The RleDataFrame class serves to hold a collection of Run Length
Encoded vectors (Rle objects) of the same length. For example, it could
be used to hold information along the genome for a number of samples,
such as sequencing coverage, DNA copy number, or GC content. This class
inherits from both DataFrame and SimpleRleList (one of the AtomicVector
types). This means that all of the usual subsetting and applying
functions will work. Also, the AtomicList functions, like mean and
sum, that automatically apply over the list elements will
work. The scalar mathematical AtomicList methods can make this
class behave much like a matrix (see Examples).
New objects can be created with the RleDataFrame constructor:
RleDataFrame(..., row.names=NULL), where ... can be a list
of Rle objects, or one or more individual Rle objects.
Use in Biobase eSet objects
The genoset class defines an annotatedDataFrameFrom
method for DataFrame, which makes it possible to include
DataFrames as assayData elements. The column names for
DataFrame cannot be NULL, which makes it impossible to
use them as assays in SummarizedExperiment at this time.
Row and Column Summaries
These objects will sometimes be in place of a matrix, as in the eSet
example above. It is convenient to have some of the summarization
methods for matrices. Each of these methods takes an RleDataFrame and
returns a single Rle. The time required is similar to that required
for a matrix. For an RleDataFrame x,
rowSums:
Sum across 'rows'.
rowMeans:
Means across 'rows'.
colSums:
Sum each Rle. This is just the sum
method for SimpleRleList.
colSums:
Mean of each Rle. This is just the
mean method for SimpleRleList.
Slots
rownames:
Object of class "characterORNULL"
Names to describe each row of the DataFrame. These may end up
taking more space than your collection of Rle objects, so consider
leaving this NULL.
nrows:
Object of class "integer" Number of rows.
elementType:
Object of class "character" Notes
that elements of the internal list are Rle objects.
elementMetadata:
Object of class
"DataTableORNULL" Metadata on the elements, see DataFrame.
metadata:
Object of class "list" Metadata on
the whole object, see DataFrame.
listData:
Object of class "list" Base list
containing the Rle objects.
Extends
Class "SimpleRleList", directly.
Class "DataFrame", directly.
Methods
as.matrix
signature(x = "RleDataFrame"): Convert to
matrix.
coerce
signature(x = "RleDataFrame"): Convert to other
classes.
colMeans
signature(x = "RleDataFrame"): Mean of each column.
colSums
signature(x = "RleDataFrame"): Sum of each column.
rowMeans
signature(x = "RleDataFrame"): Mean of each 'row'.
rowSums
signature(x = "RleDataFrame"): Sum of each 'row'.
show
signature(object = "RleDataFrame"): Short and
pretty description of an object of this type.
Author(s)
Peter M. Haverty, design suggestion from Michael Lawrence.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(genoset)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: GenomicRanges
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
*** Genoset API Changes ***
The genoset class has transitioned to the
RangedSummarizedExperiment API from the eSet API (e.g. use colnames instead of sampleNames). ***
Attaching package: 'genoset'
The following object is masked from 'package:GenomicRanges':
pos
The following objects are masked from 'package:S4Vectors':
colMeans, colSums, rowMeans, rowSums
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
Warning messages:
1: multiple methods tables found for 'colMeans'
2: multiple methods tables found for 'colSums'
3: multiple methods tables found for 'rowMeans'
4: multiple methods tables found for 'rowSums'
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genoset/RleDataFrame-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: RleDataFrame-class
> ### Title: Class '"RleDataFrame"'
> ### Aliases: RleDataFrame-class as.matrix,RleDataFrame-method
> ### coerce,RleDataFrame,matrix-method colMeans,RleDataFrame-method
> ### colSums,RleDataFrame-method colMeans,DataFrame-method
> ### rowMeans,RleDataFrame-method rowSums,RleDataFrame-method
> ### show,RleDataFrame-method RleDataFrame
> ### Keywords: classes methods
>
> ### ** Examples
>
> showClass("RleDataFrame")
Class "RleDataFrame" [package "genoset"]
Slots:
Name: rownames nrows elementType elementMetadata
Class: characterORNULL integer character DataTableORNULL
Name: metadata listData
Class: list list
Extends:
Class "SimpleRleList", directly
Class "DataFrame", directly
Class "RleList", by class "SimpleRleList", distance 2
Class "SimpleAtomicList", by class "SimpleRleList", distance 2
Class "DataTable", by class "DataFrame", distance 2
Class "AtomicList", by class "SimpleRleList", distance 3
Class "SimpleList", by class "SimpleRleList", distance 3
Class "DataTableORNULL", by class "DataFrame", distance 3
Class "List", by class "DataFrame", distance 3
Class "Vector", by class "DataFrame", distance 4
Class "Annotated", by class "DataFrame", distance 5
>
> ## Constructors
> df = new("RleDataFrame", listData=list(A=Rle(c(NA, 2:3, NA, 5), rep(2,
+ 5)), B=Rle(c(6:7, NA, 8:10),c(3,2,1,2,1,1))), nrows=10L)
>
> df2 = RleDataFrame(list(A=Rle(c(NA, 2:3, NA, 5), rep(2, 5)),
+ B=Rle(c(6:7, NA, 8:10),c(3,2,1,2,1,1))))
>
> df3 = RleDataFrame(A=Rle(c(NA, 2:3, NA, 5), rep(2, 5)), B=Rle(c(6:7,
+ NA, 8:10),c(3,2,1,2,1,1)))
>
> ## AtomicList Methods
> runValue(df)
NumericList of length 2
[["A"]] <NA> 2 3 <NA> 5
[["B"]] 6 7 <NA> 8 9 10
> runLength(df)
IntegerList of length 2
[["A"]] 2 2 2 2 2
[["B"]] 3 2 1 2 1 1
> ranges(df)
IRangesList of length 2
$A
IRanges object with 5 ranges and 0 metadata columns:
start end width
<integer> <integer> <integer>
[1] 1 2 2
[2] 3 4 2
[3] 5 6 2
[4] 7 8 2
[5] 9 10 2
$B
IRanges object with 6 ranges and 0 metadata columns:
start end width
<integer> <integer> <integer>
[1] 1 3 3
[2] 4 5 2
[3] 6 6 1
[4] 7 8 2
[5] 9 9 1
[6] 10 10 1
> mean(df)
A B
NA NA
> sum(df)
A B
NA NA
> df + 5
RleList of length 2
$A
numeric-Rle of length 2 with 1 run
Lengths: 2
Values : NA
$B
numeric-Rle of length 2 with 1 run
Lengths: 2
Values : 11
> log2(df) - 1
RleList of length 2
$A
numeric-Rle of length 10 with 5 runs
Lengths: 2 2 ... 2
Values : NA 0 ... 1.32192809488736
$B
numeric-Rle of length 10 with 6 runs
Lengths: 3 2 ... 1
Values : 1.58496250072116 1.8073549220576 ... 2.32192809488736
>
> ## Row and Column Summaries
> rowSums(df)
numeric-Rle of length 10 with 7 runs
Lengths: 2 1 1 1 3 1 1
Values : NA 8 9 10 NA 14 15
> colSums(df)
A B
20 67
> rowMeans(df)
numeric-Rle of length 10 with 7 runs
Lengths: 2 1 1 1 3 1 1
Values : NA 4 4.5 5 NA 7 7.5
> colMeans(df)
A B
3.333333 7.444444
>
> ## Coercion
> as(df, "matrix")
A B
[1,] NA 6
[2,] NA 6
[3,] 2 6
[4,] 2 7
[5,] 3 7
[6,] 3 NA
[7,] NA 8
[8,] NA 8
[9,] 5 9
[10,] 5 10
> as(df, "list")
$A
numeric-Rle of length 10 with 5 runs
Lengths: 2 2 2 2 2
Values : NA 2 3 NA 5
$B
integer-Rle of length 10 with 6 runs
Lengths: 3 2 1 2 1 1
Values : 6 7 NA 8 9 10
> as(df, "RleList")
RleList of length 2
$A
numeric-Rle of length 10 with 5 runs
Lengths: 2 2 2 2 2
Values : NA 2 3 NA 5
$B
integer-Rle of length 10 with 6 runs
Lengths: 3 2 1 2 1 1
Values : 6 7 NA 8 9 10
> as(df, "DataFrame")
DataFrame with 10 rows and 2 columns
A B
<Rle> <Rle>
1 NA 6
2 NA 6
3 2 6
4 2 7
5 3 7
6 3 NA
7 NA 8
8 NA 8
9 5 9
10 5 10
> as(df, "data.frame")
A B
1 NA 6
2 NA 6
3 2 6
4 2 7
5 3 7
6 3 NA
7 NA 8
8 NA 8
9 5 9
10 5 10
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.