1 Introduction

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.

2 Installation

  1. Download the package from 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") 
  1. Load the package into R session.
library(VariantExperiment)

3 Background

3.1 GDSArray

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
## 
## Attaching package: 'DelayedArray'
## The following objects are masked from 'package:base':
## 
##     aperm, apply, rowsum, scale, sweep
file <- GDSArray::gdsExampleFileName("seqgds")
## This is a SeqArray GDS file
gdsnodes(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> array of class GDSArray and 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
GDSArray(file, "sample.id")
## <90> array of class GDSArray and 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.

3.2 DelayedDataFrame

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.

4 VariantExperiment class

4.1 VariantExperiment class

VariantExperiment 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): family

In 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) 
## NULL
showAvailable(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 BN

4.2 slot accessors

Assay 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/data
assay(ve, 1)
## <1348 x 90 x 2> array of class DelayedArray and 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
##     ...     .     .     .     .   .     .     .     .     .
## [1347,]     0     0     0     0   .     0     0     0     0
## [1348,]     3     3     0     3   .     3     3     3     3
## 
## ,,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,]     0     0     0     0   .     0     0     0     0
## [1348,]     3     3     1     3   .     3     3     3     3
GDSArray(file, "genotype/data")  ## original GDSArray from GDS file before permutation
## <2 x 90 x 1348> array of class GDSArray and 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

In 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 seqlengths
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        114

sample-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           
## NA12892

The gdsfile() will retrieve the gds file path associated with the VariantExperiment object.

gdsfile(ve)
## [1] "/home/biocbuild/bbs-3.16-bioc/R/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()

5 Coercion methods

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.

5.1 From VCF to VariantExperiment

The 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/RtmpTpK4mN/file307ba44322bb21/se.gds"

assay data is in DelayedArray format:

assay(ve, 1)
## <1348 x 90 x 2> array of class DelayedArray and 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
##     ...     .     .     .     .   .     .     .     .     .
## [1347,]     0     0     0     0   .     0     0     0     0
## [1348,]     3     3     0     3   .     3     3     3     3
## 
## ,,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,]     0     0     0     0   .     0     0     0     0
## [1348,]     3     3     1     3   .     3     3     3     3

feature-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        114

User 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           
## NA12892

Arguments 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            22:50616806

In 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.

5.2 From GDS to VariantExperiment

The 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): family

Arguments 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 BN

Note 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
colData(ve3)  ## DataFrame object
## DataFrame with 90 rows and 1 column
##              family
##         <character>
## NA06984        1328
## NA06985            
## NA06986       13291
## ...             ...
## NA12890        1463
## NA12891            
## NA12892

5.3 customization for certain gds types

For 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              G

The 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              C

6 Subsetting methods

VariantExperiment supports basic subsetting operations using [, [[, $, and ranged-based subsetting operations using subsetByOverlap.

6.1 two-dimensional subsetting

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

6.2 $ subsetting

The $ 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           
## NA12892
ve[, 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): family

subsetting 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        114
ve[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): family

6.3 Range-based operations

VariantExperiment 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): family

In this example, only 23 out of 1348 variants were retained with the GRanges subsetting.

7 Save / load VariantExperiment object

Note 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)

7.1 load VariantExperiment object

You 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/RtmpTpK4mN/file307ba46228e3d7/ve.gds"
all.equal(ve2, ve3)
## [1] TRUE

8 Session Info

sessionInfo()
## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] GDSArray_1.17.0             DelayedArray_0.23.0        
##  [3] Matrix_1.4-1                gdsfmt_1.33.0              
##  [5] VariantExperiment_1.11.0    SummarizedExperiment_1.27.0
##  [7] Biobase_2.57.0              GenomicRanges_1.49.0       
##  [9] GenomeInfoDb_1.33.0         IRanges_2.31.0             
## [11] MatrixGenerics_1.9.0        matrixStats_0.62.0         
## [13] S4Vectors_0.35.0            BiocGenerics_0.43.0        
## [15] BiocStyle_2.25.0           
## 
## loaded via a namespace (and not attached):
##  [1] bslib_0.3.1             compiler_4.2.0          BiocManager_1.30.17    
##  [4] jquerylib_0.1.4         XVector_0.37.0          bitops_1.0-7           
##  [7] tools_4.2.0             zlibbioc_1.43.0         digest_0.6.29          
## [10] jsonlite_1.8.0          evaluate_0.15           lattice_0.20-45        
## [13] rlang_1.0.2             SeqArray_1.37.0         cli_3.3.0              
## [16] parallel_4.2.0          yaml_2.3.5              xfun_0.30              
## [19] fastmap_1.1.0           GenomeInfoDbData_1.2.8  stringr_1.4.0          
## [22] knitr_1.38              Biostrings_2.65.0       sass_0.4.1             
## [25] grid_4.2.0              R6_2.5.1                rmarkdown_2.14         
## [28] bookdown_0.26           magrittr_2.0.3          htmltools_0.5.2        
## [31] SNPRelate_1.31.0        DelayedDataFrame_1.13.0 stringi_1.7.6          
## [34] RCurl_1.98-1.6          crayon_1.5.1