inst/unitTests/test_matchValue.R
In jotsetung/mirhostgenes: mirhostgenes: putting miRNAs into genomic context
## testing the value matching...
test_doPairData <- function(){
## Simple test:
A <- 1:10
names(A) <- c("b", "a", "c", "b", "a", "a", "e", "f", "a", "c")
B <- c(2, 5, 3, 2, 5, 8, 4, 20)
names(B) <- c("a", "b", "a", "c", "c", "c", "n", "f")
Res <- doPairData(A, B, chooseFunX=chooseAll, chooseFunY=chooseAll)
expectx <- c(1, 4, 2, 2, 5, 5, 6, 6, 9, 9, 3, 3, 3, 10, 10, 10, 8, 7, NA)
expecty <- c(5, 5, 2, 3, 2, 3, 2, 3, 2, 3, 2, 5, 8, 2, 5, 8, 20, NA, 4)
checkEquals(Res$x, expectx)
checkEquals(Res$y, expecty)
## now let's use chooseAll for x and chooseOrderedValue for y
expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
expecty <- c(5, 5, 3, 3, 3, 3, 8, 8, 20, NA, 4)
Res <- doPairData(A, B, chooseFunY=chooseOrderedValue)
checkEquals(Res$x, expectx)
checkEquals(Res$y, expecty)
## same but for chooseOrderedValue for x:
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue)
expectx <- c(4, 9, 9, 10, 10, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## now with chooseOrderedValue for both.
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseOrderedValue)
expectx <- c(4, 9, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## ## OK, now with closest value for y, all for x
## ## what does this: selects all values from A and selects the one value from B for
## ## each matching name that is closest to any of the values with the same name in A
## ## Not very meaningfull though...
## Res <- doPairData(A, B, chooseFunY=chooseClosestBetween)
## expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 5, 2, 2, 2, 2, 2, 2, 20, NA, 4)
## checkEquals(Res$y, expecty)
## ## reverse thing:
## ## first select the one value from A closest to any of the values in B with the same
## ## name and then match that to all values in B; also not that meaningful
## Res <- doPairData(A, B, chooseFunX=chooseClosestBetween)
## expectx <- c(4, 2, 2, 3, 3, 3, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## OK, more meaningful:
## all in x and for each in x the closest from y with the same name.
## fails with second xselect
Res <- doPairData(A, B, chooseFunY=chooseClosestForEach)
expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 5, 2, 3, 3, 3, 2, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## reverse
## all in y and choose for each the closest in x
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach)
expectx <- c(4, 2, 2, 3, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## ## now something meaningful:
## ## select first the largest value in x and then select the closest in y
## ## NOT meaningful, since it depends on the order the functions are applied!
## Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseClosestBetween)
## expectx <- c(4, 9, 10, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 3, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## ## not meaningful... other way round.
## ## select first the value best matching in y and then the largest in y
## ## NOT meaningful, since it depends on the order the functions are applied!
## Res <- doPairData(A, B, chooseFunX=chooseClosestBetween, chooseFunY=chooseOrderedValue)
## expectx <- c(4, 2, 3, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 3, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## use the chooseClosestForEach insted... should give the same result than chooseClosestBetween
## i.e. first select the largest value in x and then select the closest from y matching
## THAT selected value in x.
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseClosestForEach)
expectx <- c(4, 9, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## reverse order; should also make sense!
## select for each in y the closest in x, then select the largest in y.
## Note: this is different than using chooseOrderedValue for x and chooseClosestForEach for y
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach, chooseFunY=chooseOrderedValue)
expectx <- c(4, 2, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## now, chooseClosestForEach for both: selects the closest pairs of data.
## this selects first the closest in x to the values in y, then, for all in y the
## closest in x (already subsetted to those that are close to values in y).
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach, chooseFunY=chooseClosestForEach)
## b: A x(1, 4), B y(5):
## chooseX: (4)
## chooseY: (5)
## a: A x(2, 5, 6, 9), B y(2, 3)
## chooseX: (2, 2)
## chooseY: (2, 2)
## c: A x(3, 10), B y(2, 5, 8)
## chooseX: (3, 3, 10)
## chooseY: (2, 2, 8)
## f: A x(8), B y(20)
## chooseX: (8)
## chooseY: (20)
## e: A x(7), B y(NA)
## n: A x(NA), B y(4)
## At last unique is called on the resulting data.frame that reduced the values for
## a and c.
expectx <- c(4, 2, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 2, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## second example:
C <- c(3, 8, 7, 1)
names(C) <- rep("a", length(C))
D <- c(9, 2, 4, 20, 3)
names(D) <- rep("a", length(D))
Res <- doPairData(C, D, chooseFunX=chooseClosestForEach,
chooseFunY=chooseClosestForEach)
## same length for some
A <- 1:4
names(A) <- c("a", "a", "b", "b")
Res <- doPairData(A, A)
checkEquals(Res$x, rep(1:4, each=2))
checkEquals(Res$y, c(1, 2, 1, 2, 3, 4, 3, 4))
}
notrun_test_doMatchMir <- function(){
library(MirhostDb.Hsapiens.v81.v21)
object <- MirhostDb.Hsapiens.v81.v21
## that's now for miRNA matching...
## Test data.
## have mat miRNAs from pre-miRNAs miR-15b, 16-1 and 223
miRNA.exprs <- c(8, 8.3, 5.6, 9.5, 4.6, 13.1)
names(miRNA.exprs) <- c("hsa-miR-15b-3p", "hsa-miR-15b-5p", "hsa-miR-16-5p", "hsa-miR-16-1-3p", "hsa-miR-223-3p", "hsa-miR-223-5p")
## expression data
tx.exprs <- data.frame(tx_id=c("NR_002612.1", "ENST00000344722", "ENST00000344722",
"ENST00000468653", "ENSESTT00000026526"),
probeset_id=c("1564443_at", "201663_s_at", "201664_at",
"215623_x_at", "229934_at"),
pre_mirna=c("hsa-mir-16-1", "hsa-mir-15b/16-2", "hsa-mir-15b/16-2",
"hsa-mir-15b/16-2", "hsa-mir-223"),
exprs=c(8.9, 5.4, 8.8, 12.1, 4.3))
rownames(tx.exprs) <- tx.exprs$probeset_id
xNamesAre <- "mat_mirna_name"
yNamesAre <- "probeset_id"
suppNames <- c("pre_mirna_id", "pre_mirna_name", "tx_id", "mat_mirna_id", "mat_mirna_name",
"gene_id", "gene_name", "probeset_id")
## define the table name for each...
names(suppNames) <- c("pre_mirna", "pre_mirna", "host_tx", "mat_mirna", "mat_mirna",
"host_gene", "host_gene", "array_feature")
xNamesAre <- match.arg(xNamesAre, suppNames)
yNamesAre <- match.arg(yNamesAre, suppNames)
## just check if we do have probesets available... if needed...
if(xNamesAre == "probeset_id" | yNamesAre == "probeset_id"){
if(!object@have_array_features)
stop("No probe set definitions available in the database! Thus 'probeset_id' can not be used for arguments 'xNamesAre' or 'yNamesAre'.")
}
## use .getWhat (without filter), but need to define start.table...
allData <- mirhostgenes:::.getWhat(object, columns=c(xNamesAre, yNamesAre),
start.table=names(suppNames)[suppNames == xNamesAre])
## OK, that's now the mapping between names in x and names in y.
## check for how many in x and in y we don't have values...
## I run into a problem here... same-named values is nice, but what if we have a situation like here?
head(allData)
## assume, have a value for let-7a-5p, and one each for the probe sets.
## 1) map first to pre-miRNAs? i.e. select for each pre-miRNA the one mat_mirna, or select
## all... use doSelectData using the pre-miRNA id replacing the mat_mirna.
## mapping:
## mat_miRNA -> host_tx/probe set:
## for each pre-miRNA, select mat_miRNAs (all or just one.)
## common name is the pre-miRNA!
## Test it!
## first get from mat miRNA to pre-miRNA
mat2pre <- mirhostgenes:::.getWhat(object, columns=c(xNamesAre, "pre_mirna_id"),
start.table=names(suppNames)[suppNames == xNamesAre])
mat2pre <- mat2pre[mat2pre[, xNamesAre] %in% names(miRNA.exprs), ]
Test <- miRNA.exprs[mat2pre[, xNamesAre]]
names(Test) <- mat2pre[, "pre_mirna_id"]
pres <- doSelectData(Test, chooseOrderedValue)
predata <- pres$x
names(predata) <- pres$name
## that way I would have values for pre-miRNAs, that I could match then to host genes.
## mapping pre-miRNAs to probe sets is n:m
pre2ps <- mirhostgenes:::.getWhat(object, columns=c("pre_mirna_id", "probeset_id"),
start.table="pre_mirna")
pre2ps <- pre2ps[pre2ps$pre_mirna_id %in% names(predata), ]
}
test_transferValues <- function(){
## load data
library(MirhostDb.Hsapiens.v75.v20)
mhg <- MirhostDb.Hsapiens.v75.v20
## define values
miRNA.exprs <- c(8, 8.3, 5.6, 9.5, 4.6, 13.1)
names(miRNA.exprs) <- c("hsa-miR-15b-3p", "hsa-miR-15b-5p", "hsa-miR-16-5p",
"hsa-miR-16-1-3p", "hsa-miR-223-3p", "hsa-miR-223-5p")
## map mat miRNAs to pre-miRNAs, select all.
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name")
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs, ResX[names(miRNA.exprs)])
## what if we map to host genes?
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_name")
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs, ResX[names(miRNA.exprs)])
## use a solveFun
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name", solveFun=chooseOrderedValue)
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs[names(ResX)], ResX)
## use a solveFun, host gene
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_name", solveFun=chooseOrderedValue)
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs[names(ResX)], ResX)
## now: use a seqstrand filter, that should only return values for pre-miRNA-16-2
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name",
filter=SeqStrandFilter(1), na.rm=TRUE)
checkEquals(any(Res$pre_mirna_name == "hsa-mir-16-1"), FALSE)
## check if the filter works for gene_biotype
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_id",
na.rm=TRUE, filter=DatabaseFilter("core"))
Res <- hostgenes(mhg, filter=GeneIdFilter(Res$gene_id))
checkEquals(unique(Res$database), "core")
## biotype
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_id",
na.rm=TRUE,
filter=GeneBiotypeFilter("miRNA", condition="!="))
Res <- hostgenes(mhg, filter=GeneIdFilter(Res$gene_id))
checkEquals(any(Res$gene_biotype == "miRNA"), FALSE)
}
jotsetung/mirhostgenes documentation built on May 19, 2019, 9:42 p.m.
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## testing the value matching...
test_doPairData <- function(){
## Simple test:
A <- 1:10
names(A) <- c("b", "a", "c", "b", "a", "a", "e", "f", "a", "c")
B <- c(2, 5, 3, 2, 5, 8, 4, 20)
names(B) <- c("a", "b", "a", "c", "c", "c", "n", "f")
Res <- doPairData(A, B, chooseFunX=chooseAll, chooseFunY=chooseAll)
expectx <- c(1, 4, 2, 2, 5, 5, 6, 6, 9, 9, 3, 3, 3, 10, 10, 10, 8, 7, NA)
expecty <- c(5, 5, 2, 3, 2, 3, 2, 3, 2, 3, 2, 5, 8, 2, 5, 8, 20, NA, 4)
checkEquals(Res$x, expectx)
checkEquals(Res$y, expecty)
## now let's use chooseAll for x and chooseOrderedValue for y
expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
expecty <- c(5, 5, 3, 3, 3, 3, 8, 8, 20, NA, 4)
Res <- doPairData(A, B, chooseFunY=chooseOrderedValue)
checkEquals(Res$x, expectx)
checkEquals(Res$y, expecty)
## same but for chooseOrderedValue for x:
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue)
expectx <- c(4, 9, 9, 10, 10, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## now with chooseOrderedValue for both.
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseOrderedValue)
expectx <- c(4, 9, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## ## OK, now with closest value for y, all for x
## ## what does this: selects all values from A and selects the one value from B for
## ## each matching name that is closest to any of the values with the same name in A
## ## Not very meaningfull though...
## Res <- doPairData(A, B, chooseFunY=chooseClosestBetween)
## expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 5, 2, 2, 2, 2, 2, 2, 20, NA, 4)
## checkEquals(Res$y, expecty)
## ## reverse thing:
## ## first select the one value from A closest to any of the values in B with the same
## ## name and then match that to all values in B; also not that meaningful
## Res <- doPairData(A, B, chooseFunX=chooseClosestBetween)
## expectx <- c(4, 2, 2, 3, 3, 3, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## OK, more meaningful:
## all in x and for each in x the closest from y with the same name.
## fails with second xselect
Res <- doPairData(A, B, chooseFunY=chooseClosestForEach)
expectx <- c(1, 4, 2, 5, 6, 9, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 5, 2, 3, 3, 3, 2, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## reverse
## all in y and choose for each the closest in x
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach)
expectx <- c(4, 2, 2, 3, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 3, 2, 5, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## ## now something meaningful:
## ## select first the largest value in x and then select the closest in y
## ## NOT meaningful, since it depends on the order the functions are applied!
## Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseClosestBetween)
## expectx <- c(4, 9, 10, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 3, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## ## not meaningful... other way round.
## ## select first the value best matching in y and then the largest in y
## ## NOT meaningful, since it depends on the order the functions are applied!
## Res <- doPairData(A, B, chooseFunX=chooseClosestBetween, chooseFunY=chooseOrderedValue)
## expectx <- c(4, 2, 3, 8, 7, NA)
## checkEquals(Res$x, expectx)
## expecty <- c(5, 3, 8, 20, NA, 4)
## checkEquals(Res$y, expecty)
## use the chooseClosestForEach insted... should give the same result than chooseClosestBetween
## i.e. first select the largest value in x and then select the closest from y matching
## THAT selected value in x.
Res <- doPairData(A, B, chooseFunX=chooseOrderedValue, chooseFunY=chooseClosestForEach)
expectx <- c(4, 9, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## reverse order; should also make sense!
## select for each in y the closest in x, then select the largest in y.
## Note: this is different than using chooseOrderedValue for x and chooseClosestForEach for y
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach, chooseFunY=chooseOrderedValue)
expectx <- c(4, 2, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 3, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## now, chooseClosestForEach for both: selects the closest pairs of data.
## this selects first the closest in x to the values in y, then, for all in y the
## closest in x (already subsetted to those that are close to values in y).
Res <- doPairData(A, B, chooseFunX=chooseClosestForEach, chooseFunY=chooseClosestForEach)
## b: A x(1, 4), B y(5):
## chooseX: (4)
## chooseY: (5)
## a: A x(2, 5, 6, 9), B y(2, 3)
## chooseX: (2, 2)
## chooseY: (2, 2)
## c: A x(3, 10), B y(2, 5, 8)
## chooseX: (3, 3, 10)
## chooseY: (2, 2, 8)
## f: A x(8), B y(20)
## chooseX: (8)
## chooseY: (20)
## e: A x(7), B y(NA)
## n: A x(NA), B y(4)
## At last unique is called on the resulting data.frame that reduced the values for
## a and c.
expectx <- c(4, 2, 3, 10, 8, 7, NA)
checkEquals(Res$x, expectx)
expecty <- c(5, 2, 2, 8, 20, NA, 4)
checkEquals(Res$y, expecty)
## second example:
C <- c(3, 8, 7, 1)
names(C) <- rep("a", length(C))
D <- c(9, 2, 4, 20, 3)
names(D) <- rep("a", length(D))
Res <- doPairData(C, D, chooseFunX=chooseClosestForEach,
chooseFunY=chooseClosestForEach)
## same length for some
A <- 1:4
names(A) <- c("a", "a", "b", "b")
Res <- doPairData(A, A)
checkEquals(Res$x, rep(1:4, each=2))
checkEquals(Res$y, c(1, 2, 1, 2, 3, 4, 3, 4))
}
notrun_test_doMatchMir <- function(){
library(MirhostDb.Hsapiens.v81.v21)
object <- MirhostDb.Hsapiens.v81.v21
## that's now for miRNA matching...
## Test data.
## have mat miRNAs from pre-miRNAs miR-15b, 16-1 and 223
miRNA.exprs <- c(8, 8.3, 5.6, 9.5, 4.6, 13.1)
names(miRNA.exprs) <- c("hsa-miR-15b-3p", "hsa-miR-15b-5p", "hsa-miR-16-5p", "hsa-miR-16-1-3p", "hsa-miR-223-3p", "hsa-miR-223-5p")
## expression data
tx.exprs <- data.frame(tx_id=c("NR_002612.1", "ENST00000344722", "ENST00000344722",
"ENST00000468653", "ENSESTT00000026526"),
probeset_id=c("1564443_at", "201663_s_at", "201664_at",
"215623_x_at", "229934_at"),
pre_mirna=c("hsa-mir-16-1", "hsa-mir-15b/16-2", "hsa-mir-15b/16-2",
"hsa-mir-15b/16-2", "hsa-mir-223"),
exprs=c(8.9, 5.4, 8.8, 12.1, 4.3))
rownames(tx.exprs) <- tx.exprs$probeset_id
xNamesAre <- "mat_mirna_name"
yNamesAre <- "probeset_id"
suppNames <- c("pre_mirna_id", "pre_mirna_name", "tx_id", "mat_mirna_id", "mat_mirna_name",
"gene_id", "gene_name", "probeset_id")
## define the table name for each...
names(suppNames) <- c("pre_mirna", "pre_mirna", "host_tx", "mat_mirna", "mat_mirna",
"host_gene", "host_gene", "array_feature")
xNamesAre <- match.arg(xNamesAre, suppNames)
yNamesAre <- match.arg(yNamesAre, suppNames)
## just check if we do have probesets available... if needed...
if(xNamesAre == "probeset_id" | yNamesAre == "probeset_id"){
if(!object@have_array_features)
stop("No probe set definitions available in the database! Thus 'probeset_id' can not be used for arguments 'xNamesAre' or 'yNamesAre'.")
}
## use .getWhat (without filter), but need to define start.table...
allData <- mirhostgenes:::.getWhat(object, columns=c(xNamesAre, yNamesAre),
start.table=names(suppNames)[suppNames == xNamesAre])
## OK, that's now the mapping between names in x and names in y.
## check for how many in x and in y we don't have values...
## I run into a problem here... same-named values is nice, but what if we have a situation like here?
head(allData)
## assume, have a value for let-7a-5p, and one each for the probe sets.
## 1) map first to pre-miRNAs? i.e. select for each pre-miRNA the one mat_mirna, or select
## all... use doSelectData using the pre-miRNA id replacing the mat_mirna.
## mapping:
## mat_miRNA -> host_tx/probe set:
## for each pre-miRNA, select mat_miRNAs (all or just one.)
## common name is the pre-miRNA!
## Test it!
## first get from mat miRNA to pre-miRNA
mat2pre <- mirhostgenes:::.getWhat(object, columns=c(xNamesAre, "pre_mirna_id"),
start.table=names(suppNames)[suppNames == xNamesAre])
mat2pre <- mat2pre[mat2pre[, xNamesAre] %in% names(miRNA.exprs), ]
Test <- miRNA.exprs[mat2pre[, xNamesAre]]
names(Test) <- mat2pre[, "pre_mirna_id"]
pres <- doSelectData(Test, chooseOrderedValue)
predata <- pres$x
names(predata) <- pres$name
## that way I would have values for pre-miRNAs, that I could match then to host genes.
## mapping pre-miRNAs to probe sets is n:m
pre2ps <- mirhostgenes:::.getWhat(object, columns=c("pre_mirna_id", "probeset_id"),
start.table="pre_mirna")
pre2ps <- pre2ps[pre2ps$pre_mirna_id %in% names(predata), ]
}
test_transferValues <- function(){
## load data
library(MirhostDb.Hsapiens.v75.v20)
mhg <- MirhostDb.Hsapiens.v75.v20
## define values
miRNA.exprs <- c(8, 8.3, 5.6, 9.5, 4.6, 13.1)
names(miRNA.exprs) <- c("hsa-miR-15b-3p", "hsa-miR-15b-5p", "hsa-miR-16-5p",
"hsa-miR-16-1-3p", "hsa-miR-223-3p", "hsa-miR-223-5p")
## map mat miRNAs to pre-miRNAs, select all.
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name")
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs, ResX[names(miRNA.exprs)])
## what if we map to host genes?
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_name")
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs, ResX[names(miRNA.exprs)])
## use a solveFun
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name", solveFun=chooseOrderedValue)
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs[names(ResX)], ResX)
## use a solveFun, host gene
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_name", solveFun=chooseOrderedValue)
ResX <- Res$x
names(ResX) <- Res$mat_mirna_name
checkEquals(miRNA.exprs[names(ResX)], ResX)
## now: use a seqstrand filter, that should only return values for pre-miRNA-16-2
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="pre_mirna_name",
filter=SeqStrandFilter(1), na.rm=TRUE)
checkEquals(any(Res$pre_mirna_name == "hsa-mir-16-1"), FALSE)
## check if the filter works for gene_biotype
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_id",
na.rm=TRUE, filter=DatabaseFilter("core"))
Res <- hostgenes(mhg, filter=GeneIdFilter(Res$gene_id))
checkEquals(unique(Res$database), "core")
## biotype
Res <- transferValues(miRNA.exprs, mhg, xNamesAre="mat_mirna_name",
toNames="gene_id",
na.rm=TRUE,
filter=GeneBiotypeFilter("miRNA", condition="!="))
Res <- hostgenes(mhg, filter=GeneIdFilter(Res$gene_id))
checkEquals(any(Res$gene_biotype == "miRNA"), FALSE)
}
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