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.
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.
## 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, sweep## This is a SeqArray GDS 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"## <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 3## <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.
## 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.
## function (file, ftnode, smpnode, assayNames = NULL, rowDataColumns = NULL,
## colDataColumns = NULL, rowDataOnDisk = TRUE, colDataOnDisk = TRUE,
## infoColumns = NULL)
## NULL## 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.
## List of length 3
## names(3): genotype/data phase/data annotation/format/DP/data## <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 3## <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.
## 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 seqlengths## 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().
## 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.
## [1] "/github/workspace/pkglib/SeqArray/extdata/CEU_Exon.gds"Some other getter function like metadata() will return
any metadata that we have saved inside the
VariantExperiment object.
## 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:
## [1] "/tmp/RtmpplSebI/filedc04120bc9/se.gds"assay data is in DelayedArray format:
## <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:
## 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 "".## 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.
## 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.
## 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).
## 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.
## 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 48## 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 [, [[, $, and ranged-based
subsetting operations using subsetByOverlap.
## class: VariantExperiment
## dim: 10 5
## metadata(0):
## assays(3): genotype/data phase/data annotation/format/DP/data
## rownames(10): 1 2 ... 9 10
## rowData names(13): annotation.id annotation.qual ... info.GP info.BN
## colnames(5): NA06984 NA06985 NA06986 NA06989 NA06994
## colData names(1): family$ 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.
## DelayedDataFrame with 90 rows and 1 column
## family
## <GDSArray>
## NA06984 1328
## NA06985
## NA06986 13291
## ... ...
## NA12890 1463
## NA12891
## NA12892## 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.
## 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 114## 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.
## 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.
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.
## [1] "/tmp/RtmpplSebI/filedc0637bf129/ve.gds"## [1] TRUE## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GDSArray_1.32.0 DelayedArray_0.38.2
## [3] SparseArray_1.12.2 S4Arrays_1.12.0
## [5] abind_1.4-8 Matrix_1.7-5
## [7] gdsfmt_1.48.1 VariantExperiment_1.26.0
## [9] SummarizedExperiment_1.42.0 Biobase_2.72.0
## [11] GenomicRanges_1.64.0 Seqinfo_1.2.0
## [13] IRanges_2.46.0 MatrixGenerics_1.24.0
## [15] matrixStats_1.5.0 S4Vectors_0.50.1
## [17] BiocGenerics_0.58.1 generics_0.1.4
## [19] BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_2.0.0 crayon_1.5.3 compiler_4.6.0
## [4] BiocManager_1.30.27 Biostrings_2.80.1 parallel_4.6.0
## [7] SNPRelate_1.46.0 jquerylib_0.1.4 RhpcBLASctl_0.23-42
## [10] yaml_2.3.12 fastmap_1.2.0 lattice_0.22-9
## [13] R6_2.6.1 XVector_0.52.0 knitr_1.51
## [16] maketools_1.3.2 bslib_0.11.0 rlang_1.2.0
## [19] cachem_1.1.0 xfun_0.57 sass_0.4.10
## [22] sys_3.4.3 cli_3.6.6 digest_0.6.39
## [25] grid_4.6.0 SeqArray_1.52.0 DelayedDataFrame_1.28.0
## [28] lifecycle_1.0.5 evaluate_1.0.5 buildtools_1.0.0
## [31] rmarkdown_2.31 tools_4.6.0 htmltools_0.5.9