qscores {MANOR} | R Documentation |
This data set provides qscore
objects that can be
applied to normalized arrayCGH
objects in order to
evaluate data quality after normalization.
data(qscores)
The following qscore
objects are provided:
clone.qscore | number of clones | |
pct.clone.qscore | percentage of clones | |
pct.spot.qscore | percentage of spots | |
pct.spot.before.qscore | percentage of spots before normalization | |
pct.replicate.qscore | average percentage of replicates | |
smoothness.qscore | signal smoothness | |
var.replicate.qscore | ||
dyn.x.qscore | signal dynamics on X chromosome | |
dyn.y.qscore | signal dynamics on Y chromosome |
People interested in tools for array-CGH analysis can visit our web-page: http://bioinfo.curie.fr.
Pierre Neuvial, manor@curie.fr.
Institut Curie, manor@curie.fr.
spatial
, qscore.summary.arrayCGH
,
qscore
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])