1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NanoStringConvenience.R
\docType{methods}
\name{preprocRccSet,RccSet-method}
\alias{preprocRccSet,RccSet-method}
\alias{preprocRccSet}
\title{Preprocess an RccSet}
\usage{
\S4method{preprocRccSet}{RccSet}(rccSet, doPosCtrlNorm = TRUE,
doBackground = TRUE, doPresAbs = TRUE, doContentNorm = TRUE,
pcnSummaryFunction = "sum", bgReference = c("both", "blanks",
"negatives"), bgSummaryFunction = "median", bgStringency = 1,
nSolverBackground.w1 = 2.18, nSolverBackground.shrink = TRUE,
paStringency = 2, normMethod = c("global", "housekeeping"),
normSummaryFunction = "median", hkgenes = NULL, hkfeatures = NULL,
quietly = FALSE)
}
\arguments{
\item{rccSet}{An RccSet.}
\item{doPosCtrlNorm}{Boolean specifying whether or not to perform positive control normalization.
(`pcd' is short for `posCtrlData', the matrix which gets added to assayData
when this step is performed.)}
\item{doBackground}{Boolean specifying whether or not to perform background correction.}
\item{doPresAbs}{Boolean specifying whether or not the presence/absence call should be
performed. For details, see presAbsCall().}
\item{doContentNorm}{Boolean specifying whether or not content normalization should be performed.}
\item{pcnSummaryFunction}{Function to be used for the positive control normalization (e.g. "mean",
"median", or "sum"). User-defined functions similar to these can be
specified here as well.}
\item{bgReference}{Measurements to use for background estimates: either "blank" (for blank
samples), "negatives" (for negative control probes), or "both". For
details on exactly how the background estimates are computed in each
case, see getBackground().}
\item{bgSummaryFunction}{Summary function for background measurements (e.g. "mean" or "median").
User-defined functions similar to these can be specified here as well.}
\item{bgStringency}{Factor by which deviation (SD or MAD) of the summarization output will be
multiplied to obtain final background estimates.}
\item{nSolverBackground.w1}{Value to use for the 'w1' argument to nSolverBackground(). (Only takes
effect if bgReference == "both"; see getBackground().)}
\item{nSolverBackground.shrink}{Value to use for the 'shrink' argument to nSolverBackground(). (Only takes
effect if bgReference == "both"; see getBackground().)}
\item{paStringency}{Multiplier to use in establishing the presence/absence call. For details,
see presAbsCall().}
\item{normMethod}{Specifies the features to be used for content normalization. "global" indicates that all
features should be used and "housekeeping" indicates that only housekeeping
features should be used. If "housekeeping" is specified and the `hk' argument
(below) is also specified, then the features indicated by `hk' will be used.
If "housekeeping" is specified and `hk' is left NULL, then the default
housekeeping features (i.e. those with CodeClass == "Housekeeping") will be used.}
\item{normSummaryFunction}{Character specifying the summary function to apply to the selected features
(e.g. "mean" or "median") during the content normalization step. User-defined
functions similar to these can be specified here as well.}
\item{hkgenes}{Character vector with gene symbols to be used for content normalization if
housekeeping is specified as the normalization method. If specified, all
features that match any of the specified symbols will be used. (To specify
specific features, use the `hkfeatures' argument instead; see below.)}
\item{hkfeatures}{Character vector with full feature names
("<CodeClass>_<GeneName>_<Accession>", e.g. "Endogenous_ACTG1_NM_001614.1")
to be used for content normalization if housekeeping is specified as the normalization
method. (Note: if this argument is specified at the same time as `hkgenes',
an error will be thrown.)}
\item{quietly}{Boolean specifying whether or not messages and warnings should be omitted.}
}
\value{
A copy of the input RccSet with additional matrices in the assayData for each
successive preprocessing step along with parameters for each step recorded in the
experimentData@preprocessing list.
}
\description{
This function is a wrapper to perform any combination of positive control
normalization, background correction, and content normalization on the
input RccSet. For each completed preprocessing step, a matrix is added to
the assayData of the resulting RccSet object:
\itemize{
\item posCtrlData: expression data after positive control normalization
\item bgEstimates: background estimates
\item bgCorrData: expression data after positive control normalization and
background correction
\item normData: expression data after positive control normalization,
background correction, and content normalization
}
(\bold{NOTE}: normData is on a log2 scale while all the other matrices are
on a linear scale.)
If any step is omitted, the corresponding matrix will not be present in
the output's assayData. The parameters for all steps are recorded in the
output's experimentData@preprocessing list (accessible through
preproc(rccSet) where rccSet is an RccSet output by this function). In
addition:
\itemize{
\item If blanks are not present in the data, use bgReference="negatives"
to prevent the function from throwing an error.
\item If positive control normalization is performed, a column named
'PosCtrl' is added to the output's phenoData to record the
positive control scaling factors.
\item If the presence/absence call is performed, a matrix named `paData'
is added to the output's assayData to indicate the
presence/absence of each feature in each sample. See the `pa'
argument for details.
\item If housekeeping normalization is performed, a column labeled
`Housekeeping' is added to the featureData to indicate which
features were used for it.
}
}
\details{
For more information on the rationale behind the recommended
preprocessing and normalization steps, please see the vignette.
}
\examples{
data(example_rccSet)
hknorm_example_rccSet <- preprocRccSet(example_rccSet)
}
\references{
\href{http://www.nanostring.com/media/pdf/MAN_nCounter_Gene_Expression_Data_Analysis_Guidelines.pdf}{NanoString nCounter(R) Expression Data Analysis Guide (2012)}
}
\author{
Dorothee Nickles, Robert Ziman
}
|
