Nothing
inst/unitTests/test_HumanDHSFilter.R
In trena: Fit transcriptional regulatory networks using gene expression, priors, machine learning
library(trena)
library(MotifDb)
library(RUnit)
library(RPostgreSQL)
#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
mef2c.tss <- 88904257
mef2c.promoter.region <- list(chrom="chr5", start=mef2c.tss-100, end=mef2c.tss+100)
mef2c.promoter.string <- with(mef2c.promoter.region, sprintf("%s:%d-%d", chrom, start, end))
#----------------------------------------------------------------------------------------------------
runTests <- function()
{
test_basicConstructor()
test_getOpenChromatinFastAndSimple()
test_getEncodeRegulatoryTableNames()
test_checkSampleOfEncodeTables(quiet=FALSE)
test_getRegulatoryRegions()
test_getCandidates.emptyRegion()
test_getCandidates.vrk2.twoRegions()
test_getCandidates.vrk2.rs13384219.variant()
} # runTests
#----------------------------------------------------------------------------------------------------
# reuse this in several tests
create.vrk2.candidateFilterSpec <- function(promoter.length=1000)
{
target.gene <- "VRK2"
genome <- "hg38"
chromosome <- "chr2"
tss <- 57907651
promoter.length <- promoter.length
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
tbl.regions <- data.frame(chrom="chr2", start=57906700, end=57906870, stringsAsFactors=FALSE)
candidateFilterSpec <- list(filterType="EncodeDNaseClusters",
genomeName=genome,
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=85L,
geneInfoDB=genome.db.uri,
regions=tbl.regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
variants=NA_character_)
candidateFilterSpec
} # create.vrk2.candidateFilterSpec
#----------------------------------------------------------------------------------------------------
# reuse this in several tests
create.vrk2.candidateFilterSpec.twoRegions <- function()
{
target.gene <- "VRK2"
genome <- "hg38"
chromosome <- "chr2"
tss <- 57907651
promoter.length <- 1000
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
tbl.regions <- data.frame(chrom=c("chr2", "chr2"),
start=c(57906700, 57907740),
end=c(57906870, 57908150),
stringsAsFactors=FALSE)
cfSpec <- list(filterType="EncodeDNaseClusters",
genomeName=genome,
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=85L,
geneInfoDB= genome.db.uri,
regions=tbl.regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
variants=NA_character_)
cfSpec
} # create.vrk2.candidateFilterSpec.twoRegions
#----------------------------------------------------------------------------------------------------
# rs13384219 A->G
# rs13384219 at chr2:57907073-57907573
# gtcagtagtggtggaaccagcatgc[A/G]aattagacaatgtgacttcatagcc
# Chromosome: 2:57907323
# vrk2 tss at chr2:57908651
create.vrk2.rs13384219.variant.candidateFilterSpec <- function(shoulder=10)
{
spec <- create.vrk2.candidateFilterSpec()
rs13384219.loc <- 57907323
left.loc <- rs13384219.loc - shoulder
right.loc <- rs13384219.loc + shoulder
spec$regions <- data.frame(chrom="chr2", start=left.loc, end=right.loc, stringsAsFactors=FALSE)
spec$variants <- "rs13384219"
spec
} # create.vrk2.rs13384219.variant.candidateFilterSpec
#----------------------------------------------------------------------------------------------------
test_basicConstructor <- function(reuse=FALSE)
{
if(!interactive()) return(TRUE)
if(!reuse) printf("--- test_basicConstructor")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=candidateFilterSpec$regions))
checkTrue(all(c("HumanDHSFilter", "CandidateFilter") %in% is(hdf)))
# make sure an unsupported genome triggers an error
candidateFilterSpec$genomeName <- "intentional error in genome name"
if(!reuse)
checkException(hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=regions)),
silent = TRUE)
if(reuse)
return(hdf)
} # test_basicConstructor
#----------------------------------------------------------------------------------------------------
# takes > 5 seconds, bioc check
test_getEncodeRegulatoryTableNames <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getEncodeRegulatoryTableNames")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=regions))
names <- getEncodeRegulatoryTableNames(hdf)
checkTrue(length(names) > 90) # 96 on (18 oct 2017)
} # test_getEncodeRegulatoryTableNames
#----------------------------------------------------------------------------------------------------
test_checkSampleOfEncodeTables <- function(quiet=TRUE)
{
if(!interactive()) return(TRUE)
printf("--- test_checkSampleOfEncodeTables")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
#fimoDatabase.uri=fimoDB,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tableNames <- getEncodeRegulatoryTableNames(hdf)
# rs13384219 at chr2:57907073-57907573
chrom <- "chr2"
rs13384219.loc <- 57907323
start <- rs13384219.loc - 10000
end <- rs13384219.loc + 10000
selectedTableNames <- tableNames[sample(1:length(tableNames), size=10)]
for(tableName in selectedTableNames){
#printf("tableName: %s", tableName)
tbl <-getRegulatoryRegions(hdf, tableName, chrom, start, end)
if(!quiet) printf("--- %s: %d rows", tableName, nrow(tbl))
checkTrue(nrow(tbl) >= 0)
checkEquals(colnames(tbl), c("chrom", "chromStart", "chromEnd", "count", "score"))
}
} # test_checkSampleOfEncodeTables
#----------------------------------------------------------------------------------------------------
# use this sample code to poke at the encode data offered by uscs
# note that most of the tables here only serve to list, not regions, but
# metadata: what the inputs where, where the bb (bigBed) file can be found.
# for instance, the Hotspot table has this single line:
# fileName
# 1 /gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwA549Hotspot.broadPeak.bb
explore.ucsc.database <- function()
{
library(RMySQL)
driver <- MySQL()
host <- "genome-mysql.cse.ucsc.edu"
user <- "genome"
dbname <- "hg38"
db <- dbConnect(driver, user = user, host = host, dbname = dbname)
tables <- c("wgEncodeRegDnaseClustered", "wgEncodeRegDnaseUwA549Hotspot", "wgEncodeRegDnaseUwA549Peak")
main.clause <- sprintf("select * from %s where", tables[1]);
chrom <- "chr5"
start <- 88819630
end <- 88835936
query <- paste(main.clause,
sprintf("chrom = '%s'", chrom),
sprintf("and chromStart >= %d", start),
sprintf("and chromEnd <= %d", end),
collapse = " ")
suppressWarnings(dbGetQuery(db, sprintf("select * from %s limit 5", tables[3])))
} # explore.ucsc.database
#----------------------------------------------------------------------------------------------------
test_getRegulatoryRegions <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getRegulatoryRegions");
hdf <- test_basicConstructor(reuse=TRUE)
tableNames <- getEncodeRegulatoryTableNames(hdf)
table <- "wgEncodeRegDnaseClustered"
checkTrue(table %in% tableNames)
# some DHS regions, good for testing small region overlap handling
# chrom chromStart chromEnd score sourceCount
# 1 chr1 88801880 88802150 573 64
# 2 chr1 88802480 88802930 287 13
# 3 chr1 88803000 88803270 541 60
# 4 chr1 88811140 88811290 100 1
# 5 chr1 88811400 88811550 68 1
# a small region, entirely within a DHS region
chrom = "chr1"
start <- 88802520
end <- 88802530
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, end)
# a small region, overhanging that DHS region at its upperbound, on the right
chrom = "chr1"
start <- 88802145
end <- 88802155
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, 88802150)
# a small region, overhanging that DHS region at its lowerbound, on the left
chrom = "chr1"
start <- 88801878
end <- 88801883
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, 88801880)
checkEquals(tbl.regions$chromEnd, end)
# another completely contained-in-DHS-region, small area of interest
chrom <- "chr1"
start <- 167830160
end <- 167830180
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, end)
# the regions around the AQP4 regulatory snps
loc <- c(26864410, 26865469, 26855623, 26855854, 26850565)
rsids <- c("rs3763040", "rs3875089", "rs335929", "rs3763043", "rs9951307")
tbl.snps <- data.frame(chrom=rep("chr18", 5), start=loc-1, end=loc+1, name=rsids, stringsAsFactors=FALSE)
tbl.regions <- getRegulatoryRegions(hdf, table, "chr18", min(loc), max(loc))
# with these hand-picked locations we know to expect an equal or larger number of
# open chromatin regions in the aggregating "wgEncodeRegDnaseClustered" table
checkTrue(nrow(tbl.regions) >= length(loc))
# all the snp locs should fall within these regions
checkTrue(all(loc >= tbl.regions$start))
checkTrue(all(loc <= tbl.regions$end))
} # test_getRegulatoryRegions
#----------------------------------------------------------------------------------------------------
test_getCandidates.emptyRegion <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.emptyRegion")
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
hdf <- HumanDHSFilter("hg38",
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=80L,
geneInfoDatabase.uri=genome.db.uri,
regions=data.frame(chrom="chr18", start=26850560, end=26850565, stringsAsFactors=FALSE),
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE)
tbl <- getCandidates(hdf)
checkEquals(nrow(tbl), 0)
} # test_getCandidates.emptyRegion
#----------------------------------------------------------------------------------------------------
test_getCandidates.vrk2.twoRegions <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.vrk2.twoRegions")
cfSpec <- create.vrk2.candidateFilterSpec.twoRegions()
hdf <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=97L,
geneInfoDatabase.uri=geneInfoDB,
region=regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tbl <- getCandidates(hdf)
checkEquals(colnames(tbl),
c("motifName", "chrom", "motifStart", "motifEnd", "strand", "motifScore", "motifRelativeScore", "match",
"regulatoryRegionStart", "regualtoryRegionEnd", "regulatorySequence", "variant", "shortMotif"))
# make sure all motifs fall within the specified restions
# cfSpec$regionsSpec: [1] "chr2:57906700-57906870" "chr2:57907740-57908150"
starts <- tbl$motifStart
ends <- tbl$motifEnd
starts.in.region.1 <- intersect(which(starts >= 57906700), which(starts <= 57906870))
starts.in.region.2 <- intersect(which(starts >= 57907740), which(starts <= 57908150))
# did we find them all?
checkEquals(nrow(tbl), 11)
checkTrue(all(sort(c(starts.in.region.1, starts.in.region.2)) == seq_len(nrow(tbl))))
checkTrue(all(ends[starts.in.region.1] <= 57906870))
checkTrue(all(ends[starts.in.region.2] <= 57908150))
expected.motifs <- c("Hsapiens-jaspar2016-GATA3-MA0037.2", "Hsapiens-jaspar2016-FOXL1-MA0033.2",
"Hsapiens-jaspar2016-GATA5-MA0766.1", "Hsapiens-jaspar2016-RHOXF1-MA0719.1",
"Hsapiens-jaspar2016-RHOXF1-MA0719.1", "Hsapiens-jaspar2016-SPI1-MA0080.1",
"Hsapiens-jaspar2016-SPI1-MA0080.1", "Hsapiens-jaspar2016-ETS1-MA0098.1",
"Hsapiens-jaspar2016-ETS1-MA0098.1", "Hsapiens-jaspar2016-GATA2-MA0036.1",
"Hsapiens-jaspar2016-GATA2-MA0036.1")
checkTrue(all(tbl$motifName %in% expected.motifs))
tbl.conservative <- associateTranscriptionFactors(MotifDb, tbl, source="MotifDb", expand.rows=TRUE)
motifDb.associated.genes <- c("GATA3","FOXL1","GATA5","RHOXF1","RHOXF1","SPI1","SPI1","ETS1","ETS1","GATA2", "GATA2")
checkEquals(sort(motifDb.associated.genes), sort(tbl.conservative$geneSymbol))
# we used MotifDb as our source of motifs, wherein motifName is, for example,
# Hsapiens-jaspar2016-GATA2-MA0036.1.
# if we use TFClass mapping from motifs to transcription factor gene names, we must create
# a new column "shortMotif" so that the motif has a form we can use as a TFclass key
tbl$shortMotif <- unlist(lapply(strsplit(tbl$motifName, split="-"), "[", 4))
tbl.liberal <- associateTranscriptionFactors(MotifDb, tbl, source="TFClass", expand.rows=TRUE)
tfClass.associated.genes <- unique(tbl.liberal$geneSymbol)
checkEquals(length(tfClass.associated.genes), 110)
# despite the high number of genes associated to motifs in TFClass, not all MotifDb associations
# are found
checkEquals(setdiff(motifDb.associated.genes, tfClass.associated.genes), c("SPI1", "ETS1"))
} # test_getCandidates.vrk2.twoRegions
#----------------------------------------------------------------------------------------------------
test_getCandidates.vrk2.rs13384219.variant <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.vrk2.rs13384219.variant")
cfSpec <- create.vrk2.rs13384219.variant.candidateFilterSpec()
hdf.wt <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
variants=NA_character_,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
hdf.var <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
variants=variants,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tbl.wt <- getCandidates(hdf.wt)
tbl.var <- getCandidates(hdf.var)
checkEquals(dim(tbl.wt), c(53, 13))
checkEquals(dim(tbl.var), c(45, 13))
# many motifs survive, 9 are lost, 2 are gained
# see trena::assessSnp for close comparison of losses and gains, wherein differing scores are reported
checkEquals(length(intersect(tbl.wt$motifName, tbl.var$motifName)), 36)
checkEquals(length(setdiff(tbl.wt$motifName, tbl.var$motifName)), 9)
checkEquals(length(setdiff(tbl.var$motifName, tbl.wt$motifName)), 2)
} # test_getCandidates.vrk2.rs13384219.variant
#------------------------------------------------------------------------------------------------------------------------
# demonstrate an initially unanticipated use of this class: just get the open chromatin regions from ucsc. a hack...
test_getOpenChromatinFastAndSimple <- function()
{
printf("--- test_getOpenChromatinFastAndSimple")
hdf <- HumanDHSFilter("hg38", "wgEncodeRegDnaseClustered", pwmMatchPercentageThreshold=0,
geneInfoDatabase.uri="bogus", regions=data.frame(), pfms=list())
chrom <- "chr14"
start <- 91654248
end <- 93010778
tbl.dhs <- getRegulatoryRegions(hdf, "wgEncodeRegDnaseClustered", chrom, start, end)
checkEquals(colnames(tbl.dhs), c("chrom", "chromStart", "chromEnd", "count", "score"))
checkTrue(nrow(tbl.dhs) > 1200)
checkTrue(all(tbl.dhs$chrom == chrom))
checkTrue(all(tbl.dhs$start >= start))
checkTrue(all(tbl.dhs$end <= end))
} # test_getOpenChromatinFastAndSimple
#------------------------------------------------------------------------------------------------------------------------
if(!interactive())
runTests()
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library(trena)
library(MotifDb)
library(RUnit)
library(RPostgreSQL)
#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
mef2c.tss <- 88904257
mef2c.promoter.region <- list(chrom="chr5", start=mef2c.tss-100, end=mef2c.tss+100)
mef2c.promoter.string <- with(mef2c.promoter.region, sprintf("%s:%d-%d", chrom, start, end))
#----------------------------------------------------------------------------------------------------
runTests <- function()
{
test_basicConstructor()
test_getOpenChromatinFastAndSimple()
test_getEncodeRegulatoryTableNames()
test_checkSampleOfEncodeTables(quiet=FALSE)
test_getRegulatoryRegions()
test_getCandidates.emptyRegion()
test_getCandidates.vrk2.twoRegions()
test_getCandidates.vrk2.rs13384219.variant()
} # runTests
#----------------------------------------------------------------------------------------------------
# reuse this in several tests
create.vrk2.candidateFilterSpec <- function(promoter.length=1000)
{
target.gene <- "VRK2"
genome <- "hg38"
chromosome <- "chr2"
tss <- 57907651
promoter.length <- promoter.length
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
tbl.regions <- data.frame(chrom="chr2", start=57906700, end=57906870, stringsAsFactors=FALSE)
candidateFilterSpec <- list(filterType="EncodeDNaseClusters",
genomeName=genome,
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=85L,
geneInfoDB=genome.db.uri,
regions=tbl.regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
variants=NA_character_)
candidateFilterSpec
} # create.vrk2.candidateFilterSpec
#----------------------------------------------------------------------------------------------------
# reuse this in several tests
create.vrk2.candidateFilterSpec.twoRegions <- function()
{
target.gene <- "VRK2"
genome <- "hg38"
chromosome <- "chr2"
tss <- 57907651
promoter.length <- 1000
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
tbl.regions <- data.frame(chrom=c("chr2", "chr2"),
start=c(57906700, 57907740),
end=c(57906870, 57908150),
stringsAsFactors=FALSE)
cfSpec <- list(filterType="EncodeDNaseClusters",
genomeName=genome,
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=85L,
geneInfoDB= genome.db.uri,
regions=tbl.regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
variants=NA_character_)
cfSpec
} # create.vrk2.candidateFilterSpec.twoRegions
#----------------------------------------------------------------------------------------------------
# rs13384219 A->G
# rs13384219 at chr2:57907073-57907573
# gtcagtagtggtggaaccagcatgc[A/G]aattagacaatgtgacttcatagcc
# Chromosome: 2:57907323
# vrk2 tss at chr2:57908651
create.vrk2.rs13384219.variant.candidateFilterSpec <- function(shoulder=10)
{
spec <- create.vrk2.candidateFilterSpec()
rs13384219.loc <- 57907323
left.loc <- rs13384219.loc - shoulder
right.loc <- rs13384219.loc + shoulder
spec$regions <- data.frame(chrom="chr2", start=left.loc, end=right.loc, stringsAsFactors=FALSE)
spec$variants <- "rs13384219"
spec
} # create.vrk2.rs13384219.variant.candidateFilterSpec
#----------------------------------------------------------------------------------------------------
test_basicConstructor <- function(reuse=FALSE)
{
if(!interactive()) return(TRUE)
if(!reuse) printf("--- test_basicConstructor")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=candidateFilterSpec$regions))
checkTrue(all(c("HumanDHSFilter", "CandidateFilter") %in% is(hdf)))
# make sure an unsupported genome triggers an error
candidateFilterSpec$genomeName <- "intentional error in genome name"
if(!reuse)
checkException(hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=regions)),
silent = TRUE)
if(reuse)
return(hdf)
} # test_basicConstructor
#----------------------------------------------------------------------------------------------------
# takes > 5 seconds, bioc check
test_getEncodeRegulatoryTableNames <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getEncodeRegulatoryTableNames")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
regions=regions))
names <- getEncodeRegulatoryTableNames(hdf)
checkTrue(length(names) > 90) # 96 on (18 oct 2017)
} # test_getEncodeRegulatoryTableNames
#----------------------------------------------------------------------------------------------------
test_checkSampleOfEncodeTables <- function(quiet=TRUE)
{
if(!interactive()) return(TRUE)
printf("--- test_checkSampleOfEncodeTables")
candidateFilterSpec <- create.vrk2.candidateFilterSpec()
hdf <- with(candidateFilterSpec,
HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
#fimoDatabase.uri=fimoDB,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tableNames <- getEncodeRegulatoryTableNames(hdf)
# rs13384219 at chr2:57907073-57907573
chrom <- "chr2"
rs13384219.loc <- 57907323
start <- rs13384219.loc - 10000
end <- rs13384219.loc + 10000
selectedTableNames <- tableNames[sample(1:length(tableNames), size=10)]
for(tableName in selectedTableNames){
#printf("tableName: %s", tableName)
tbl <-getRegulatoryRegions(hdf, tableName, chrom, start, end)
if(!quiet) printf("--- %s: %d rows", tableName, nrow(tbl))
checkTrue(nrow(tbl) >= 0)
checkEquals(colnames(tbl), c("chrom", "chromStart", "chromEnd", "count", "score"))
}
} # test_checkSampleOfEncodeTables
#----------------------------------------------------------------------------------------------------
# use this sample code to poke at the encode data offered by uscs
# note that most of the tables here only serve to list, not regions, but
# metadata: what the inputs where, where the bb (bigBed) file can be found.
# for instance, the Hotspot table has this single line:
# fileName
# 1 /gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwA549Hotspot.broadPeak.bb
explore.ucsc.database <- function()
{
library(RMySQL)
driver <- MySQL()
host <- "genome-mysql.cse.ucsc.edu"
user <- "genome"
dbname <- "hg38"
db <- dbConnect(driver, user = user, host = host, dbname = dbname)
tables <- c("wgEncodeRegDnaseClustered", "wgEncodeRegDnaseUwA549Hotspot", "wgEncodeRegDnaseUwA549Peak")
main.clause <- sprintf("select * from %s where", tables[1]);
chrom <- "chr5"
start <- 88819630
end <- 88835936
query <- paste(main.clause,
sprintf("chrom = '%s'", chrom),
sprintf("and chromStart >= %d", start),
sprintf("and chromEnd <= %d", end),
collapse = " ")
suppressWarnings(dbGetQuery(db, sprintf("select * from %s limit 5", tables[3])))
} # explore.ucsc.database
#----------------------------------------------------------------------------------------------------
test_getRegulatoryRegions <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getRegulatoryRegions");
hdf <- test_basicConstructor(reuse=TRUE)
tableNames <- getEncodeRegulatoryTableNames(hdf)
table <- "wgEncodeRegDnaseClustered"
checkTrue(table %in% tableNames)
# some DHS regions, good for testing small region overlap handling
# chrom chromStart chromEnd score sourceCount
# 1 chr1 88801880 88802150 573 64
# 2 chr1 88802480 88802930 287 13
# 3 chr1 88803000 88803270 541 60
# 4 chr1 88811140 88811290 100 1
# 5 chr1 88811400 88811550 68 1
# a small region, entirely within a DHS region
chrom = "chr1"
start <- 88802520
end <- 88802530
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, end)
# a small region, overhanging that DHS region at its upperbound, on the right
chrom = "chr1"
start <- 88802145
end <- 88802155
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, 88802150)
# a small region, overhanging that DHS region at its lowerbound, on the left
chrom = "chr1"
start <- 88801878
end <- 88801883
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, 88801880)
checkEquals(tbl.regions$chromEnd, end)
# another completely contained-in-DHS-region, small area of interest
chrom <- "chr1"
start <- 167830160
end <- 167830180
tbl.regions <- getRegulatoryRegions(hdf, table, chrom, start, end)
checkEquals(nrow(tbl.regions), 1)
checkEquals(tbl.regions$chromStart, start)
checkEquals(tbl.regions$chromEnd, end)
# the regions around the AQP4 regulatory snps
loc <- c(26864410, 26865469, 26855623, 26855854, 26850565)
rsids <- c("rs3763040", "rs3875089", "rs335929", "rs3763043", "rs9951307")
tbl.snps <- data.frame(chrom=rep("chr18", 5), start=loc-1, end=loc+1, name=rsids, stringsAsFactors=FALSE)
tbl.regions <- getRegulatoryRegions(hdf, table, "chr18", min(loc), max(loc))
# with these hand-picked locations we know to expect an equal or larger number of
# open chromatin regions in the aggregating "wgEncodeRegDnaseClustered" table
checkTrue(nrow(tbl.regions) >= length(loc))
# all the snp locs should fall within these regions
checkTrue(all(loc >= tbl.regions$start))
checkTrue(all(loc <= tbl.regions$end))
} # test_getRegulatoryRegions
#----------------------------------------------------------------------------------------------------
test_getCandidates.emptyRegion <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.emptyRegion")
db.address <- system.file(package="trena", "extdata")
genome.db.uri <- paste("sqlite:/", db.address, "vrk2.neighborhood.hg38.gtfAnnotation.db", sep = "/")
hdf <- HumanDHSFilter("hg38",
encodeTableName="wgEncodeRegDnaseClustered",
pwmMatchPercentageThreshold=80L,
geneInfoDatabase.uri=genome.db.uri,
regions=data.frame(chrom="chr18", start=26850560, end=26850565, stringsAsFactors=FALSE),
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE)
tbl <- getCandidates(hdf)
checkEquals(nrow(tbl), 0)
} # test_getCandidates.emptyRegion
#----------------------------------------------------------------------------------------------------
test_getCandidates.vrk2.twoRegions <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.vrk2.twoRegions")
cfSpec <- create.vrk2.candidateFilterSpec.twoRegions()
hdf <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=97L,
geneInfoDatabase.uri=geneInfoDB,
region=regions,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tbl <- getCandidates(hdf)
checkEquals(colnames(tbl),
c("motifName", "chrom", "motifStart", "motifEnd", "strand", "motifScore", "motifRelativeScore", "match",
"regulatoryRegionStart", "regualtoryRegionEnd", "regulatorySequence", "variant", "shortMotif"))
# make sure all motifs fall within the specified restions
# cfSpec$regionsSpec: [1] "chr2:57906700-57906870" "chr2:57907740-57908150"
starts <- tbl$motifStart
ends <- tbl$motifEnd
starts.in.region.1 <- intersect(which(starts >= 57906700), which(starts <= 57906870))
starts.in.region.2 <- intersect(which(starts >= 57907740), which(starts <= 57908150))
# did we find them all?
checkEquals(nrow(tbl), 11)
checkTrue(all(sort(c(starts.in.region.1, starts.in.region.2)) == seq_len(nrow(tbl))))
checkTrue(all(ends[starts.in.region.1] <= 57906870))
checkTrue(all(ends[starts.in.region.2] <= 57908150))
expected.motifs <- c("Hsapiens-jaspar2016-GATA3-MA0037.2", "Hsapiens-jaspar2016-FOXL1-MA0033.2",
"Hsapiens-jaspar2016-GATA5-MA0766.1", "Hsapiens-jaspar2016-RHOXF1-MA0719.1",
"Hsapiens-jaspar2016-RHOXF1-MA0719.1", "Hsapiens-jaspar2016-SPI1-MA0080.1",
"Hsapiens-jaspar2016-SPI1-MA0080.1", "Hsapiens-jaspar2016-ETS1-MA0098.1",
"Hsapiens-jaspar2016-ETS1-MA0098.1", "Hsapiens-jaspar2016-GATA2-MA0036.1",
"Hsapiens-jaspar2016-GATA2-MA0036.1")
checkTrue(all(tbl$motifName %in% expected.motifs))
tbl.conservative <- associateTranscriptionFactors(MotifDb, tbl, source="MotifDb", expand.rows=TRUE)
motifDb.associated.genes <- c("GATA3","FOXL1","GATA5","RHOXF1","RHOXF1","SPI1","SPI1","ETS1","ETS1","GATA2", "GATA2")
checkEquals(sort(motifDb.associated.genes), sort(tbl.conservative$geneSymbol))
# we used MotifDb as our source of motifs, wherein motifName is, for example,
# Hsapiens-jaspar2016-GATA2-MA0036.1.
# if we use TFClass mapping from motifs to transcription factor gene names, we must create
# a new column "shortMotif" so that the motif has a form we can use as a TFclass key
tbl$shortMotif <- unlist(lapply(strsplit(tbl$motifName, split="-"), "[", 4))
tbl.liberal <- associateTranscriptionFactors(MotifDb, tbl, source="TFClass", expand.rows=TRUE)
tfClass.associated.genes <- unique(tbl.liberal$geneSymbol)
checkEquals(length(tfClass.associated.genes), 110)
# despite the high number of genes associated to motifs in TFClass, not all MotifDb associations
# are found
checkEquals(setdiff(motifDb.associated.genes, tfClass.associated.genes), c("SPI1", "ETS1"))
} # test_getCandidates.vrk2.twoRegions
#----------------------------------------------------------------------------------------------------
test_getCandidates.vrk2.rs13384219.variant <- function()
{
if(!interactive()) return(TRUE)
printf("--- test_getCandidates.vrk2.rs13384219.variant")
cfSpec <- create.vrk2.rs13384219.variant.candidateFilterSpec()
hdf.wt <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
variants=NA_character_,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
hdf.var <- with(cfSpec, HumanDHSFilter(genomeName,
encodeTableName=encodeTableName,
pwmMatchPercentageThreshold=85L,
geneInfoDatabase.uri=geneInfoDB,
regions=regions,
variants=variants,
pfms = as.list(query(query(MotifDb, "sapiens"),"jaspar2016")),
quiet=TRUE))
tbl.wt <- getCandidates(hdf.wt)
tbl.var <- getCandidates(hdf.var)
checkEquals(dim(tbl.wt), c(53, 13))
checkEquals(dim(tbl.var), c(45, 13))
# many motifs survive, 9 are lost, 2 are gained
# see trena::assessSnp for close comparison of losses and gains, wherein differing scores are reported
checkEquals(length(intersect(tbl.wt$motifName, tbl.var$motifName)), 36)
checkEquals(length(setdiff(tbl.wt$motifName, tbl.var$motifName)), 9)
checkEquals(length(setdiff(tbl.var$motifName, tbl.wt$motifName)), 2)
} # test_getCandidates.vrk2.rs13384219.variant
#------------------------------------------------------------------------------------------------------------------------
# demonstrate an initially unanticipated use of this class: just get the open chromatin regions from ucsc. a hack...
test_getOpenChromatinFastAndSimple <- function()
{
printf("--- test_getOpenChromatinFastAndSimple")
hdf <- HumanDHSFilter("hg38", "wgEncodeRegDnaseClustered", pwmMatchPercentageThreshold=0,
geneInfoDatabase.uri="bogus", regions=data.frame(), pfms=list())
chrom <- "chr14"
start <- 91654248
end <- 93010778
tbl.dhs <- getRegulatoryRegions(hdf, "wgEncodeRegDnaseClustered", chrom, start, end)
checkEquals(colnames(tbl.dhs), c("chrom", "chromStart", "chromEnd", "count", "score"))
checkTrue(nrow(tbl.dhs) > 1200)
checkTrue(all(tbl.dhs$chrom == chrom))
checkTrue(all(tbl.dhs$start >= start))
checkTrue(all(tbl.dhs$end <= end))
} # test_getOpenChromatinFastAndSimple
#------------------------------------------------------------------------------------------------------------------------
if(!interactive())
runTests()
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