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Last data update: 2014.03.03

R: Generates summaries on the specified windows

stat_aggregate

R Documentation

Generates summaries on the specified windows

Description

Generates summaries on the specified windows

Usage


## S4 method for signature 'GRanges'
stat_aggregate(data, ..., xlab, ylab, main, by, FUN,
                          maxgap=0L, minoverlap=1L,
                          type=c("any", "start", "end", "within", "equal"),
                          select=c("all", "first", "last", "arbitrary"),
                          y = NULL, window = NULL, facets = NULL, 
                          method = c("mean", "median","max",
                                   "min", "sum", "count", "identity"),
                          geom = NULL)

Arguments

`data`	A `GRanges` or `data.frame` object.
`...`	Arguments passed to plot function. such as aes() and color.
`xlab`	Label for x
`ylab`	Label for y
`main`	Title for plot.
`by`	An object with 'start', 'end', and 'width' methods. Passed to `aggreagate`.
`FUN`	The function, found via 'match.fun', to be applied to each window of 'x'. Passed to `aggreagate`.
`maxgap, minoverlap`	It passed to `findOverlaps`. Intervals with a separation of `maxgap` or less and a minimum of `minoverlap` overlapping positions, allowing for `maxgap`, are considered to be overlapping. `maxgap` should be a scalar, non-negative, integer. `minoverlap` should be a scalar, positive integer.
`type`	It passed to `findOverlaps`. By default, any overlap is accepted. By specifying the `type` parameter, one can select for specific types of overlap. The types correspond to operations in Allen's Interval Algebra (see references). If `type` is `start` or `end`, the intervals are required to have matching starts or ends, respectively. While this operation seems trivial, the naive implementation using `outer` would be much less efficient. Specifying `equal` as the type returns the intersection of the `start` and `end` matches. If `type` is `within`, the query interval must be wholly contained within the subject interval. Note that all matches must additionally satisfy the `minoverlap` constraint described above. The `maxgap` parameter has special meaning with the special overlap types. For `start`, `end`, and `equal`, it specifies the maximum difference in the starts, ends or both, respectively. For `within`, it is the maximum amount by which the query may be wider than the subject.
`select`	It passed to `findOverlaps`. When `select` is `"all"` (the default), the results are returned as a Hits object. When `select` is `"first"`, `"last"`, or `"arbitrary"` the results are returned as an integer vector of length `query` containing the first, last, or arbitrary overlapping interval in `subject`, with `NA` indicating intervals that did not overlap any intervals in `subject`. If `select` is `"all"`, a Hits object is returned. For all other `select` the return value depends on the `drop` argument. When `select != "all" && !drop`, an IntegerList is returned, where each element of the result corresponds to a space in `query`. When`select != "all" && drop`, an integer vector is returned containing indices that are offset to align with the unlisted `query`.
`y`	A character indicate the varialbe column for which aggregation is taken on, same as aes(y = ).
`window`	Integer value indicate window size.
`facets`	Faceting formula to use.
`method`	customized method for aggregating, if FUN is not provided.
`geom`	The geometric object to use display the data.

Value

A 'Layer'.

Author(s)

Tengfei Yin

Examples

library(GenomicRanges)
set.seed(1)
N <- 1000
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames = 
              sample(c("chr1", "chr2", "chr3"),
                     size = N, replace = TRUE),
              IRanges(
                      start = sample(1:300, size = N, replace = TRUE),
                      width = sample(70:75, size = N,replace = TRUE)),
              strand = sample(c("+", "-", "*"), size = N, 
                replace = TRUE),
              value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
              sample = sample(c("Normal", "Tumor"), 
                size = N, replace = TRUE),
              pair = sample(letters, size = N, 
                replace = TRUE))


ggplot(gr) + stat_aggregate(aes(y = value))
## or
## ggplot(gr) + stat_aggregate(y = "value")
ggplot(gr) + stat_aggregate(aes(y = value), window = 36)
ggplot(gr) + stat_aggregate(aes(y = value), select = "first")
## Not run: 
## no hits 
ggplot(gr) + stat_aggregate(aes(y = value), select = "first", type = "within")

## End(Not run)
ggplot(gr) + stat_aggregate(window = 30,  aes(y = value),fill = "gray40", geom = "bar")
ggplot(gr) + stat_aggregate(window = 100, fill = "gray40", aes(y = value),
                           method = "max", geom = "bar")

ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot")
ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 60)
## now facets need to take place inside stat_* geom_* for an accurate computation
ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 30,
              facets = sample ~ seqnames)
## FIXME:
## autoplot(gr, stat = "aggregate", aes(y = value), window = 36)
## autoplot(gr, stat = "aggregate", geom = "boxplot", aes(y = value), window = 36)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> library(ggbio)
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: ggplot2
Need specific help about ggbio? try mailing 
 the maintainer or visit http://tengfei.github.com/ggbio/

Attaching package: 'ggbio'

The following objects are masked from 'package:ggplot2':

    geom_bar, geom_rect, geom_segment, ggsave, stat_bin, stat_identity,
    xlim

Warning message:
replacing previous import 'ggplot2::Position' by 'BiocGenerics::Position' when loading 'ggbio' 
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ggbio/stat_aggregate-method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: stat_aggregate
> ### Title: Generates summaries on the specified windows
> ### Aliases: stat_aggregate stat_aggregate,GRanges-method
> ###   stat_aggregate,missing-method stat_aggregate,uneval-method
> 
> ### ** Examples
> 
> library(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
> set.seed(1)
> N <- 1000
> ## ======================================================================
> ##  simmulated GRanges
> ## ======================================================================
> gr <- GRanges(seqnames = 
+               sample(c("chr1", "chr2", "chr3"),
+                      size = N, replace = TRUE),
+               IRanges(
+                       start = sample(1:300, size = N, replace = TRUE),
+                       width = sample(70:75, size = N,replace = TRUE)),
+               strand = sample(c("+", "-", "*"), size = N, 
+                 replace = TRUE),
+               value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
+               sample = sample(c("Normal", "Tumor"), 
+                 size = N, replace = TRUE),
+               pair = sample(letters, size = N, 
+                 replace = TRUE))
> 
> 
> ggplot(gr) + stat_aggregate(aes(y = value))
> ## or
> ## ggplot(gr) + stat_aggregate(y = "value")
> ggplot(gr) + stat_aggregate(aes(y = value), window = 36)
> ggplot(gr) + stat_aggregate(aes(y = value), select = "first")
> ## Not run: 
> ##D ## no hits 
> ##D ggplot(gr) + stat_aggregate(aes(y = value), select = "first", type = "within")
> ## End(Not run)
> ggplot(gr) + stat_aggregate(window = 30,  aes(y = value),fill = "gray40", geom = "bar")
> ggplot(gr) + stat_aggregate(window = 100, fill = "gray40", aes(y = value),
+                            method = "max", geom = "bar")
> 
> ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot")
> ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 60)
> ## now facets need to take place inside stat_* geom_* for an accurate computation
> ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 30,
+               facets = sample ~ seqnames)
> ## FIXME:
> ## autoplot(gr, stat = "aggregate", aes(y = value), window = 36)
> ## autoplot(gr, stat = "aggregate", geom = "boxplot", aes(y = value), window = 36)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>