In Bioconductor/DelayedDataFrame: Delayed operation on DataFrame using standard DataFrame metaphor

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Introduction

As the genetic/genomic data are having increasingly larger profile, the annotation file are also getting much bigger than expected. 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. With some newly developed data structure as HDF5 or GDS, and the R interface of DelayedArray to represent on-disk data structures with different back-end in R-user-friendly array data structure (e.g., HDF5Array,GDSArray), the high-throughput genetic/genomic data are now being able to easily loaded and manipulated within R. However, the annotation files for the samples and features inside the high-through data are also getting unexpectedly larger than before. With an ordinary data.frame or DataFrame, it is still getting more and more challenging for any analysis to be done within R. So here we have developed the DelayedDataFrame, which has the very similar characteristics as data.frame and DataFrame. But at the same time, all column data could be optionally saved on-disk (e.g., in DelayedArray structure with any back-end). Common operations like constructing, subsetting, splitting, combining could be done in the same way as DataFrame. This feature of DelayedDataFrame could enable efficient on-disk reading and processing of the large-scale annotation files, and at the same, signicantly saves memory space with common DataFrame metaphor in R and Bioconductor.

Installation

Download the package from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("DelayedDataFrame")

The development version is also available to download through github:

BiocManager::install("Bioconductor/DelayedDataFrame")

Load the package into R session before using:

library(DelayedDataFrame)

DelayedDataFrame class

class extension

DelayedDataFrame extends the DataFrame data structure, with an additional slot called lazyIndex, which saves all the mapping indexes for each column of the data inside DelayedDataFrame. It is similar to data.frame in terms of construction, subsetting, splitting, combining... The rownames are having same feature as DataFrame. It will not be given automatically, but only by explicitly specify in the constructor function DelayedDataFrame(, row.names=...) or using the slot setter function rownames()<-.

Here we use the GDSArray data as example to show the DelayedDataFrame characteristics. GDSArray is a Bioconductor package that represents GDS files as objects derived from the DelayedArray package and DelayedArray class. It carries the on-disk data path and represent the GDS nodes in a DelayedArray-derived data structure.

The GDSArray() constructor takes 2 arguments: the file path and the GDS node name inside the GDS file.

library(GDSArray)
file <- SeqArray::seqExampleFileName("gds")
gdsnodes(file)
varid <- GDSArray(file, "annotation/id")  
DP <- GDSArray(file, "annotation/info/DP")

We use an ordinary character vector and the GDSArray objects to construct a DelayedDataFrame object.

ddf <- DelayedDataFrame(varid, DP)  ## only accommodate 1D GDSArrays with same length

slot accessors

The slots of DelayedDataFrame could be accessed by lazyIndex(), nrow(), rownames() (if not NULL) functions. With a newly constructed DelayedDataFrame object, the initial value of lazyIndex slot will be NULL for all columns.

lazyIndex(ddf)
nrow(ddf)
rownames(ddf)

lazyIndex slot

The lazyIndex slot is in LazyIndex class, which is defined in the DelayedDataFrame package and extends the SimpleList class. The listData slot saves unique indexes for all the columns, and the index slots saves the position of index in listData slot for each column in DelayedDataFrame object. In the above example, with an initial construction of DelayedDataFrame object, the index for each column will all be NULL, and all 3 columns points the NULL values which sits in the first position in listData slot of lazyIndex.

lazyIndex(ddf)@listData
lazyIndex(ddf)@index

Whenever an operation is done (e.g., subsetting), the listData slot inside the DelayedDataFrame stays the same, but the lazyIndex slot will be updated, so that the show method, further statistical calculation will be applied to the subsetting data set. For example, here we subset the DelayedDataFrame object ddf to keep only the first 5 rows, and see how the lazyIndex works. As shown in below, after subsetting, the listData slot in ddf1 stays the same as ddf. But the subsetting operation was recorded in the lazyIndex slot, and the slots of lazyIndex, nrows and rownames (if not NULL) are all updated. So the subsetting operation is kind of delayed.

ddf1 <- ddf[1:20,]
identical(ddf@listData, ddf1@listData)
lazyIndex(ddf1)
nrow(ddf1)

Only when functions like DataFrame(), or as.list(), the lazyIndex will be realized and DelayedDataFrame returned. We will show the realization in the following coercion method section.

DelayedDataFrame methods

The common methods on data.frame or DataFrame are also defined on DelayedDataFrame class, so that they behave similarily on DelayedDataFrame objects.

Coercion methods

Coercion methods between DelayedDataFrame and other data structures are defined. When coercing from ANY to DelayedDataFrame, the lazyIndex slot will be added automatically, with the initial NULL value of indexes for each column.

• From vector

as(letters, "DelayedDataFrame")

• From DataFrame

as(DataFrame(letters), "DelayedDataFrame")

• From list

(a <- as(list(a=1:5, b=6:10), "DelayedDataFrame"))
lazyIndex(a)

When coerce DelayedDataFrame into other data structure, the lazyIndex slot will be realized and the new data structure returned. For example, when DelayedDataFrame is coerced into a DataFrame object, the listData slot will be updated according to the lazyIndex slot.

df1 <- as(ddf1, "DataFrame")
df1@listData
dim(df1)

Subsetting methods

subsetting by [

two-dimensional [ subsetting on DelayedDataFrame objects by integer, character, logical values all work.

• integer subscripts.

ddf[, 1, drop=FALSE]

• character subscripts (column names).

ddf[, "DP", drop=FALSE]

• logical subscripts.

ddf[, c(TRUE,FALSE), drop=FALSE]

When subsetting using [ on an already subsetted DelayedDataFrame object, the lazyIndex, nrows and rownames(if not NULL) slot will be updated.

(a <- ddf1[1:10, 2, drop=FALSE])
lazyIndex(a)
nrow(a)

subsetting by [[

The [[ subsetting will take column subscripts for integer or character values, and return corresponding columns in it's original data format.

ddf[[1]]
ddf[["varid"]]
identical(ddf[[1]], ddf[["varid"]])

rbind/cbind

When doing rbind, the lazyIndex of input arguments will be realized and a new DelayedDataFrame with NULL lazyIndex will be returned.

ddf2 <- ddf[21:40, ]
(ddfrb <- rbind(ddf1, ddf2))
lazyIndex(ddfrb)

cbind of DelayedDataFrame objects will keep all existing lazyIndex of input arguments and carry into the new DelayedDataFrame object.

(ddfcb <- cbind(varid = ddf1[,1, drop=FALSE], DP=ddf1[, 2, drop=FALSE]))
lazyIndex(ddfcb)

sessionInfo

sessionInfo()

Bioconductor/DelayedDataFrame documentation built on Nov. 2, 2024, 7:21 a.m.