Imputing log2 ratios

Description

Imputing log2 ratios

Usage

impute.lowess(aCGH.obj, chrominfo = human.chrom.info.Jul03, maxChrom =
23, smooth = 0.1)

Arguments

Details

There are two main reasons to impute data. One is that given that imputation is reasonable, one can increase the analytical power and improve results. Another, more practical, is that at the moment many widely used fuctions in R do not support missing values. While procedures such as kNN imputations is widely used for gene expression data, it is more powerful to take advantage of the genomic structure of the array CGH data and use a smoother. Note that we perform only one pass os smoothing. If there still remain missing values, they are imputed by the median on the chromosome or chromosomal arm where applicable,

Value

Computes and returns the imputed log2 ratio matrix of the aCGH object.

See Also

aCGH, impute.HMM.

Examples

datadir <- system.file(package = "aCGH")
datadir <- paste(datadir, "/examples", sep="")

clones.info <-
      read.table(file = file.path(datadir, "clones.info.ex.txt"),
                 header = TRUE, sep = "\t", quote="", comment.char="")
log2.ratios <-
      read.table(file = file.path(datadir, "log2.ratios.ex.txt"),
                 header = TRUE, sep = "\t", quote="", comment.char="")
ex.acgh <- create.aCGH(log2.ratios, clones.info)

## Imputing the log2 ratios 

log2.ratios.imputed(ex.acgh) <- impute.lowess(ex.acgh)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(aCGH)
Loading required package: cluster
Loading required package: survival
Loading required package: multtest
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: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'aCGH'

The following object is masked from 'package:stats':

    heatmap

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/aCGH/impute.lowess.Rd_%03d_medium.png", width=480, height=480)
> ### Name: impute.lowess
> ### Title: Imputing log2 ratios
> ### Aliases: impute.lowess
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> datadir <- system.file(package = "aCGH")
> datadir <- paste(datadir, "/examples", sep="")
> 
> clones.info <-
+       read.table(file = file.path(datadir, "clones.info.ex.txt"),
+                  header = TRUE, sep = "\t", quote="", comment.char="")
> log2.ratios <-
+       read.table(file = file.path(datadir, "log2.ratios.ex.txt"),
+                  header = TRUE, sep = "\t", quote="", comment.char="")
> ex.acgh <- create.aCGH(log2.ratios, clones.info)
> 
> ## Imputing the log2 ratios 
> 
> log2.ratios.imputed(ex.acgh) <- impute.lowess(ex.acgh)
Processing chromosome  1 
Processing chromosome  2 
Processing chromosome  3 
Processing chromosome  4 
Processing chromosome  5 
Processing chromosome  6 
Processing chromosome  7 
Processing chromosome  8 
Processing chromosome  9 
Processing chromosome  10 
Processing chromosome  11 
Processing chromosome  12 
Processing chromosome  13 
Processing chromosome  14 
Processing chromosome  15 
Processing chromosome  16 
Processing chromosome  17 
Processing chromosome  18 
Processing chromosome  19 
Processing chromosome  20 
Processing chromosome  21 
Processing chromosome  22 
Processing chromosome  23 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>