R/BioC序列处理之五:Rle和Ranges
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(本文已于2015.09.08更新)
生物序列信息不仅仅指序列本身,它们还包括其他类型的信息,如基因都定位在哪些序列(染色体)上,正链还是负链,什么位置,其他数据库对应的编号是什么,有什么功能等等。下面介绍BioC中用于这些数据存储和处理的Rle和Ranges类。
1 Rle(Run Length Encoding,行程编码)
1.1 Rle类和Rle对象
序列或基因最终要定位到染色体上。序列往往数量非常巨大,但染色体数量很少,如果每条序列的染色体定位都显式标注,将会产生大量的重复信息,更糟糕的是它们要占用大量的内存。BioC的IRanges包为这些数据提供了一种简便可行的信息压缩方式,即Rle。如果染色体1-3分别有3000,5000和2000个基因,基因的染色体注释可以用字符向量表示,也可以用Rle对象表示:
library(IRanges) ##可以不执行,载入Biostrings包将自动载入依赖包IRangeslibrary(Biostrings)chr.str <- c(rep("ChrI", 3000), rep("ChrII", 5000), rep("ChrIII", 2000))chr.rle <- Rle(chr.str)
两种方式的效果是完全一样的,但是Rle对象占用空间还不到字符向量的2%:
## Rle对象向量化后和原向量是完全相同的:identical(as.vector(chr.rle), chr.str)
## [1] TRUE
## 对象大小(内存占用)比:as.vector(object.size(chr.rle)/object.size(chr.str))
## [1] 0.01616283
使用Rle并不总是可以“压缩”数据。如果信息没有重复或重复量很少,Rle会占用更多的内存:
strx <- sample(DNA_BASES, 10000, replace = TRUE)strx.rle <- Rle(strx)as.vector(object.size(strx.rle)/object.size(strx))
## [1] 1.130721
Rle对象用两个属性来表示原向量,一个是值(values),可以是向量或因子;另一个是长度(lengths),为整型数据,表示对应位置的value的重复次数。
chr.rle
## character-Rle of length 10000 with 3 runs## Lengths: 3000 5000 2000## Values : "ChrI" "ChrII" "ChrIII"
getClass(class(chr.rle))
## Class "Rle" [package "S4Vectors"]## ## Slots:## ## Name: values lengths elementMetadata metadata## Class: vectorORfactor integer DataTableORNULL list## ## Extends: ## Class "Vector", directly## Class "Annotated", by class "Vector", distance 2
1.2 Rle对象的处理方法
1.2.1 Rle对象构建/获取:
Rle对象可以用构造函数Rle来产生,它有两种用法:
Rle(values)Rle(values, lengths)
values和lengths均为(原子)向量。第一种用法前面已经出现过了,我们看看第二种用法:
chr.rle <- Rle(values = c("Chr1", "Chr2", "Chr3", "Chr1", "Chr3"), lengths = c(3, 2, 5, 4, 5))chr.rle
## character-Rle of length 19 with 5 runs## Lengths: 3 2 5 4 5## Values : "Chr1" "Chr2" "Chr3" "Chr1" "Chr3"
原子向量也可以通过类型转换函数as由原子向量产生,它等价于上面的第一种方式:
as(chr.str, "Rle")
## character-Rle of length 10000 with 3 runs## Lengths: 3000 5000 2000## Values : "ChrI" "ChrII" "ChrIII"
1.2.2 获取属性:
Rle是S4类,Rle对象的属性如值、长度等可以使用属性读取函数获取:
runLength(chr.rle)
## [1] 3 2 5 4 5
runValue(chr.rle)
## [1] "Chr1" "Chr2" "Chr3" "Chr1" "Chr3"
nrun(chr.rle)
## [1] 5
start(chr.rle)
## [1] 1 4 6 11 15
end(chr.rle)
## [1] 3 5 10 14 19
width(chr.rle)
## [1] 3 2 5 4 5
1.2.3 属性替换:
Rle对象的长度和值还可以使用属性替换函数进行修改:
runLength(chr.rle) <- rep(3, nrun(chr.rle))chr.rle
## character-Rle of length 15 with 5 runs## Lengths: 3 3 3 3 3## Values : "Chr1" "Chr2" "Chr3" "Chr1" "Chr3"
runValue(chr.rle)[3:4] <- c("III", "IV")chr.rle
## character-Rle of length 15 with 5 runs## Lengths: 3 3 3 3 3## Values : "Chr1" "Chr2" "III" "IV" "Chr3"
## 替换向量和被替换向量的长度必需相同,否则出错。下面两个语句都不正确:runValue(chr.rle) <- c("ChrI", "ChrV")
## Error in .Call2("Rle_constructor", values, lengths, check, 0L, PACKAGE = "S4Vectors"): 'length(lengths)' != 'length(values)'
runLength(chr.rle) <- 3
## Error in .Call2("Rle_constructor", values, lengths, check, 0L, PACKAGE = "S4Vectors"): 'length(lengths)' != 'length(values)'
1.2.4 类型转换:
除使用as.vector函数外,Rle对象还可以使用很多函数进行类型转换,如:
as.factor(chr.rle)
## [1] Chr1 Chr1 Chr1 Chr2 Chr2 Chr2 III III III IV IV IV Chr3 Chr3## [15] Chr3## Levels: Chr1 Chr2 Chr3 III IV
as.character(chr.rle)
## [1] "Chr1" "Chr1" "Chr1" "Chr2" "Chr2" "Chr2" "III" "III" "III" "IV" ## [11] "IV" "IV" "Chr3" "Chr3" "Chr3"
1.2.5 Rle的S4类集团泛函数运算
Rle是BioC定义的基础数据类型。既然“基础”,那么它应当能进行R语言中数据的一般性运算,比如加减乘除、求模、求余等数学运算。事实也是如此,Rle支持R语言S4类集团泛函数(group generic functions,“集团通用函数”?)运算,包括算术、复数、比较、逻辑、数学函数和R语言的汇总("max", "min", "range", "prod", "sum", "any", "all"等)运算(没有去验证是否所有运算都已实现)。下面仅简单具几个例子,具体情况请参考Rle-class的相关说明:
set.seed(0)rle1 <- Rle(sample(4, 6, replace = TRUE))rle2 <- Rle(sample(5, 12, replace = TRUE))rle3 <- Rle(sample(4, 8, replace = TRUE))rle1 + rle2
## integer-Rle of length 12 with 11 runs## Lengths: 1 1 1 1 1 1 1 1 1 2 1## Values : 9 7 6 7 5 3 5 6 4 7 5
rle1 + rle3
## integer-Rle of length 8 with 8 runs## Lengths: 1 1 1 1 1 1 1 1## Values : 8 4 6 7 5 4 5 4
rle1 * rle2
## integer-Rle of length 12 with 11 runs## Lengths: 1 1 1 1 1 1 1 1 1 2 1## Values : 20 10 8 12 4 2 4 8 4 12 4
sqrt(rle1)
## numeric-Rle of length 6 with 5 runs## Lengths: 1 2 ... 1## Values : 2 1.4142135623731 ... 1
range(rle1)
## [1] 1 4
cumsum(rle1)
## integer-Rle of length 6 with 6 runs## Lengths: 1 1 1 1 1 1## Values : 4 6 8 11 15 16
(rle1 <- Rle(sample(DNA_BASES, 10, replace = TRUE)))
## character-Rle of length 10 with 9 runs## Lengths: 1 1 1 1 2 1 1 1 1## Values : "C" "A" "C" "T" "C" "G" "C" "A" "T"
(rle2 <- Rle(sample(DNA_BASES, 8, replace = TRUE)))
## character-Rle of length 8 with 8 runs## Lengths: 1 1 1 1 1 1 1 1## Values : "G" "T" "A" "G" "C" "T" "G" "T"
paste(rle1, rle2, sep = "")
## character-Rle of length 10 with 10 runs## Lengths: 1 1 1 1 1 1 1 1 1 1## Values : "CG" "AT" "CA" "TG" "CC" "CT" "GG" "CT" "AG" "TT"
2 Ranges(序列区间/范围)
2.1 BioC中的Ranges
Ranges是一类特殊但又常用的数据类型,它们可以表示小段序列在大段序列中的位置、名称和组织结构等信息。BioC中与Ranges定义有关的软件包主要有IRanges, GenomicRanges和GenomicFeatures。<br>IRanges包定义了Ranges的一般数据结构和处理方法,但不直接面向序列处理;GenomicRanges包定义的GRanges和GRangesList类除了储存Ranges信息外还包含了序列的名称和DNA链等信息;而GenomicFeatures(包)则处理以数据库形式提供的GRanges信息,如基因、外显子、内含子、启动子、UTR等。<br>先看看BioC中Ranges最基本的类定义:
getClass("Ranges")
## Virtual Class "Ranges" [package "IRanges"]## ## Slots:## ## Name: elementType elementMetadata metadata## Class: character DataTableORNULL list## ## Extends: ## Class "IntegerList", directly## Class "RangesORmissing", directly## Class "AtomicList", by class "IntegerList", distance 2## Class "List", by class "IntegerList", distance 3## Class "Vector", by class "IntegerList", distance 4## Class "Annotated", by class "IntegerList", distance 5## ## Known Subclasses: ## Class "IRanges", directly## Class "Partitioning", directly## Class "GappedRanges", directly## Class "NCList", directly## Class "IntervalTree", directly## Class "NormalIRanges", by class "IRanges", distance 2## Class "PartitioningByEnd", by class "Partitioning", distance 2## Class "PartitioningByWidth", by class "Partitioning", distance 2## Class "PartitioningMap", by class "Partitioning", distance 3
Ranges是虚拟类,实际应用中最常用的IRanges子类,它继承了Ranges的数据结构,另外多设置了3个slots(存储槽),分别用于存贮Ranges的起点、宽度和名称信息。由于Ranges由整数确定,所以称为IRanges(Integer Ranges,整数区间),但也有人理解成间隔区间(Interval Ranges):
getSlots("Ranges")
## elementType elementMetadata metadata ## "character" "DataTableORNULL" "list"
getSlots("IRanges")
## start width NAMES elementType ## "integer" "integer" "characterORNULL" "character" ## elementMetadata metadata ## "DataTableORNULL" "list"
GRanges是Ranges概念在序列处理上的具体应用,但它和IRanges没有继承关系:
library(GenomicRanges)getSlots("GRanges")
## seqnames ranges strand elementMetadata ## "Rle" "IRanges" "Rle" "DataFrame" ## seqinfo metadata ## "Seqinfo" "list"
Ranges对于序列处理非常重要,除GenomicRanges外,Biostrings一些类的定义也应用了Ranges:
getSlots("XStringViews")
## subject ranges elementType elementMetadata ## "XString" "IRanges" "character" "DataTableORNULL" ## metadata ## "list"
2.2 对象构建和属性获取
IRanges对象可以使用对象构造函数IRanges产生,需提供起点(start)、终点(end)和宽度(width)三个参数中的任意两个:
ir1 <- IRanges(start = 1:10, width = 10:1)ir2 <- IRanges(start = 1:10, end = 11)ir3 <- IRanges(end = 11, width = 10:1)ir1
## IRanges of length 10## start end width## [1] 1 10 10## [2] 2 10 9## [3] 3 10 8## [4] 4 10 7## [5] 5 10 6## [6] 6 10 5## [7] 7 10 4## [8] 8 10 3## [9] 9 10 2## [10] 10 10 1
GRanges对象也可以使用构造函数生成,其方式与数据框对象生成有些类似:
genes <- GRanges(seqnames = c("Chr1", "Chr3", "Chr3"), ranges = IRanges(start = c(1300, 1050, 2000), end = c(2500, 1870, 3200)), strand = c("+", "+", "-"), seqlengths = c(Chr1 = 1e+05, Chr3 = 2e+05))genes
## GRanges object with 3 ranges and 0 metadata columns:## seqnames ranges strand## <Rle> <IRanges> <Rle>## [1] Chr1 [1300, 2500] +## [2] Chr3 [1050, 1870] +## [3] Chr3 [2000, 3200] -## -------## seqinfo: 2 sequences from an unspecified genome
IRanges和GRanges都是S4类,其属性获取有相应的函数:
start(ir1)
## [1] 1 2 3 4 5 6 7 8 9 10
end(ir1)
## [1] 10 10 10 10 10 10 10 10 10 10
width(ir1)
## [1] 10 9 8 7 6 5 4 3 2 1
ranges(genes)
## IRanges of length 3## start end width## [1] 1300 2500 1201## [2] 1050 1870 821## [3] 2000 3200 1201
start(ranges(genes))
## [1] 1300 1050 2000
Views对象也包含有IRanges属性:
## 按长度设置产生随机序列的函数rndSeq <- function(dict, n) { paste(sample(dict, n, replace = T), collapse = "")}set.seed(0)dna <- DNAString(rndSeq(DNA_BASES, 1000))vws <- as(maskMotif(dna, "TGA"), "Views")(ir <- ranges(vws))
## IRanges of length 18## start end width## [1] 1 104 104## [2] 108 264 157## [3] 268 268 1## [4] 272 300 29## [5] 304 393 90## ... ... ... ...## [14] 586 752 167## [15] 756 851 96## [16] 855 912 58## [17] 916 989 74## [18] 993 1000 8
模式匹配的match类函数返回IRanges对象,而vmatch类函数返回GRanges类对象:
2.3 IRanges对象的运算和处理方法
2.3.1 Ranges内变换(Intra-range transformations)
这种类型的处理函数包括shift,flank,narrow,reflect,resize,restrict和promoters等,它们对每个Ranges进行独立处理。为了方便理解,我们使用IRanges包的Vignette提供的一个很有用的IRanges作图函数(稍做修改):
plotRanges <- function(x, xlim = x, main = deparse(substitute(x)), col = "black", add = FALSE, ybottom = NULL, ...) { require(scales) col <- alpha(col, 0.5) height <- 1 sep <- 0.5 if (is(xlim, "Ranges")) { xlim <- c(min(start(xlim)), max(end(xlim)) * 1.2) } if (!add) { bins <- disjointBins(IRanges(start(x), end(x) + 1)) ybottom <- bins * (sep + height) - height par(mar = c(3, 0.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot.new() plot.window(xlim, c(0, max(bins) * (height + sep))) } rect(start(x) - 0.5, ybottom, end(x) + 0.5, ybottom + height, col = col, ...) text((start(x) + end(x))/2, ybottom + height/2, 1:length(x), col = "white", xpd = TRUE) title(main) axis(1) invisible(ybottom)}
shift函数对Ranges进行平移(下面图形中蓝色为原始Ranges,红色为变换后的Ranges,黑色/灰色则为参考Ranges,其他颜色为重叠区域):
ir <- IRanges(c(3000, 2500), width = c(300, 850))ir.trans <- shift(ir, 500)xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "shift", col = "blue")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")
flank函数获取Ranges的相邻区域,width参数为整数表示左侧,负数表示右侧:
ir.trans <- flank(ir, width = 200)xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "flank", col = "blue")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")
reflect函数获取Ranges的镜面对称区域,bounds为用于设置镜面位置的Ranges对象:
bounds <- IRanges(c(2000, 3000), width = 500)ir.trans <- reflect(ir, bounds = bounds)xlim <- c(0, max(end(ir, ir.trans, bounds)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "reflect", col = "blue")plotRanges(bounds, add = TRUE, ybottom = ybottom, main = "")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")
promoters函数获取promoter区域,upstream和downstream分别设置上游和下游截取的序列长度:
ir.trans <- promoters(ir, upstream = 1000, downstream = 100)xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "promoters", col = "blue")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")
resize函数改变Ranges的大小,width设置宽度,fix设置固定位置(start/end/center):
ir.trans <- resize(ir, width = c(100, 1300), fix = "start")xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "resize, fix=\"start\"", col = "blue")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")ir.trans <- resize(ir, width = c(100, 1300), fix = "center")xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, main = "resize, fix=\"center\"", col = "blue")plotRanges(ir.trans, add = TRUE, ybottom = ybottom, main = "", col = "red")
其他函数的使用请自行尝试使用。
2.3.2 Ranges间转换(Inter-range transformations)
range函数用于获取Ranges所包括的整个区域(包括间隔区);reduce将重叠区域合并;gaps用于获取间隔区域:
ir <- IRanges(c(200, 1000, 3000, 2500), width = c(600, 1000, 300, 850))ir.trans <- range(ir)xlim <- c(0, max(end(ir, ir.trans)) * 1.3)ybottom <- plotRanges(ir, xlim = xlim, col = "blue")plotRanges(ir.trans, xlim = xlim, col = "red", main = "range")ir.trans <- reduce(ir)plotRanges(ir.trans, xlim = xlim, col = "red", main = "reduce")ir.trans <- gaps(ir)plotRanges(ir.trans, xlim = xlim, col = "red", main = "gaps")
2.3.3 Ranges对象间的集合运算
intersect求交集区域;setdiff求差异区域;union求并集:
ir1 <- IRanges(c(200, 1000, 3000, 2500), width = c(600, 1000, 300, 850))ir2 <- IRanges(c(100, 1500, 2000, 3500), width = c(600, 800, 1000, 550))xlim <- c(0, max(end(ir1, ir2)) * 1.3)ybottom <- plotRanges(reduce(ir1), xlim = xlim, col = "blue", main = "original")plotRanges(reduce(ir2), xlim = xlim, col = "blue", main = "", add = TRUE, ybottom = ybottom)plotRanges(intersect(ir1, ir2), xlim = xlim, col = "red")plotRanges(setdiff(ir1, ir2), xlim = xlim, col = "red")plotRanges(union(ir1, ir2), xlim = xlim, col = "red")
此外还有punion,pintersect,psetdiff和pgap函数,进行element-wise的运算。
3 SessionInfo()
sessionInfo()
## R version 3.2.2 (2015-08-14)## Platform: x86_64-pc-linux-gnu (64-bit)## Running under: Debian GNU/Linux 8 (jessie)## ## locale:## [1] LC_CTYPE=zh_CN.utf8 LC_NUMERIC=C ## [3] LC_TIME=zh_CN.utf8 LC_COLLATE=zh_CN.utf8 ## [5] LC_MONETARY=zh_CN.utf8 LC_MESSAGES=zh_CN.utf8 ## [7] LC_PAPER=zh_CN.utf8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=zh_CN.utf8 LC_IDENTIFICATION=C ## ## attached base packages:## [1] stats4 parallel stats graphics grDevices utils datasets ## [8] methods base ## ## other attached packages:## [1] scales_0.3.0 GenomicRanges_1.18.4 GenomeInfoDb_1.2.5 ## [4] Biostrings_2.34.1 XVector_0.6.0 IRanges_2.0.1 ## [7] S4Vectors_0.4.0 BiocGenerics_0.12.1 zblog_0.1.0 ## [10] knitr_1.11 ## ## loaded via a namespace (and not attached):## [1] Rcpp_0.12.0 plyr_1.8.3 formatR_1.2 magrittr_1.5 ## [5] evaluate_0.7.2 highr_0.5 stringi_0.5-5 zlibbioc_1.12.0 ## [9] tools_3.2.2 stringr_1.0.0 munsell_0.4.2 colorspace_1.2-6
Created: 2015-09-08 二 10:46
Emacs 24.4.1 (Org mode 8.2.10)
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