Perform rank invariant set normalization on gene expression values from two experimental conditions or phenotypes
NormDataY
=
mainvarsetnorm(DataX, DataY
)
NormDataY
=
mainvarsetnorm(..., 'Thresholds', ThresholdsValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Exclude', ExcludeValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Percentile', PercentileValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Iterate', IterateValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Method', MethodValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Span', SpanValue
,
...)
NormDataY
=
mainvarsetnorm(..., 'Showplot', ShowplotValue
,
...)
DataX  Vector of gene expression values from a single experimental condition or phenotype, where each row corresponds to a gene. These data points are used as the baseline. 
DataY  Vector of gene expression values from a single experimental condition or phenotype, where each row corresponds to a gene. These data points will be normalized using the baseline. 
ThresholdsValue  Vector that sets the thresholds for the lowest average
rank and the highest average rank between the two data sets. The average
rank for each data point is determined by first converting the values
in Note These individual thresholds are used to determine the rank invariant set, which is a set of data points, each having a proportional rank difference (prd) smaller than its predetermined threshold. For more information on the rank invariant set, see Description.

ExcludeValue  Property to filter the invariant set of data points,
by excluding the data points whose average rank (between 
PercentileValue  Property to stop the iteration process when the number
of data points in the invariant set reaches Note If you do not use this property, the iteration process continues until no more data points are eliminated.

IterateValue  Property to control the iteration process for determining
the invariant set of data points. Enter Tip Select

MethodValue  Property to select the smoothing method used to normalize
the data. Enter 
SpanValue  Property to set the window size for the smoothing method.
If 
ShowplotValue  Property to control the plotting of a pair of MA scatter
plots (before and after normalization). M is the ratio between 
normalizes
the values in NormDataY
=
mainvarsetnorm(DataX, DataY
)DataY
, a vector of gene expression
values, to a reference vector, DataX
, using
the invariant set method. NormDataY
is
a vector of normalized gene expression values from DataY
.
Specifically, mainvarsetnorm
:
Determines the proportional rank difference (prd)
for each pair of ranks, RankX and RankY,
from the two vectors of gene expression values, DataX
and DataY
.
prd = abs(RankX  RankY)
Determines the invariant set of data points by selecting
data points whose proportional rank differences (prd)
are below threshold, which is a predetermined threshold
for a given data point (defined by the ThresholdsValue
property).
It optionally repeats the process until either no more data points
are eliminated, or a predetermined percentage of data points is reached.
The invariant set is data points with a prd < threshold.
Uses the invariant set of data points to calculate
the lowess or running median smoothing curve, which is used to normalize
the data in DataY
.
Note
If DataX
or DataY
contains
NaN values, then NormDataY
will also contain
NaN values at the corresponding positions.
Tip
mainvarsetnorm
is useful for correcting for
dye bias in twocolor microarray data.
calls NormDataY
= mainvarsetnorm(...,
'PropertyName
', PropertyValue
,
...)mainvarsetnorm
with optional
properties that use property name/property value pairs. You can specify
one or more properties in any order. Each PropertyName
must
be enclosed in single quotation marks and is case insensitive. These
property name/property value pairs are as follows:
sets the thresholds for the lowest average rank and
the highest average rank between the two data sets. The average rank
for each data point is determined by first converting the values in NormDataY
=
mainvarsetnorm(..., 'Thresholds', ThresholdsValue
,
...)DataX
and DataY
to
ranks, then averaging the two ranks for each data point. Then, the
threshold for each data point is determined by interpolating between
the threshold for the lowest average rank and the threshold for the
highest average rank.
Note
These individual thresholds are used to determine the rank invariant set, which is a set of data points, each having a proportional rank difference (prd) smaller than its predetermined threshold. For more information on the rank invariant set, see Description.
ThresholdsValue
is a 1by2 vector
[LT, HT
], where LT
is
the threshold for the lowest average rank and HT
is
threshold for the highest average rank. Select these two thresholds
empirically to limit the spread of the invariant set, but allow enough
data points to determine the normalization relationship. Values must
be between 0
and 1
. Default
is [0.03, 0.07
].
filters the invariant set of data points, by excluding
the data points whose average rank (between NormDataY
=
mainvarsetnorm(..., 'Exclude', ExcludeValue
,
...)DataX
and DataY
)
is in the highest N
ranked averages or
lowest N
ranked averages.
stops the iteration process when the number of data
points in the invariant set reaches NormDataY
=
mainvarsetnorm(..., 'Percentile', PercentileValue
,
...)N
percent
of the total number of input data points. Default is 1
.
Note
If you do not use this property, the iteration process continues until no more data points are eliminated.
controls the iteration process for determining the
invariant set of data points. When NormDataY
=
mainvarsetnorm(..., 'Iterate', IterateValue
,
...)IterateValue
is true
, mainvarsetnorm
repeats
the process until either no more data points are eliminated, or a
predetermined percentage of data points (PercentileValue
)
is reached. When IterateValue
is false
,
performs only one iteration of the process. Default is true
.
Tip
Select false
for smaller data sets, typically
less than 200 data points.
selects the smoothing method for normalizing the data.
When NormDataY
=
mainvarsetnorm(..., 'Method', MethodValue
,
...)MethodValue
is 'lowess'
, mainvarsetnorm
uses
the lowess method. When MethodValue
is
'runmedian'
, mainvarsetnorm
uses
the running median method. Default is 'lowess'
.
sets the window size for the smoothing method. If NormDataY
=
mainvarsetnorm(..., 'Span', SpanValue
,
...)SpanValue
is
less than 1, the window size is that percentage of the number of data
points. If SpanValue
is equal to or greater
than 1, the window size is of size SpanValue
.
Default is 0.05
, which corresponds to a window
size equal to 5% of the total number of data points in the invariant
set.
determines whether to plot a pair of MA scatter plots
(before and after normalization). M is the ratio between NormDataY
=
mainvarsetnorm(..., 'Showplot', ShowplotValue
,
...)DataX
and DataY
.
A is the average of DataX
and DataY
.
When ShowplotValue
is true
, mainvarsetnorm
plots
the MA scatter plots. Default is false
.
[1] Tseng, G.C., Oh, MinKyu, Rohlin, L., Liao, J.C., and Wong, W.H. (2001) Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research. 29, 25492557.
[2] Hoffmann, R., Seidl, T., and Dugas, M. (2002) Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biology. 3(7): research 0033.10033.11.