Examples of qscore objects (quality scores) to apply to CGH arrays

Description

This data set provides qscore objects that can be applied to normalized arrayCGH objects in order to evaluate data quality after normalization.

Usage

data(qscores)

Format

The following qscore objects are provided:

Note

People interested in tools for array-CGH analysis can visit our web-page: http://bioinfo.curie.fr.

Author(s)

Pierre Neuvial, manor@curie.fr.

Source

Institut Curie, manor@curie.fr.

See Also

spatial, qscore.summary.arrayCGH, qscore

Examples

data(qscores)
data(spatial)

## define a list of qscores
qscore.list <- list(clone=clone.qscore, pct.clone=pct.clone.qscore,
pct.spot=pct.spot.qscore, pct.replicate=pct.replicate.qscore,
smoothness=smoothness.qscore, dyn.x=dyn.x.qscore, dyn.y=dyn.y.qscore,
var.replicate=var.replicate.qscore)

## compute quality scores for a couple of normalized arrays
gradient.norm$quality <- qscore.summary.arrayCGH(gradient.norm,
qscore.list)
print(gradient.norm$quality[, 2:3])

qscore.list$dyn.x$args$test <- 23
qscore.list$dyn.y$args$test <- 24
edge.norm$quality <- qscore.summary.arrayCGH(edge.norm, qscore.list)
print(edge.norm$quality[, 2:3])

Results

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(MANOR)
Loading required package: GLAD

######################################################################################

Have fun with GLAD

For smoothing it is possible to use either
the AWS algorithm (Polzehl and Spokoiny, 2002,
or the HaarSeg algorithm (Ben-Yaacov and Eldar, Bioinformatics,  2008,

If you use the package with AWS, please cite:
Hupe et al. (Bioinformatics, 2004, and Polzehl and Spokoiny (2002,

If you use the package with HaarSeg, please cite:
Hupe et al. (Bioinformatics, 2004, and (Ben-Yaacov and Eldar, Bioinformatics, 2008,

For fast computation it is recommanded to use
the daglad function with smoothfunc=haarseg

######################################################################################

New options are available in daglad: see help for details.


Attaching package: 'MANOR'

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

    norm

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MANOR/qscores.Rd_%03d_medium.png", width=480, height=480)
> ### Name: qscores
> ### Title: Examples of qscore objects (quality scores) to apply to CGH
> ###   arrays
> ### Aliases: clone.qscore pct.clone.qscore pct.spot.qscore
> ###   pct.spot.before.qscore pct.replicate.qscore smoothness.qscore
> ###   dynamics.qscore var.replicate.qscore dyn.x.qscore dyn.y.qscore
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(qscores)
> data(spatial)
> 
> ## define a list of qscores
> qscore.list <- list(clone=clone.qscore, pct.clone=pct.clone.qscore,
+ pct.spot=pct.spot.qscore, pct.replicate=pct.replicate.qscore,
+ smoothness=smoothness.qscore, dyn.x=dyn.x.qscore, dyn.y=dyn.y.qscore,
+ var.replicate=var.replicate.qscore)
> 
> ## compute quality scores for a couple of normalized arrays
> gradient.norm$quality <- qscore.summary.arrayCGH(gradient.norm,
+ qscore.list)
> print(gradient.norm$quality[, 2:3])
                                                               label    score
1                               Number of clones after normalization 3227.000
2                           Proportion of clones after normalization   96.600
3                            Proportion of spots after normalization   85.300
4 Average proportion of remaining spots by clone after normalization   91.900
5                          Local signal variability along the genome    0.033
6                                    Signal dynamics on X chromosome    1.233
7                                    Signal dynamics on Y chromosome    0.328
8                               Average variability among replicates    0.050
> 
> qscore.list$dyn.x$args$test <- 23
> qscore.list$dyn.y$args$test <- 24
> edge.norm$quality <- qscore.summary.arrayCGH(edge.norm, qscore.list)
> print(edge.norm$quality[, 2:3])
                                                               label    score
1                               Number of clones after normalization 2364.000
2                           Proportion of clones after normalization  100.000
3                            Proportion of spots after normalization   95.900
4 Average proportion of remaining spots by clone after normalization   96.300
5                          Local signal variability along the genome    0.021
6                                    Signal dynamics on X chromosome    0.959
7                                    Signal dynamics on Y chromosome    0.990
8                               Average variability among replicates    0.010
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>