VariantExperiment 1.21.0
With the rapid development of the biotechnologies, the sequencing (e.g., DNA, bulk/single-cell RNA, etc.) and other types of biological data are getting increasingly larger-profile. The memory space in R has been an obstable for fast and efficient data processing, because most available R or Bioconductor packages are developed based on in-memory data manipulation. SingleCellExperiment has achieved efficient on-disk saving/reading of the large-scale count data as HDF5Array objects. However, there was still no such light-weight containers available for high-throughput variant data (e.g., DNA-seq, genotyping, etc.).
We have developed VariantExperiment, a Bioconductor package to
contain variant data into RangedSummarizedExperiment object. The
package converts and represent VCF/GDS files using standard
SummarizedExperiment metaphor. It is a container for high-through
variant data with GDS back-end.
In VariantExperiment, The high-throughput variant data is saved in
DelayedArray objects with GDS back-end. In addition to the
light-weight Assay data, it also supports the on-disk saving of
annotation data for both features and samples (corresponding to
rowData/colData respectively) by implementing the
DelayedDataFrame data structure. The on-disk representation of
both assay data and annotation data realizes on-disk reading and
processing and saves R memory space significantly. The interface of
RangedSummarizedExperiment data format enables easy and common
manipulations for high-throughput variant data with common
SummarizedExperiment metaphor in R and Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VariantExperiment")Or install the development version of the package from Github.
BiocManager::install("Bioconductor/VariantExperiment") library(VariantExperiment)GDSArray is a Bioconductor package that represents GDS files as
objects derived from the DelayedArray package and DelayedArray
class. It converts GDS nodes into a DelayedArray-derived data
structure. The rich common methods and data operations defined on
GDSArray makes it more R-user-friendly than working with the GDS
file directly.
The GDSArray() constructor takes 2 arguments: the file path and the
GDS node name (which can be retrieved with the gdsnodes() function)
inside the GDS file.
library(GDSArray)## Loading required package: gdsfmt## Loading required package: DelayedArray## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:S4Vectors':
##
## expand## Loading required package: S4Arrays## Loading required package: abind##
## Attaching package: 'S4Arrays'## The following object is masked from 'package:abind':
##
## abind## The following object is masked from 'package:base':
##
## rowsum## Loading required package: SparseArray##
## Attaching package: 'DelayedArray'## The following objects are masked from 'package:base':
##
## apply, scale, sweepfile <- GDSArray::gdsExampleFileName("seqgds")## This is a SeqArray GDS filegdsnodes(file)## [1] "sample.id" "variant.id"
## [3] "position" "chromosome"
## [5] "allele" "genotype/data"
## [7] "genotype/~data" "genotype/extra.index"
## [9] "genotype/extra" "phase/data"
## [11] "phase/~data" "phase/extra.index"
## [13] "phase/extra" "annotation/id"
## [15] "annotation/qual" "annotation/filter"
## [17] "annotation/info/AA" "annotation/info/AC"
## [19] "annotation/info/AN" "annotation/info/DP"
## [21] "annotation/info/HM2" "annotation/info/HM3"
## [23] "annotation/info/OR" "annotation/info/GP"
## [25] "annotation/info/BN" "annotation/format/DP/data"
## [27] "annotation/format/DP/~data" "sample.annotation/family"GDSArray(file, "genotype/data")## <2 x 90 x 1348> GDSArray object of type "integer":
## ,,1
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 0 0 0 0
## [2,] 3 3 0 3 . 0 0 0 0
##
## ,,2
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 0 0 0 0
## [2,] 3 3 0 3 . 0 0 0 0
##
## ...
##
## ,,1347
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 0 0 0 0 . 0 0 0 0
## [2,] 0 0 0 0 . 0 0 0 0
##
## ,,1348
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 3 3 3 3
## [2,] 3 3 1 3 . 3 3 3 3GDSArray(file, "sample.id")## <90> GDSArray object of type "character":
## [1] [2] [3] . [89] [90]
## "NA06984" "NA06985" "NA06986" . "NA12891" "NA12892"More details about GDS or GDSArray format can be found in the
vignettes of the gdsfmt, SNPRelate, SeqArray, GDSArray
and DelayedArray packages.
DelayedDataFrame is a Bioconductor package that implements
delayed operations on DataFrame objects using standard DataFrame
metaphor. Each column of data inside DelayedDataFrame is represented
as 1-dimensional GDSArray with on-disk GDS file. Methods like
show,validity check, [, [[ subsetting, rbind, cbind are
implemented for DelayedDataFrame. The DelayedDataFrame stays lazy
until an explicit realization call like DataFrame() constructor or
as.list() triggered. More details about DelayedDataFrame data
structure could be found in the vignette of DelayedDataFrame
package.
VariantExperiment classVariantExperiment classVariantExperiment class is defined to extend
RangedSummarizedExperiment. The difference would be that the assay
data are saved as DelayedArray, and the annotation data are saved by
default as DelayedDataFrame (with option to save as ordinary
DataFrame), both of which are representing the data on-disk with
GDS back-end.
Conversion methods into VariantExperiment object are
defined directly for VCF and GDS files. Here we show one simple
example to convert a DNA-sequencing data in GDS format into
VariantExperiment and some class-related operations.
ve <- makeVariantExperimentFromGDS(file)
ve## class: VariantExperiment
## dim: 1348 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(1348): 1 2 ... 1347 1348
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(1): familyIn this example, the sequencing file in GDS format was converted into
a VariantExperiment object, with all available assay data saved into
the assay slot, all available feature annotation nodes into
rowRanges/rowData slot, and all available sample annotation nodes
into colData slot. The available values for each arguments in
makeVariantExperimentFromGDS() function can be retrieved using the
showAvailable() function.
args(makeVariantExperimentFromGDS)## function (file, ftnode, smpnode, assayNames = NULL, rowDataColumns = NULL,
## colDataColumns = NULL, rowDataOnDisk = TRUE, colDataOnDisk = TRUE,
## infoColumns = NULL)
## NULLshowAvailable(file)## CharacterList of length 4
## [["assayNames"]] genotype/data phase/data annotation/format/DP/data
## [["rowDataColumns"]] allele annotation/id annotation/qual annotation/filter
## [["colDataColumns"]] family
## [["infoColumns"]] AC AN DP HM2 HM3 OR GP BNAssay data are in GDSArray format, and could be retrieve by the
assays()/assay() function. NOTE that when converted into a
VariantExperiment object, the assay data will be checked and
permuted, so that the first 2 dimensions always match to features
(variants/snps) and samples respectively, no matter how are the
dimensions are with the original GDSArray that can be constructed.
assays(ve)## List of length 3
## names(3): genotype/data phase/data annotation/format/DP/dataassay(ve, 1)## <1348 x 90 x 2> DelayedArray object of type "integer":
## ,,1
## NA06984 NA06985 NA06986 NA06989 ... NA12889 NA12890 NA12891 NA12892
## 1 3 3 0 3 . 0 0 0 0
## 2 3 3 0 3 . 0 0 0 0
## ... . . . . . . . . .
## 1347 0 0 0 0 . 0 0 0 0
## 1348 3 3 0 3 . 3 3 3 3
##
## ,,2
## NA06984 NA06985 NA06986 NA06989 ... NA12889 NA12890 NA12891 NA12892
## 1 3 3 0 3 . 0 0 0 0
## 2 3 3 0 3 . 0 0 0 0
## ... . . . . . . . . .
## 1347 0 0 0 0 . 0 0 0 0
## 1348 3 3 1 3 . 3 3 3 3GDSArray(file, "genotype/data") ## original GDSArray from GDS file before permutation## <2 x 90 x 1348> GDSArray object of type "integer":
## ,,1
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 0 0 0 0
## [2,] 3 3 0 3 . 0 0 0 0
##
## ,,2
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 0 0 0 0
## [2,] 3 3 0 3 . 0 0 0 0
##
## ...
##
## ,,1347
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 0 0 0 0 . 0 0 0 0
## [2,] 0 0 0 0 . 0 0 0 0
##
## ,,1348
## [,1] [,2] [,3] [,4] ... [,87] [,88] [,89] [,90]
## [1,] 3 3 0 3 . 3 3 3 3
## [2,] 3 3 1 3 . 3 3 3 3In this example, the original GDSArray object from genotype data was
2 x 90 x 1348. But it was permuted to 1348 x 90 x 2 when
constructed into the VariantExperiment object.
The rowData() of the VariantExperiment is by default saved in
DelayedDataFrame format. We can use rowRanges() / rowData() to
retrieve the feature-related annotation file, with/without a
GenomicRange format.
rowRanges(ve)## GRanges object with 1348 ranges and 13 metadata columns:
## seqnames ranges strand | annotation.id annotation.qual
## <Rle> <IRanges> <Rle> | <GDSArray> <GDSArray>
## 1 1 1105366 * | rs111751804 NaN
## 2 1 1105411 * | rs114390380 NaN
## 3 1 1110294 * | rs1320571 NaN
## ... ... ... ... . ... ...
## 1346 22 43691009 * | rs8135982 NaN
## 1347 22 43691073 * | rs116581756 NaN
## 1348 22 48958933 * | rs5771206 NaN
## annotation.filter REF ALT info.AC info.AN
## <GDSArray> <DelayedArray> <DelayedArray> <GDSArray> <GDSArray>
## 1 PASS T C 4 114
## 2 PASS G A 1 106
## 3 PASS G A 6 154
## ... ... ... ... ... ...
## 1346 PASS C T 11 142
## 1347 PASS G A 1 152
## 1348 PASS A G 1 6
## info.DP info.HM2 info.HM3 info.OR info.GP info.BN
## <GDSArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray>
## 1 3251 0 0 1:1115503 132
## 2 2676 0 0 1:1115548 132
## 3 7610 1 1 1:1120431 88
## ... ... ... ... ... ... ...
## 1346 823 0 0 22:45312345 116
## 1347 1257 0 0 22:45312409 132
## 1348 48 0 0 22:50616806 114
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsrowData(ve)## DelayedDataFrame with 1348 rows and 13 columns
## annotation.id annotation.qual annotation.filter REF
## <GDSArray> <GDSArray> <GDSArray> <DelayedArray>
## 1 rs111751804 NaN PASS T
## 2 rs114390380 NaN PASS G
## 3 rs1320571 NaN PASS G
## ... ... ... ... ...
## 1346 rs8135982 NaN PASS C
## 1347 rs116581756 NaN PASS G
## 1348 rs5771206 NaN PASS A
## ALT info.AC info.AN info.DP info.HM2 info.HM3
## <DelayedArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray>
## 1 C 4 114 3251 0 0
## 2 A 1 106 2676 0 0
## 3 A 6 154 7610 1 1
## ... ... ... ... ... ... ...
## 1346 T 11 142 823 0 0
## 1347 A 1 152 1257 0 0
## 1348 G 1 6 48 0 0
## info.OR info.GP info.BN
## <GDSArray> <GDSArray> <GDSArray>
## 1 1:1115503 132
## 2 1:1115548 132
## 3 1:1120431 88
## ... ... ... ...
## 1346 22:45312345 116
## 1347 22:45312409 132
## 1348 22:50616806 114sample-related annotation is by default in DelayedDataFrame format,
and could be retrieved by colData().
colData(ve)## DelayedDataFrame with 90 rows and 1 column
## family
## <GDSArray>
## NA06984 1328
## NA06985
## NA06986 13291
## ... ...
## NA12890 1463
## NA12891
## NA12892The gdsfile() will retrieve the gds file path associated with the
VariantExperiment object.
gdsfile(ve)## [1] "/home/biocbuild/bbs-3.21-bioc/R/site-library/SeqArray/extdata/CEU_Exon.gds"Some other getter function like metadata() will return any metadata
that we have saved inside the VariantExperiment object.
metadata(ve)## list()To take advantage of the functions and methods that are defined on
SummarizedExperiment, from which the VariantExperiment extends, we
have defined coercion methods from VCF and GDS to
VariantExperiment.
VCF to VariantExperimentThe coercion function of makeVariantExperimentFromVCF could
convert the VCF file directly into VariantExperiment object. To
achieve the best storage efficiency, the assay data are saved in
DelayedArray format, and the annotation data are saved in
DelayedDataFrame format (with no option of ordinary DataFrame),
which could be retrieved by rowData() for feature related
annotations and colData() for sample related annotations (Only when
sample.info argument is specified).
vcf <- SeqArray::seqExampleFileName("vcf")
ve <- makeVariantExperimentFromVCF(vcf, out.dir = tempfile())
ve## class: VariantExperiment
## dim: 1348 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(1348): 1 2 ... 1347 1348
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(0):Internally, the VCF file was converted into a on-disk GDS file,
which could be retrieved by:
gdsfile(ve)## [1] "/tmp/RtmpmAxygd/file206ac84dbd8895/se.gds"assay data is in DelayedArray format:
assay(ve, 1)## <1348 x 90 x 2> DelayedArray object of type "integer":
## ,,1
## NA06984 NA06985 NA06986 NA06989 ... NA12889 NA12890 NA12891 NA12892
## 1 3 3 0 3 . 0 0 0 0
## 2 3 3 0 3 . 0 0 0 0
## ... . . . . . . . . .
## 1347 0 0 0 0 . 0 0 0 0
## 1348 3 3 0 3 . 3 3 3 3
##
## ,,2
## NA06984 NA06985 NA06986 NA06989 ... NA12889 NA12890 NA12891 NA12892
## 1 3 3 0 3 . 0 0 0 0
## 2 3 3 0 3 . 0 0 0 0
## ... . . . . . . . . .
## 1347 0 0 0 0 . 0 0 0 0
## 1348 3 3 1 3 . 3 3 3 3feature-related annotation is in DelayedDataFrame format:
rowData(ve)## DelayedDataFrame with 1348 rows and 13 columns
## annotation.id annotation.qual annotation.filter REF
## <GDSArray> <GDSArray> <GDSArray> <DelayedArray>
## 1 rs111751804 NaN PASS T
## 2 rs114390380 NaN PASS G
## 3 rs1320571 NaN PASS G
## ... ... ... ... ...
## 1346 rs8135982 NaN PASS C
## 1347 rs116581756 NaN PASS G
## 1348 rs5771206 NaN PASS A
## ALT info.AC info.AN info.DP info.HM2 info.HM3
## <DelayedArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray>
## 1 C 4 114 3251 0 0
## 2 A 1 106 2676 0 0
## 3 A 6 154 7610 1 1
## ... ... ... ... ... ... ...
## 1346 T 11 142 823 0 0
## 1347 A 1 152 1257 0 0
## 1348 G 1 6 48 0 0
## info.OR info.GP info.BN
## <GDSArray> <GDSArray> <GDSArray>
## 1 1:1115503 132
## 2 1:1115548 132
## 3 1:1120431 88
## ... ... ... ...
## 1346 22:45312345 116
## 1347 22:45312409 132
## 1348 22:50616806 114User could also have the opportunity to save the sample related
annotation info directly into the VariantExperiment object, by
providing the file path to the sample.info argument, and then
retrieve by colData().
sampleInfo <- system.file("extdata", "Example_sampleInfo.txt",
package="VariantExperiment")
vevcf <- makeVariantExperimentFromVCF(vcf, sample.info = sampleInfo)## Warning in (function (node, name, val = NULL, storage = storage.mode(val), :
## Missing characters are converted to "".colData(vevcf)## DelayedDataFrame with 90 rows and 1 column
## family
## <GDSArray>
## NA06984 1328
## NA06985
## NA06986 13291
## ... ...
## NA12890 1463
## NA12891
## NA12892Arguments could be specified to take only certain info columns or format columns from the vcf file.
vevcf1 <- makeVariantExperimentFromVCF(vcf, info.import=c("OR", "GP"))
rowData(vevcf1)## DelayedDataFrame with 1348 rows and 7 columns
## annotation.id annotation.qual annotation.filter REF
## <GDSArray> <GDSArray> <GDSArray> <DelayedArray>
## 1 rs111751804 NaN PASS T
## 2 rs114390380 NaN PASS G
## 3 rs1320571 NaN PASS G
## ... ... ... ... ...
## 1346 rs8135982 NaN PASS C
## 1347 rs116581756 NaN PASS G
## 1348 rs5771206 NaN PASS A
## ALT info.OR info.GP
## <DelayedArray> <GDSArray> <GDSArray>
## 1 C 1:1115503
## 2 A 1:1115548
## 3 A 1:1120431
## ... ... ... ...
## 1346 T 22:45312345
## 1347 A 22:45312409
## 1348 G 22:50616806In the above example, only 2 info entries (“OR” and “GP”) are read
into the VariantExperiment object.
The start and count arguments could be used to specify the start
position and number of variants to read into Variantexperiment
object.
vevcf2 <- makeVariantExperimentFromVCF(vcf, start=101, count=1000)
vevcf2## class: VariantExperiment
## dim: 1000 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(1000): 101 102 ... 1099 1100
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(0):For the above example, only 1000 variants are read into the
VariantExperiment object, starting from the position of 101.
GDS to VariantExperimentThe coercion function of makeVariantExperimentFromGDS coerces GDS
files into VariantExperiment objects directly, with the assay data
saved as DelayedArray, and the rowData()/colData() in
DelayedDataFrame by default (with the option of ordinary DataFrame
object).
gds <- SeqArray::seqExampleFileName("gds")
ve <- makeVariantExperimentFromGDS(gds)
ve## class: VariantExperiment
## dim: 1348 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(1348): 1 2 ... 1347 1348
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(1): familyArguments could be specified to take only certain annotation columns
for features and samples. All available data entries for
makeVariantExperimentFromGDS arguments could be retrieved by the
showAvailable() function with the gds file name as input.
showAvailable(gds)## CharacterList of length 4
## [["assayNames"]] genotype/data phase/data annotation/format/DP/data
## [["rowDataColumns"]] allele annotation/id annotation/qual annotation/filter
## [["colDataColumns"]] family
## [["infoColumns"]] AC AN DP HM2 HM3 OR GP BNNote that the infoColumns from gds file will be saved as columns
inside the rowData(), with the prefix of
“info.”. rowDataOnDisk/colDataOnDisk could be set as FALSE to
save all annotation data in ordinary DataFrame format.
ve3 <- makeVariantExperimentFromGDS(gds,
rowDataColumns = c("allele", "annotation/id"),
infoColumns = c("AC", "AN", "DP"),
rowDataOnDisk = TRUE,
colDataOnDisk = FALSE)
rowData(ve3) ## DelayedDataFrame object ## DelayedDataFrame with 1348 rows and 6 columns
## annotation.id REF ALT info.AC info.AN
## <GDSArray> <DelayedArray> <DelayedArray> <GDSArray> <GDSArray>
## 1 rs111751804 T C 4 114
## 2 rs114390380 G A 1 106
## 3 rs1320571 G A 6 154
## ... ... ... ... ... ...
## 1346 rs8135982 C T 11 142
## 1347 rs116581756 G A 1 152
## 1348 rs5771206 A G 1 6
## info.DP
## <GDSArray>
## 1 3251
## 2 2676
## 3 7610
## ... ...
## 1346 823
## 1347 1257
## 1348 48colData(ve3) ## DataFrame object## DataFrame with 90 rows and 1 column
## family
## <character>
## NA06984 1328
## NA06985
## NA06986 13291
## ... ...
## NA12890 1463
## NA12891
## NA12892For GDS formats of SEQ_ARRAY (defined in SeqArray as
SeqVarGDSClass class) and SNP_ARRAY (defined in SNPRelate as
SNPGDSFileClass class), we have made some customized transfer of
certain nodes when reading into VariantExperiment object for users’
convenience.
The allele node in SEQ_ARRAY gds file is converted into 2 columns
in rowData() asn REF and ALT.
veseq <- makeVariantExperimentFromGDS(file,
rowDataColumns = c("allele"),
infoColumns = character(0))
rowData(veseq)## DelayedDataFrame with 1348 rows and 2 columns
## REF ALT
## <DelayedArray> <DelayedArray>
## 1 T C
## 2 G A
## 3 G A
## ... ... ...
## 1346 C T
## 1347 G A
## 1348 A GThe snp.allele node in SNP_ARRAY gds file was converted into 2
columns in rowData() as snp.allele1 and snp.allele2.
snpfile <- SNPRelate::snpgdsExampleFileName()
vesnp <- makeVariantExperimentFromGDS(snpfile,
rowDataColumns = c("snp.allele"))
rowData(vesnp)## DelayedDataFrame with 9088 rows and 2 columns
## snp.allele1 snp.allele2
## <DelayedArray> <DelayedArray>
## 1 G T
## 2 C T
## 3 A G
## ... ... ...
## 9086 A G
## 9087 C T
## 9088 A CVariantExperiment supports basic subsetting operations using [,
[[, $, and ranged-based subsetting operations using
subsetByOverlap.
ve[1:10, 1:5]## class: VariantExperiment
## dim: 10 5
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(10): 1 2 ... 9 10
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(5): NA06984 NA06985 NA06986 NA06989 NA06994
## colData names(1): family$ subsettingThe $ subsetting can be operated directly on colData() columns,
for easy sample extraction. NOTE that the colData/rowData are
(by default) in the DelayedDataFrame format, with each column saved
as GDSArray. So when doing subsetting, we need to use as.logical()
to convert the 1-dimensional GDSArray into ordinary vector.
colData(ve)## DelayedDataFrame with 90 rows and 1 column
## family
## <GDSArray>
## NA06984 1328
## NA06985
## NA06986 13291
## ... ...
## NA12890 1463
## NA12891
## NA12892ve[, as.logical(ve$family == "1328")]## class: VariantExperiment
## dim: 1348 2
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(1348): 1 2 ... 1347 1348
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(2): NA06984 NA06989
## colData names(1): familysubsetting by rowData() columns.
rowData(ve)## DelayedDataFrame with 1348 rows and 13 columns
## annotation.id annotation.qual annotation.filter REF
## <GDSArray> <GDSArray> <GDSArray> <DelayedArray>
## 1 rs111751804 NaN PASS T
## 2 rs114390380 NaN PASS G
## 3 rs1320571 NaN PASS G
## ... ... ... ... ...
## 1346 rs8135982 NaN PASS C
## 1347 rs116581756 NaN PASS G
## 1348 rs5771206 NaN PASS A
## ALT info.AC info.AN info.DP info.HM2 info.HM3
## <DelayedArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray> <GDSArray>
## 1 C 4 114 3251 0 0
## 2 A 1 106 2676 0 0
## 3 A 6 154 7610 1 1
## ... ... ... ... ... ... ...
## 1346 T 11 142 823 0 0
## 1347 A 1 152 1257 0 0
## 1348 G 1 6 48 0 0
## info.OR info.GP info.BN
## <GDSArray> <GDSArray> <GDSArray>
## 1 1:1115503 132
## 2 1:1115548 132
## 3 1:1120431 88
## ... ... ... ...
## 1346 22:45312345 116
## 1347 22:45312409 132
## 1348 22:50616806 114ve[as.logical(rowData(ve)$REF == "T"),]## class: VariantExperiment
## dim: 214 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(214): 1 4 ... 1320 1328
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(1): familyVariantExperiment objects support all of the findOverlaps()
methods and associated functions. This includes subsetByOverlaps(),
which makes it easy to subset a VariantExperiment object by an
interval.
ve1 <- subsetByOverlaps(ve, GRanges("22:1-48958933"))
ve1## class: VariantExperiment
## dim: 23 90
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(23): 1326 1327 ... 1347 1348
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(90): NA06984 NA06985 ... NA12891 NA12892
## colData names(1): familyIn this example, only 23 out of 1348 variants were retained with the
GRanges subsetting.
VariantExperiment objectNote that after the subsetting by [, $ or Ranged-based operations, and you feel satisfied with the data for downstream
analysis, you need to save that VariantExperiment object to
synchronize the gds file (on-disk) associated with the subset of data
(in-memory representation) before any statistical analysis. Otherwise,
an error will be returned.
0
## save VariantExperiment object
Use the function saveVariantExperiment to synchronize the on-disk
and in-memory representation. This function writes the processed data
as ve.gds, and save the R object (which lazily represent the
backend data set) as ve.rds under the specified directory. It
finally returns a new VariantExperiment object into current R
session generated from the newly saved data.
a <- tempfile()
ve2 <- saveVariantExperiment(ve1, dir=a, replace=TRUE, chunk_size = 30)VariantExperiment objectYou can alternatively use loadVariantExperiment to load the
synchronized data into R session, by providing only the file
directory. It reads the VariantExperiment object saved as ve.rds, as lazy
representation of the backend ve.gds file under the specific
directory.
ve3 <- loadVariantExperiment(dir=a)
gdsfile(ve3)## [1] "/tmp/RtmpmAxygd/file206ac81d495637/ve.gds"all.equal(ve2, ve3)## [1] TRUEsessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GDSArray_1.27.0 DelayedArray_0.33.0
## [3] SparseArray_1.7.0 S4Arrays_1.7.0
## [5] abind_1.4-8 Matrix_1.7-1
## [7] gdsfmt_1.43.0 VariantExperiment_1.21.0
## [9] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [11] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [13] IRanges_2.41.0 MatrixGenerics_1.19.0
## [15] matrixStats_1.4.1 S4Vectors_0.45.0
## [17] BiocGenerics_0.53.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 compiler_4.5.0 BiocManager_1.30.25
## [4] crayon_1.5.3 Biostrings_2.75.0 parallel_4.5.0
## [7] SNPRelate_1.41.0 jquerylib_0.1.4 yaml_2.3.10
## [10] fastmap_1.2.0 lattice_0.22-6 R6_2.5.1
## [13] XVector_0.47.0 knitr_1.48 bookdown_0.41
## [16] GenomeInfoDbData_1.2.13 bslib_0.8.0 rlang_1.1.4
## [19] cachem_1.1.0 xfun_0.48 sass_0.4.9
## [22] cli_3.6.3 zlibbioc_1.53.0 digest_0.6.37
## [25] grid_4.5.0 SeqArray_1.47.0 DelayedDataFrame_1.23.0
## [28] lifecycle_1.0.4 evaluate_1.0.1 rmarkdown_2.28
## [31] httr_1.4.7 tools_4.5.0 htmltools_0.5.8.1
## [34] UCSC.utils_1.3.0