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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 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.
ThresholdsValue is a 1-by-2 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]. | |
ExcludeValue | Property to filter the invariant set of data points, by excluding the data points whose average rank (between DataX and DataY) is in the highest N ranked averages or lowest N ranked averages. | |
PercentileValue | Property to stop the iteration process when the number of data points in the invariant set reaches N percent of the total number of input data points. Default is 1. | |
IterateValue | Property to control the iteration process for determining the invariant set of data points. Enter true to repeat the process until either no more data points are eliminated, or a predetermined percentage of data points (PercentileValue) is reached. Enter false to perform only one iteration of the process. Default is true. | |
MethodValue | Property to select the smoothing method used to normalize the data. Enter 'lowess' or 'runmedian'. Default is 'lowess'. | |
SpanValue | Property to set the window size for the smoothing method. If 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. | |
ShowplotValue | Property to control the plotting of a pair of M-A scatter plots (before and after normalization). M is the ratio between DataX and DataY. A is the average of DataX and DataY. Enter true to create the pair of M-A scatter plots. Default is false. |
NormDataY = mainvarsetnorm(DataX, DataY) normalizes the values in 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. |
NormDataY = mainvarsetnorm(..., 'PropertyName', PropertyValue, ...) calls 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:
NormDataY =
mainvarsetnorm(..., 'Thresholds', ThresholdsValue,
...) 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 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 1-by-2 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].
NormDataY = mainvarsetnorm(..., 'Exclude', ExcludeValue, ...) filters the invariant set of data points, by excluding the data points whose average rank (between DataX and DataY) is in the highest N ranked averages or lowest N ranked averages.
NormDataY = mainvarsetnorm(..., 'Percentile', PercentileValue, ...) stops the iteration process when the number of data points in the invariant set reaches 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. |
NormDataY = mainvarsetnorm(..., 'Iterate', IterateValue, ...) controls the iteration process for determining the invariant set of data points. When 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.
NormDataY = mainvarsetnorm(..., 'Method', MethodValue, ...) selects the smoothing method for normalizing the data. When MethodValue is 'lowess', mainvarsetnorm uses the lowess method. When MethodValue is 'runmedian', mainvarsetnorm uses the running median method. Default is 'lowess'.
NormDataY = mainvarsetnorm(..., 'Span', SpanValue, ...) sets the window size for the smoothing method. If 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.
NormDataY = mainvarsetnorm(..., 'Showplot', ShowplotValue, ...) determines whether to plot a pair of M-A scatter plots (before and after normalization). M is the ratio between DataX and DataY. A is the average of DataX and DataY. When ShowplotValue is true, mainvarsetnorm plots the M-A scatter plots. Default is false.
[1] Tseng, G.C., Oh, Min-Kyu, 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, 2549-2557.
[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 0033.1-0033.11.