( phylo.fun.rslt,
'"Query_Q9XWC3"' )
checkEquals( res.highScoringAnnotations$biological_process,
c( "GO:0009267", "GO:0036170", "GO:0036180", "GO:0071216" ) )
checkTrue( is.na( res.highScoringAnnotations$cellular_component ) )
checkTrue( is.na( res.highScoringAnnotations$molecular_function ) )
f <- file( project.file.path(
'Query_Q9NTK1_phyloFun_annotations_serialized.txt'), "r" )
q9ntk1.pf.res <- unserialize( f )
close( f )
res.highScoringAnnotations <- highScoringAnnotations( q9ntk1.pf.res,
'"Query_Q9NTK1"' )
checkEquals( res.highScoringAnnotations$biological_process,
c( "GO:0045892", "GO:0046677", "GO:0051260", "GO:0001514", "GO:0007420",
"GO:0007426", "GO:0051960" ) )
checkEquals( res.highScoringAnnotations$cellular_component,
c( "GO:0005739", "GO:0005634", "GO:0005886", "GO:0019005" ) )
checkEquals( res.highScoringAnnotations$molecular_function,
c( "GO:0003730", "GO:0008135" ) )
# Test tree whose Baysian network representation has nodes with unreachable
# states:
phylo.tree.unreachbl.stts <- read.tree(
project.file.path( 'test_tree_unreachbl_stts.newick')
)
# Test predictionsToCharacterVector
print("Testing predictionsToCharacterVector(...)")
res.predictionsToCharacterVector <- predictionsToCharacterVector( phylo.fun.rslt, '"Query_Q9XWC3"' )
exp.predictionsToCharacterVector <- c( 'GO:0009267', 'GO:0036170',
'GO:0036180', 'GO:0071216' )
checkEquals( res.predictionsToCharacterVector, exp.predictionsToCharacterVector )
res.predictionsToCharacterVector <- predictionsToCharacterVector( phylo.fun.rslt, 'Query_Q9XWC3' )
checkEquals( res.predictionsToCharacterVector, exp.predictionsToCharacterVector )
checkEquals( predictionsToCharacterVector( NULL, 'Query_Q9XWC3' ), 'unknown' )
checkEquals( predictionsToCharacterVector( list(), 'Query_Q9XWC3' ), 'unknown' )
phylo.fun.rslt.unknown <- phylo.fun.rslt
phylo.fun.rslt.unknown$biological_process$pred[ '"Query_Q9XWC3"' ][[1]][[ 1, 'unknown' ]] <- 0.6
checkEquals( predictionsToCharacterVector( phylo.fun.rslt.unknown, 'Query_Q9XWC3' ), 'unknown')
# Test goTermPredictionTable
print("Testing goTermPredictionTable(...)")
res.goTermPredictionTable <- goTermPredictionTable( phylo.fun.rslt, 'Query_Q9XWC3' )
checkEquals( class( res.goTermPredictionTable ), 'data.frame' )
checkEquals( ncol( res.goTermPredictionTable ), 8 )
checkTrue( nrow( res.goTermPredictionTable ) > 0 )
res.goTermPredictionTable <- goTermPredictionTable( phylo.fun.rslt.unknown, 'Query_Q9XWC3' )
checkEquals( res.goTermPredictionTable,
matrix(
c( NA, 'unknown', NA, NA, NA, NA, NA, NA ), byrow=T, ncol=8,
dimnames=list( c(), c( "id", "name", "term_type", "acc", "is_obsolete",
"is_root", "is_relation", "relation_distance" ) )
)
)
# Initialize a database connection to the Gene Ontology
go.con <- connectToGeneOntology()
# Test getDescendantNodes
print("Testing getDescendantNodes(...)")
res.getDescendantNodes <- getDescendantNodes( phylo.tree, 12 )
exp.getDescendantNodes <- c( 13, 14 )
checkEquals( res.getDescendantNodes, exp.getDescendantNodes )
checkTrue( is.null( getDescendantNodes( phylo.tree, 8 ) ) )
# Test annotationMatrixForBayesNetwork
print("Testing annotationMatrixForBayesNetwork(...)")
annot.df.diag.evidnc <- read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function
GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process")
res.annotationMatrixForBayesNetwork <- annotationMatrixForBayesNetwork( annot.df.diag.evidnc )
# print( res.annotationMatrixForBayesNetwork )
checkTrue( ! is.null( res.annotationMatrixForBayesNetwork ) )
checkEquals( class( res.annotationMatrixForBayesNetwork ), 'matrix' )
checkEquals( ncol( res.annotationMatrixForBayesNetwork ),
length( unique( annot.df.diag.evidnc[ , 3 ] ) ) )
checkEquals( rownames( res.annotationMatrixForBayesNetwork ), 'GO' )
checkEquals( colnames( res.annotationMatrixForBayesNetwork ),
surroundEachWithQuotes( unique( annot.df.diag.evidnc[ , 3 ] ) ) )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"A0K2M8"' ]],
"GO:0004525 & GO:0004530 & GO:0042269"
)
checkTrue( is.null( annotationMatrixForBayesNetwork( NULL ) ) )
# Check with homologs missing experimentally verified function annotations:
res.annotationMatrixForBayesNetwork <- annotationMatrixForBayesNetwork(
annot.df.diag.evidnc, all.accessions=c( unique( annot.df.diag.evidnc[ , 3 ] ),
'Protein_A', 'Protein_B' )
)
# print( res.annotationMatrixForBayesNetwork )
checkTrue( ! is.null( res.annotationMatrixForBayesNetwork ) )
checkEquals( ncol( res.annotationMatrixForBayesNetwork ),
length( unique( annot.df.diag.evidnc[ , 3 ] ) ) + 2 )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"Protein_A"' ]],
'unknown' )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"Protein_B"' ]],
'unknown' )
# Test annotationToString
print("Testing annotationToString(...)")
res.annotationToString <- annotationToString( c( "GO_A", "GO_B", "GO_C" ) )
exp.annotationToString <- "GO_A & GO_B & GO_C"
checkEquals( res.annotationToString, exp.annotationToString )
# Test goTypeAnnotationMatrices
print("Testing goTypeAnnotationMatrices(...)")
annotation.df <- read.table( stringsAsFactors=FALSE, text=
"GO:0006275 IEA A0K2M8 biological_process
GO:0003688 IC A0K2M8 molecular_function
GO:9696967 IC A0K2M8 molecular_function
")
go.type.annos.no.restriction <- goTypeAnnotationMatrices( annotation.df, NULL )
# print( go.type.annos.no.restriction )
checkEquals( names( go.type.annos.no.restriction ), c( 'biological_process', 'cellular_component', 'molecular_function' ) )
anno.df <- go.type.annos.no.restriction$biological_process
checkEquals( anno.df[[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ]],
'GO:0006275' )
anno.df <- go.type.annos.no.restriction$molecular_function
checkTrue( 'GO:0003688' %in% anno.df[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ] )
go.type.annos.restriction <- goTypeAnnotationMatrices( annotation.df )
anno.df <- go.type.annos.restriction$molecular_function
checkEquals( nrow( anno.df ), 1 )
checkEquals( anno.df[[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ]],
'GO:0003688' )
# Test goAnnotationSpaceList
print("Testing goAnnotationSpaceList(...)")
go.type.annos.lst <- list(
biological_process=read.table( stringsAsFactors=FALSE, text=
"GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process"),
cellular_component=as.data.frame( stringsAsFactors=FALSE, matrix( nrow=0, ncol=4 ) ),
molecular_function=read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function"
))
res.annotationSpace <- goAnnotationSpaceList( go.type.annos.lst, unknown.annot=NULL )
exp.annotationSpace <- list(biological_process = list(c("GO:0042263", "GO:0042267"),
c("GO:0042269")), cellular_component = list(), molecular_function = list(c("GO:0004525",
"GO:0004530"), c("GO:0004527")))
# print( res.annotationSpace )
checkEquals( res.annotationSpace, exp.annotationSpace )
# With UNKOWN annotation
res.annotationSpace <- goAnnotationSpaceList( go.type.annos.lst, unknown.annot='unknown' )
exp.annotationSpace.with.unknown <- lapply( exp.annotationSpace, function(x) append( x, 'unknown' ) )
# print( res.annotationSpace )
checkEquals( res.annotationSpace, exp.annotationSpace.with.unknown )
# Test findMatchingRow
print("Testing findMatchingRow(...)")
p.mut.tbl <- as.matrix( read.table( header=T, text=
" p.mutation.Sequence.Distance min.Sequence.Distance min.Domain.Architecture.Distance min.Euclidean.Distance.To.Origin max.Sequence.Distance max.Domain.Architecture.Distance max.Euclidean.Distance.To.Origin
1 0.00 0.00 0.00 0.00 0.00 0 0.00
2 0.50 0.03 0.00 0.03 0.03 0 0.03
3 0.67 0.06 0.00 0.06 0.06 0 0.06
4 0.75 0.07 0.00 0.07 0.07 0 0.07
5 0.83 0.11 0.00 0.11 0.11 0 0.11
6 0.86 0.12 0.00 0.12 1.50 1 1.98
7 0.87 1.51 0.00 1.51 1.52 1 1.82
8 0.88 1.53 0.00 1.53 1.54 1 1.84
9 0.89 1.55 0.00 1.56 1.58 1 1.87
10 0.90 1.59 0.16 1.61 1.60 1 1.89
11 0.91 1.61 0.00 1.62 1.66 1 2.29
12 0.92 1.67 0.06 1.71 1.92 1 2.14" )
)
mtch.col.1 <- findMatchingRow( p.mut.tbl, 0.8, 4 )
# print( mtch.col.1 )
exp.mtch.col.1 <- c( 0.86, 0.12, 0.00, 0.12, 1.50, 1, 1.98 )
checkEquals( exp.mtch.col.1, mtch.col.1 )
mtch.col.2 <- findMatchingRow( p.mut.tbl, 1.54, 4 )
# print( mtch.col.2 )
exp.mtch.col.2 <- c( 0.88, 1.53, 0.00, 1.53, 1.54, 1, 1.84 )
checkEquals( exp.mtch.col.2, mtch.col.2 )
mtch.col.3 <- findMatchingRow( p.mut.tbl, 7272, 4 )
# print( mtch.col.3 )
exp.mtch.col.3 <- c( 0.92, 1.67, 0.06, 1.71, 1.92, 1, 2.14)
checkEquals( exp.mtch.col.3, mtch.col.3 )
mtch.col.4 <- findMatchingRow(
matrix( c(0.33, 0.66, 1.0, 0.5, 1.0, 1.5), ncol=2 ), 0.9, 1
)
checkEquals( c(0.66, 1.0), mtch.col.4 )
# Test eliminateUnreachableStates
print("Testing eliminateUnreachableStates(...)")
cpt.states <- c( 'GO_1', 'GO_2', 'unknown' )
cpt <- matrix( c( 0.5, 0.5, 0, 1, 0, 0, 0.5, 0.5, 0 ),
nrow=3, ncol=3, byrow=T,
dimnames=list( cpt.states, cpt.states )
)
res.eliminateUnreachableStates <- eliminateUnreachableStates( cpt )
exp.eliminateUnreachableStates <- matrix( c( 0.5, 0.5, 1, 0, 0.5, 0.5 ),
nrow=3, ncol=2, byrow=T,
dimnames=list( cpt.states, c( 'GO_1', 'GO_2' ) )
)
checkEquals( res.eliminateUnreachableStates, exp.eliminateUnreachableStates )
# No state is unreachable -> check equality:
cpt <- matrix( c( 0.5, 0.3, 0.2, 1, 0, 0, 0.5, 0.5, 0 ),
nrow=3, ncol=3, byrow=T,
dimnames=list( cpt.states, cpt.states )
)
res.eliminateUnreachableStates <- eliminateUnreachableStates( cpt )
checkEquals( res.eliminateUnreachableStates, cpt )
# Test conditionalProbabilityTable
print("Testing conditionalProbabilityTable(...)")
ua <- list( c( "GO_1", "GO_2", "GO_3" ), c( "GO_1", "GO_2" ), "GO_3" )
p.mut.tbl.lst <- list()
p.mut.tbl.lst[[ "GO_1" ]] <- matrix( c(0.33, 0.66, 1.0, 0.5, 1.0, 1.5), ncol=2 )
p.mut.tbl.lst[[ "GO_2" ]] <- matrix( c(0.25, 0.5, 0.75, 0.5, 1.0, 1.5), ncol=2 )
p.mut.tbl.lst[[ "GO_3" ]] <- matrix( c(0.45, 0.75, 0.98, 0.5, 1.0, 1.5), ncol=2 )
# print( p.mut.tbl.lst )
cpt.anno <- c( ua, 'unknown' )
cpt.anno.str <- as.character( lapply( cpt.anno, annotationToString ) )
con.prbs.tbl <- conditionalProbabilityTable( 0.9, cpt.anno, cpt.anno.str,
p.mut.tbl.lst, 1 )
# print( con.prbs.tbl )
checkEquals( 1.0, sum( con.prbs.tbl[ , 1 ] ) )
# print( 1 - p.mut.tbl.lst[[ 1 ]][[ 2, 1 ]] )
checkEquals( 1 - p.mut.tbl.lst[[ 3 ]][[ 2, 1 ]], con.prbs.tbl[[ 1, 1 ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 1 ] ) )
checkEquals( 1 - p.mut.tbl.lst[[ 1 ]][[ 2, 1 ]], con.prbs.tbl[[ 2, 2 ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 3 ] ) )
checkEquals( 1 - p.mut.tbl.lst[[ 3 ]][[ 2, 1 ]], con.prbs.tbl[[ 3, 3 ]] )
checkEquals( 0, con.prbs.tbl[[ 'unknown', 'unknown' ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 'unknown' ] ) )
# Test conditionalProbabilityTables
print("Testing conditionalProbabilityTables(...)")
phylo.tree.4.brnch.lngths <- read.tree( project.file.path(
'test_tree_4_branch_lengths.newick' ) )
annos <- c( ua, 'unknown' )
annos.str <- as.character( lapply( annos, annotationToString ) )
res.conditionalProbabilityTables <- conditionalProbabilityTables(
unique( phylo.tree.4.brnch.lngths$edge.length ),
annos, annos.str, p.mut.tbl.lst, mutTblLengthColIndx=1 )
# print( res.conditionalProbabilityTables )
checkEquals( names( res.conditionalProbabilityTables ),
as.character( unique( phylo.tree.4.brnch.lngths$edge.length ) ) )
# print( res.conditionalProbabilityTables[[ '0.59211' ]][ , 1 ])
checkEquals( setNames( c( 0.25, 0.25, 0.25, 0.25 ),
c( 'GO_1 & GO_2 & GO_3', 'GO_1 & GO_2', 'GO_3', 'unknown' ) ),
res.conditionalProbabilityTables[[ '0.59211' ]][ , 1 ]
)
# Test mutationProbability
print("Testing mutationProbability(...)")
anno <- c( "A", "B", "C" )
branch.length <- 0.5
mut.prob.tbls <- list( "A"=matrix( c(0.2, 0.5), nrow=1 ),
"B"=matrix( c(0.3, 0.5), nrow=1 ), "C"=matrix( c(0.4, 0.5), nrow=1 )
)
res.mutationProbability <- mutationProbability( anno, branch.length, mut.prob.tbls, 1 )
exp.mutationProbability <- 0.4
checkEquals( res.mutationProbability, exp.mutationProbability )
# Test get.node.label
print("Testing get.node.label(...)")
checkEquals(get.node.label(phylo.tree, 21), "21")
checkTrue(identical(class(get.node.label(phylo.tree, 11)), "character"))
# Test edge.to.formula
print("Testing edge.to.formula(...)")
# Test a tip's formula
indx <- which(phylo.tree$edge[, 2] == 1)
frml <- edge.to.formula(phylo.tree, indx)
checkTrue(identical(class(frml), 'formula'))
checkTrue(length(as.character(frml)) == 2)
checkTrue(grepl('[a-zA-Z]+', as.character(frml)[[2]], perl=T))
indx <- which(phylo.tree$edge[, 1] == (length(phylo.tree$tip.label)+1))[1]
frml <- edge.to.formula(phylo.tree, indx)
checkTrue(! grepl('[a-zA-Z]+', as.character(frml)[[2]], perl=T))
# Test bayesNodes
# Tree in which the state 'unknown' is unreachable
print("Testing bayesNodes(...)")
res.bayesNodes <- bayesNodes(
phylo.tree.unreachbl.stts, c( 'GO:0043047', 'unknown' )
)
exp.anno.space <- c( 'GO:0043047', 'unknown' )
# print( res.bayesNodes )
checkEquals( length( res.bayesNodes ), 38 )
root.cpt <- res.bayesNodes[[ 1 ]]
checkEquals( root.cpt[[ 'values' ]],
matrix( c( 1, 1 ), ncol=1, dimnames=list( exp.anno.space, c() ) )
)
# print( exp.anno.space )
# print( root.cpt[[ 'levels' ]] )
checkEquals( exp.anno.space, root.cpt[[ 'levels' ]] )
for( i in 2:length(res.bayesNodes) ) {
desc.cpt <- res.bayesNodes[[ i ]]
print(
checkEquals( desc.cpt[[ 'values' ]],
matrix( c( 0.96, 0.04, 1, 0 ), nrow=2, ncol=2,
dimnames=list( exp.anno.space, exp.anno.space)
)
)
)
print( checkEquals( exp.anno.space, desc.cpt[[ 'levels' ]] ) )
}
# Test tree without unreachable 'unknown' state:
annot.df <- read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function
GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process")
go.type.annos <- goTypeAnnotationMatrices( annot.df )
anno.space.lst <- goAnnotationSpaceList( go.type.annos )
bys.nds <- bayesNodes( phylo.tree, anno.space.lst$molecular_function )
checkTrue( length( bys.nds ) == 21 )
root.bys.nd <- bys.nds[[ 1 ]]
# print( root.bys.nd )
# print( uniq.annos )
# print( root.bys.nd$levels )
# print( root.bys.nd$values )
# print( anno.space.lst$molecular_function )
checkTrue( length( root.bys.nd$values ) == length( anno.space.lst$molecular_function ) )
checkEquals( root.bys.nd$levels, as.character( lapply( anno.space.lst$molecular_function, annotationToString ) ) )
# print(bys.nds[[2]]$values)
# Test create bayesian Network
print( "Testing create bayesian Network" )
plist <- compileCPT( bys.nds )
grain.res <- try( grain( plist ), silent=T )
checkTrue( ! identical( 'try-error', class( grain.res ) ) )
# Check conditional probability table for leaf '"A0KEC3"':
print( "Testing conditional probability tables of compiled Bayesian network" )
res.cpt <- as.matrix( plist$'"A0KEC3"' )
exp.states <- c( "GO:0004525 & GO:0004530", "GO:0004527", "unknown" )
checkTrue( ! is.null( res.cpt ) )
checkEquals( class( res.cpt ), c( "parray", "array" ) )
checkEquals( rownames( res.cpt ), exp.states )
checkEquals( colnames( res.cpt ), exp.states )
checkEquals( as.numeric( res.cpt[ , 1 ] ), c( 0.670, 0.165, 0.165 ) )
checkEquals( as.numeric( res.cpt[ , 2 ] ), c( 0.135, 0.730, 0.135 ) )
checkEquals( as.numeric( res.cpt[ , 3 ] ), c( 0.5, 0.5, 0 ) )
# Test getTipsWithNaAnnotation
print("Testing getTipsWithNaAnnotation(...)")
checkEquals( getTipsWithNaAnnotation( phylo.tree, annotation.matrix ),
c("\"Protein_1\"") )
# Test multiple NA annotations
am <- annotation.matrix
am[1, 6] <- NA
checkEquals(getTipsWithNaAnnotation(phylo.tree, am),
c("\"Protein_1\"", "\"A0AEI7\""))
checkEquals(
getTipsWithNaAnnotation(phylo.tree, am, negate=T),
c( "\"A0RLX8\"", "\"A0LE53\"", "\"A0PKB2\"", "\"A0Q3U6\"", "\"A0K2M8\"",
"\"A0KR35\"", "\"A0KEC3\"", "\"A0Q3U7\"", "\"A0L3I7\"")
)
# Clean Up:
dbDisconnect( go.con )
groupschoof/PhyloFun documentation built on May 17, 2019, 8:38 a.m.
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require( PhyloFun )
# Initialize test data:
#######################
# Test tree is midpoint rooted!
phylo.tree <- read.tree(project.file.path( 'test_tree.newick'))
fl <- file(project.file.path('test_annotations_2.tbl'),"r")
annotation.matrix <- unserialize(fl)
close(fl)
# Test roundBranchLengths
print("Testing roundBranchLengths(...)")
res.roundBranchLengths <- roundBranchLengths( phylo.tree )
exp.roundBranchLengths <- c( 0.01, 0.90, 1.17, 0.13, 0.13, 0.11, 0.62, 0.75,
0.16, 0.27, 0.31, 0.16, 0.10, 0.20, 0.59, 0.23,
0.07, 0.14, 0.67, 0.57 )
# print( phylo.tree$edge.length )
# print( res.roundBranchLengths$edge.length )
checkEquals( res.roundBranchLengths$edge.length, exp.roundBranchLengths )
# Test cumulativeBranchLengthsToRoot
print("Testing cumulativeBranchLengthsToRoot(...)")
res.cumulativeBranchLengthsToRoot <- cumulativeBranchLengthsToRoot( 2, phylo.tree )
exp.cumulativeBranchLengthsToRoot <- 1.17788
checkEquals( res.cumulativeBranchLengthsToRoot, exp.cumulativeBranchLengthsToRoot )
res.cumulativeBranchLengthsToRoot <- cumulativeBranchLengthsToRoot( 7, phylo.tree )
checkEquals( res.cumulativeBranchLengthsToRoot, exp.cumulativeBranchLengthsToRoot )
# Test maxDistanceToRoot
print("Testing maxDistanceToRoot(...)")
res.maxDistanceToRoot <- maxDistanceToRoot( phylo.tree )
exp.maxDistanceToRoot <- 1.17788
checkEquals( res.maxDistanceToRoot, exp.maxDistanceToRoot )
# Test mostAppropriateAnnotation
print("Testing mostAppropriateAnnotation(...)")
preds <- setNames( c( 0.5, 0.5, 0.5 ),
c( 'GO:0009267 & GO:0036170 & GO:0036180 & GO:0071216', 'unknown',
'GO:0009267 & GO:0036170' )
)
res.mostAppropriateAnnotation <- mostAppropriateAnnotation( preds )
exp.mostAppropriateAnnotation <- c( 'GO:0009267', 'GO:0036170', 'GO:0036180', 'GO:0071216' )
checkEquals( res.mostAppropriateAnnotation, exp.mostAppropriateAnnotation )
# Some PhyloFun results to be arguments for tesed methods:
f <- file( project.file.path( 'test_phyloFun_serialized_result.bin' ), 'r' )
phylo.fun.rslt <- unserialize( f )
close( f )
# Test highScoringAnnotations
print("Testing highScoringAnnotations(...)")
res.highScoringAnnotations <- highScoringAnnotations( phylo.fun.rslt,
'"Query_Q9XWC3"' )
checkEquals( res.highScoringAnnotations$biological_process,
c( "GO:0009267", "GO:0036170", "GO:0036180", "GO:0071216" ) )
checkTrue( is.na( res.highScoringAnnotations$cellular_component ) )
checkTrue( is.na( res.highScoringAnnotations$molecular_function ) )
f <- file( project.file.path(
'Query_Q9NTK1_phyloFun_annotations_serialized.txt'), "r" )
q9ntk1.pf.res <- unserialize( f )
close( f )
res.highScoringAnnotations <- highScoringAnnotations( q9ntk1.pf.res,
'"Query_Q9NTK1"' )
checkEquals( res.highScoringAnnotations$biological_process,
c( "GO:0045892", "GO:0046677", "GO:0051260", "GO:0001514", "GO:0007420",
"GO:0007426", "GO:0051960" ) )
checkEquals( res.highScoringAnnotations$cellular_component,
c( "GO:0005739", "GO:0005634", "GO:0005886", "GO:0019005" ) )
checkEquals( res.highScoringAnnotations$molecular_function,
c( "GO:0003730", "GO:0008135" ) )
# Test tree whose Baysian network representation has nodes with unreachable
# states:
phylo.tree.unreachbl.stts <- read.tree(
project.file.path( 'test_tree_unreachbl_stts.newick')
)
# Test predictionsToCharacterVector
print("Testing predictionsToCharacterVector(...)")
res.predictionsToCharacterVector <- predictionsToCharacterVector( phylo.fun.rslt, '"Query_Q9XWC3"' )
exp.predictionsToCharacterVector <- c( 'GO:0009267', 'GO:0036170',
'GO:0036180', 'GO:0071216' )
checkEquals( res.predictionsToCharacterVector, exp.predictionsToCharacterVector )
res.predictionsToCharacterVector <- predictionsToCharacterVector( phylo.fun.rslt, 'Query_Q9XWC3' )
checkEquals( res.predictionsToCharacterVector, exp.predictionsToCharacterVector )
checkEquals( predictionsToCharacterVector( NULL, 'Query_Q9XWC3' ), 'unknown' )
checkEquals( predictionsToCharacterVector( list(), 'Query_Q9XWC3' ), 'unknown' )
phylo.fun.rslt.unknown <- phylo.fun.rslt
phylo.fun.rslt.unknown$biological_process$pred[ '"Query_Q9XWC3"' ][[1]][[ 1, 'unknown' ]] <- 0.6
checkEquals( predictionsToCharacterVector( phylo.fun.rslt.unknown, 'Query_Q9XWC3' ), 'unknown')
# Test goTermPredictionTable
print("Testing goTermPredictionTable(...)")
res.goTermPredictionTable <- goTermPredictionTable( phylo.fun.rslt, 'Query_Q9XWC3' )
checkEquals( class( res.goTermPredictionTable ), 'data.frame' )
checkEquals( ncol( res.goTermPredictionTable ), 8 )
checkTrue( nrow( res.goTermPredictionTable ) > 0 )
res.goTermPredictionTable <- goTermPredictionTable( phylo.fun.rslt.unknown, 'Query_Q9XWC3' )
checkEquals( res.goTermPredictionTable,
matrix(
c( NA, 'unknown', NA, NA, NA, NA, NA, NA ), byrow=T, ncol=8,
dimnames=list( c(), c( "id", "name", "term_type", "acc", "is_obsolete",
"is_root", "is_relation", "relation_distance" ) )
)
)
# Initialize a database connection to the Gene Ontology
go.con <- connectToGeneOntology()
# Test getDescendantNodes
print("Testing getDescendantNodes(...)")
res.getDescendantNodes <- getDescendantNodes( phylo.tree, 12 )
exp.getDescendantNodes <- c( 13, 14 )
checkEquals( res.getDescendantNodes, exp.getDescendantNodes )
checkTrue( is.null( getDescendantNodes( phylo.tree, 8 ) ) )
# Test annotationMatrixForBayesNetwork
print("Testing annotationMatrixForBayesNetwork(...)")
annot.df.diag.evidnc <- read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function
GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process")
res.annotationMatrixForBayesNetwork <- annotationMatrixForBayesNetwork( annot.df.diag.evidnc )
# print( res.annotationMatrixForBayesNetwork )
checkTrue( ! is.null( res.annotationMatrixForBayesNetwork ) )
checkEquals( class( res.annotationMatrixForBayesNetwork ), 'matrix' )
checkEquals( ncol( res.annotationMatrixForBayesNetwork ),
length( unique( annot.df.diag.evidnc[ , 3 ] ) ) )
checkEquals( rownames( res.annotationMatrixForBayesNetwork ), 'GO' )
checkEquals( colnames( res.annotationMatrixForBayesNetwork ),
surroundEachWithQuotes( unique( annot.df.diag.evidnc[ , 3 ] ) ) )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"A0K2M8"' ]],
"GO:0004525 & GO:0004530 & GO:0042269"
)
checkTrue( is.null( annotationMatrixForBayesNetwork( NULL ) ) )
# Check with homologs missing experimentally verified function annotations:
res.annotationMatrixForBayesNetwork <- annotationMatrixForBayesNetwork(
annot.df.diag.evidnc, all.accessions=c( unique( annot.df.diag.evidnc[ , 3 ] ),
'Protein_A', 'Protein_B' )
)
# print( res.annotationMatrixForBayesNetwork )
checkTrue( ! is.null( res.annotationMatrixForBayesNetwork ) )
checkEquals( ncol( res.annotationMatrixForBayesNetwork ),
length( unique( annot.df.diag.evidnc[ , 3 ] ) ) + 2 )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"Protein_A"' ]],
'unknown' )
checkEquals( res.annotationMatrixForBayesNetwork[[ 'GO', '"Protein_B"' ]],
'unknown' )
# Test annotationToString
print("Testing annotationToString(...)")
res.annotationToString <- annotationToString( c( "GO_A", "GO_B", "GO_C" ) )
exp.annotationToString <- "GO_A & GO_B & GO_C"
checkEquals( res.annotationToString, exp.annotationToString )
# Test goTypeAnnotationMatrices
print("Testing goTypeAnnotationMatrices(...)")
annotation.df <- read.table( stringsAsFactors=FALSE, text=
"GO:0006275 IEA A0K2M8 biological_process
GO:0003688 IC A0K2M8 molecular_function
GO:9696967 IC A0K2M8 molecular_function
")
go.type.annos.no.restriction <- goTypeAnnotationMatrices( annotation.df, NULL )
# print( go.type.annos.no.restriction )
checkEquals( names( go.type.annos.no.restriction ), c( 'biological_process', 'cellular_component', 'molecular_function' ) )
anno.df <- go.type.annos.no.restriction$biological_process
checkEquals( anno.df[[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ]],
'GO:0006275' )
anno.df <- go.type.annos.no.restriction$molecular_function
checkTrue( 'GO:0003688' %in% anno.df[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ] )
go.type.annos.restriction <- goTypeAnnotationMatrices( annotation.df )
anno.df <- go.type.annos.restriction$molecular_function
checkEquals( nrow( anno.df ), 1 )
checkEquals( anno.df[[ which( anno.df[ , 3 ] == 'A0K2M8' ), 1 ]],
'GO:0003688' )
# Test goAnnotationSpaceList
print("Testing goAnnotationSpaceList(...)")
go.type.annos.lst <- list(
biological_process=read.table( stringsAsFactors=FALSE, text=
"GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process"),
cellular_component=as.data.frame( stringsAsFactors=FALSE, matrix( nrow=0, ncol=4 ) ),
molecular_function=read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function"
))
res.annotationSpace <- goAnnotationSpaceList( go.type.annos.lst, unknown.annot=NULL )
exp.annotationSpace <- list(biological_process = list(c("GO:0042263", "GO:0042267"),
c("GO:0042269")), cellular_component = list(), molecular_function = list(c("GO:0004525",
"GO:0004530"), c("GO:0004527")))
# print( res.annotationSpace )
checkEquals( res.annotationSpace, exp.annotationSpace )
# With UNKOWN annotation
res.annotationSpace <- goAnnotationSpaceList( go.type.annos.lst, unknown.annot='unknown' )
exp.annotationSpace.with.unknown <- lapply( exp.annotationSpace, function(x) append( x, 'unknown' ) )
# print( res.annotationSpace )
checkEquals( res.annotationSpace, exp.annotationSpace.with.unknown )
# Test findMatchingRow
print("Testing findMatchingRow(...)")
p.mut.tbl <- as.matrix( read.table( header=T, text=
" p.mutation.Sequence.Distance min.Sequence.Distance min.Domain.Architecture.Distance min.Euclidean.Distance.To.Origin max.Sequence.Distance max.Domain.Architecture.Distance max.Euclidean.Distance.To.Origin
1 0.00 0.00 0.00 0.00 0.00 0 0.00
2 0.50 0.03 0.00 0.03 0.03 0 0.03
3 0.67 0.06 0.00 0.06 0.06 0 0.06
4 0.75 0.07 0.00 0.07 0.07 0 0.07
5 0.83 0.11 0.00 0.11 0.11 0 0.11
6 0.86 0.12 0.00 0.12 1.50 1 1.98
7 0.87 1.51 0.00 1.51 1.52 1 1.82
8 0.88 1.53 0.00 1.53 1.54 1 1.84
9 0.89 1.55 0.00 1.56 1.58 1 1.87
10 0.90 1.59 0.16 1.61 1.60 1 1.89
11 0.91 1.61 0.00 1.62 1.66 1 2.29
12 0.92 1.67 0.06 1.71 1.92 1 2.14" )
)
mtch.col.1 <- findMatchingRow( p.mut.tbl, 0.8, 4 )
# print( mtch.col.1 )
exp.mtch.col.1 <- c( 0.86, 0.12, 0.00, 0.12, 1.50, 1, 1.98 )
checkEquals( exp.mtch.col.1, mtch.col.1 )
mtch.col.2 <- findMatchingRow( p.mut.tbl, 1.54, 4 )
# print( mtch.col.2 )
exp.mtch.col.2 <- c( 0.88, 1.53, 0.00, 1.53, 1.54, 1, 1.84 )
checkEquals( exp.mtch.col.2, mtch.col.2 )
mtch.col.3 <- findMatchingRow( p.mut.tbl, 7272, 4 )
# print( mtch.col.3 )
exp.mtch.col.3 <- c( 0.92, 1.67, 0.06, 1.71, 1.92, 1, 2.14)
checkEquals( exp.mtch.col.3, mtch.col.3 )
mtch.col.4 <- findMatchingRow(
matrix( c(0.33, 0.66, 1.0, 0.5, 1.0, 1.5), ncol=2 ), 0.9, 1
)
checkEquals( c(0.66, 1.0), mtch.col.4 )
# Test eliminateUnreachableStates
print("Testing eliminateUnreachableStates(...)")
cpt.states <- c( 'GO_1', 'GO_2', 'unknown' )
cpt <- matrix( c( 0.5, 0.5, 0, 1, 0, 0, 0.5, 0.5, 0 ),
nrow=3, ncol=3, byrow=T,
dimnames=list( cpt.states, cpt.states )
)
res.eliminateUnreachableStates <- eliminateUnreachableStates( cpt )
exp.eliminateUnreachableStates <- matrix( c( 0.5, 0.5, 1, 0, 0.5, 0.5 ),
nrow=3, ncol=2, byrow=T,
dimnames=list( cpt.states, c( 'GO_1', 'GO_2' ) )
)
checkEquals( res.eliminateUnreachableStates, exp.eliminateUnreachableStates )
# No state is unreachable -> check equality:
cpt <- matrix( c( 0.5, 0.3, 0.2, 1, 0, 0, 0.5, 0.5, 0 ),
nrow=3, ncol=3, byrow=T,
dimnames=list( cpt.states, cpt.states )
)
res.eliminateUnreachableStates <- eliminateUnreachableStates( cpt )
checkEquals( res.eliminateUnreachableStates, cpt )
# Test conditionalProbabilityTable
print("Testing conditionalProbabilityTable(...)")
ua <- list( c( "GO_1", "GO_2", "GO_3" ), c( "GO_1", "GO_2" ), "GO_3" )
p.mut.tbl.lst <- list()
p.mut.tbl.lst[[ "GO_1" ]] <- matrix( c(0.33, 0.66, 1.0, 0.5, 1.0, 1.5), ncol=2 )
p.mut.tbl.lst[[ "GO_2" ]] <- matrix( c(0.25, 0.5, 0.75, 0.5, 1.0, 1.5), ncol=2 )
p.mut.tbl.lst[[ "GO_3" ]] <- matrix( c(0.45, 0.75, 0.98, 0.5, 1.0, 1.5), ncol=2 )
# print( p.mut.tbl.lst )
cpt.anno <- c( ua, 'unknown' )
cpt.anno.str <- as.character( lapply( cpt.anno, annotationToString ) )
con.prbs.tbl <- conditionalProbabilityTable( 0.9, cpt.anno, cpt.anno.str,
p.mut.tbl.lst, 1 )
# print( con.prbs.tbl )
checkEquals( 1.0, sum( con.prbs.tbl[ , 1 ] ) )
# print( 1 - p.mut.tbl.lst[[ 1 ]][[ 2, 1 ]] )
checkEquals( 1 - p.mut.tbl.lst[[ 3 ]][[ 2, 1 ]], con.prbs.tbl[[ 1, 1 ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 1 ] ) )
checkEquals( 1 - p.mut.tbl.lst[[ 1 ]][[ 2, 1 ]], con.prbs.tbl[[ 2, 2 ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 3 ] ) )
checkEquals( 1 - p.mut.tbl.lst[[ 3 ]][[ 2, 1 ]], con.prbs.tbl[[ 3, 3 ]] )
checkEquals( 0, con.prbs.tbl[[ 'unknown', 'unknown' ]] )
checkEquals( 1.0, sum( con.prbs.tbl[ , 'unknown' ] ) )
# Test conditionalProbabilityTables
print("Testing conditionalProbabilityTables(...)")
phylo.tree.4.brnch.lngths <- read.tree( project.file.path(
'test_tree_4_branch_lengths.newick' ) )
annos <- c( ua, 'unknown' )
annos.str <- as.character( lapply( annos, annotationToString ) )
res.conditionalProbabilityTables <- conditionalProbabilityTables(
unique( phylo.tree.4.brnch.lngths$edge.length ),
annos, annos.str, p.mut.tbl.lst, mutTblLengthColIndx=1 )
# print( res.conditionalProbabilityTables )
checkEquals( names( res.conditionalProbabilityTables ),
as.character( unique( phylo.tree.4.brnch.lngths$edge.length ) ) )
# print( res.conditionalProbabilityTables[[ '0.59211' ]][ , 1 ])
checkEquals( setNames( c( 0.25, 0.25, 0.25, 0.25 ),
c( 'GO_1 & GO_2 & GO_3', 'GO_1 & GO_2', 'GO_3', 'unknown' ) ),
res.conditionalProbabilityTables[[ '0.59211' ]][ , 1 ]
)
# Test mutationProbability
print("Testing mutationProbability(...)")
anno <- c( "A", "B", "C" )
branch.length <- 0.5
mut.prob.tbls <- list( "A"=matrix( c(0.2, 0.5), nrow=1 ),
"B"=matrix( c(0.3, 0.5), nrow=1 ), "C"=matrix( c(0.4, 0.5), nrow=1 )
)
res.mutationProbability <- mutationProbability( anno, branch.length, mut.prob.tbls, 1 )
exp.mutationProbability <- 0.4
checkEquals( res.mutationProbability, exp.mutationProbability )
# Test get.node.label
print("Testing get.node.label(...)")
checkEquals(get.node.label(phylo.tree, 21), "21")
checkTrue(identical(class(get.node.label(phylo.tree, 11)), "character"))
# Test edge.to.formula
print("Testing edge.to.formula(...)")
# Test a tip's formula
indx <- which(phylo.tree$edge[, 2] == 1)
frml <- edge.to.formula(phylo.tree, indx)
checkTrue(identical(class(frml), 'formula'))
checkTrue(length(as.character(frml)) == 2)
checkTrue(grepl('[a-zA-Z]+', as.character(frml)[[2]], perl=T))
indx <- which(phylo.tree$edge[, 1] == (length(phylo.tree$tip.label)+1))[1]
frml <- edge.to.formula(phylo.tree, indx)
checkTrue(! grepl('[a-zA-Z]+', as.character(frml)[[2]], perl=T))
# Test bayesNodes
# Tree in which the state 'unknown' is unreachable
print("Testing bayesNodes(...)")
res.bayesNodes <- bayesNodes(
phylo.tree.unreachbl.stts, c( 'GO:0043047', 'unknown' )
)
exp.anno.space <- c( 'GO:0043047', 'unknown' )
# print( res.bayesNodes )
checkEquals( length( res.bayesNodes ), 38 )
root.cpt <- res.bayesNodes[[ 1 ]]
checkEquals( root.cpt[[ 'values' ]],
matrix( c( 1, 1 ), ncol=1, dimnames=list( exp.anno.space, c() ) )
)
# print( exp.anno.space )
# print( root.cpt[[ 'levels' ]] )
checkEquals( exp.anno.space, root.cpt[[ 'levels' ]] )
for( i in 2:length(res.bayesNodes) ) {
desc.cpt <- res.bayesNodes[[ i ]]
print(
checkEquals( desc.cpt[[ 'values' ]],
matrix( c( 0.96, 0.04, 1, 0 ), nrow=2, ncol=2,
dimnames=list( exp.anno.space, exp.anno.space)
)
)
)
print( checkEquals( exp.anno.space, desc.cpt[[ 'levels' ]] ) )
}
# Test tree without unreachable 'unknown' state:
annot.df <- read.table( stringsAsFactors=FALSE, text=
"GO:0004530 IEA A0K2M8 molecular_function
GO:0004525 IC A0K2M8 molecular_function
GO:0004527 IC Q5ZL72 molecular_function
GO:0042263 IC Q5ZL72 biological_process
GO:0042267 IDA Q5ZL72 biological_process
GO:0042269 IDA A0K2M8 biological_process")
go.type.annos <- goTypeAnnotationMatrices( annot.df )
anno.space.lst <- goAnnotationSpaceList( go.type.annos )
bys.nds <- bayesNodes( phylo.tree, anno.space.lst$molecular_function )
checkTrue( length( bys.nds ) == 21 )
root.bys.nd <- bys.nds[[ 1 ]]
# print( root.bys.nd )
# print( uniq.annos )
# print( root.bys.nd$levels )
# print( root.bys.nd$values )
# print( anno.space.lst$molecular_function )
checkTrue( length( root.bys.nd$values ) == length( anno.space.lst$molecular_function ) )
checkEquals( root.bys.nd$levels, as.character( lapply( anno.space.lst$molecular_function, annotationToString ) ) )
# print(bys.nds[[2]]$values)
# Test create bayesian Network
print( "Testing create bayesian Network" )
plist <- compileCPT( bys.nds )
grain.res <- try( grain( plist ), silent=T )
checkTrue( ! identical( 'try-error', class( grain.res ) ) )
# Check conditional probability table for leaf '"A0KEC3"':
print( "Testing conditional probability tables of compiled Bayesian network" )
res.cpt <- as.matrix( plist$'"A0KEC3"' )
exp.states <- c( "GO:0004525 & GO:0004530", "GO:0004527", "unknown" )
checkTrue( ! is.null( res.cpt ) )
checkEquals( class( res.cpt ), c( "parray", "array" ) )
checkEquals( rownames( res.cpt ), exp.states )
checkEquals( colnames( res.cpt ), exp.states )
checkEquals( as.numeric( res.cpt[ , 1 ] ), c( 0.670, 0.165, 0.165 ) )
checkEquals( as.numeric( res.cpt[ , 2 ] ), c( 0.135, 0.730, 0.135 ) )
checkEquals( as.numeric( res.cpt[ , 3 ] ), c( 0.5, 0.5, 0 ) )
# Test getTipsWithNaAnnotation
print("Testing getTipsWithNaAnnotation(...)")
checkEquals( getTipsWithNaAnnotation( phylo.tree, annotation.matrix ),
c("\"Protein_1\"") )
# Test multiple NA annotations
am <- annotation.matrix
am[1, 6] <- NA
checkEquals(getTipsWithNaAnnotation(phylo.tree, am),
c("\"Protein_1\"", "\"A0AEI7\""))
checkEquals(
getTipsWithNaAnnotation(phylo.tree, am, negate=T),
c( "\"A0RLX8\"", "\"A0LE53\"", "\"A0PKB2\"", "\"A0Q3U6\"", "\"A0K2M8\"",
"\"A0KR35\"", "\"A0KEC3\"", "\"A0Q3U7\"", "\"A0L3I7\"")
)
# Clean Up:
dbDisconnect( go.con )
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