| Title: | Rank Product method for identifying differentially expressed genes with application in meta-analysis |
|---|---|
| Description: | Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. |
| Authors: | Francesco Del Carratore <[email protected]>, Andris Jankevics <[email protected]> Fangxin Hong <[email protected]>, Ben Wittner <[email protected]>, Rainer Breitling <[email protected]>, and Florian Battke <[email protected]> |
| Maintainer: | Francesco Del Carratore <[email protected]> |
| License: | file LICENSE |
| Version: | 3.33.0 |
| Built: | 2024-11-27 04:54:52 UTC |
| Source: | https://github.com/bioc/RankProd |
A dataset of LC-MS features, obtained from twenty apples. The last ten apples are spiked with known compounds. This set provides a test case for biomarker selection methods: the task is to retrive the true biomarker variables. The raw LC-MS data hava been converted to CDF format and processed with XCMS to obtain the basepeaks.
data(Apples)data(Apples)
The format is a list of four elements:
mz |
the m/z values of the features (rounded) |
rt |
the retention times of the features |
apples.data |
a matrix containing the intensities in the individual samples |
apples.data.vsn |
a matrix containing the intesities after variance stabilization and normalization performed with the vsn package |
Biom |
the indices of the "true" biomarkers |
apples.cl |
numeric vector encoding which samples are part of the spiked class (code 1) and which ones are controls (code 0) |
Francesco Del Carratore
P. Franceschi, D. Masuero, U. Vrhovsek, F. Mattivi and R. Wehrens: A benchmark spike-in data set for biomarker identification in metabolomics. J. Chemom. 26, 16-24 (2012)
R. Wehrens, P. Franceschi, U. Vrhovsek and F. Mattivi. Stability-based biomarker selection. Analytica Chimica Acta (2011), 705, 15-23. http://dx.doi.org/10.1016/j.aca.2011.01.039
data(Apples) ## show features identified in all apples plot(rt, mz, xlab = "Retention time (s)", ylab = "m/z", main = "Spiked apples - subset")data(Apples) ## show features identified in all apples plot(rt, mz, xlab = "Retention time (s)", ylab = "m/z", main = "Spiked apples - subset")
These data are from Affy ATH1 array experiments of genomic response to brassinosteroid in Arabidopsis conducted by two laoratories. The data set contains 500 random selected genes and 10 samples, 6 from lab 1 and 4 from lab 2. Data were pre-processed by RMA
data(arab)data(arab)
arab |
matrix of gene expression levels of 500 genes from 10 samples, rows correspond to genes and columns to mRNA samples. |
arab.cl |
numeric vector encoding the treatment classes, 5 brassinosteroid-treated cases (code 1) and 5 control cases (code 0) |
arab.gnames |
character vector containing the AffyID of
the 500 genes for the expression matrix |
arab.origin |
numeric vector encoding the origin of the samples, 6 samples from lab 1 (code 1) and 4 samples from lab 2 (code 2) |
Nemhauser JL, Mockler TC, Chory J. Interdependency of
brassinosteroid and auxin signaling in Arabidopsis.
PLoS Biol. 2004 21460.
Microarray data from AtGenExpress
(http://arabidopsis.org/info/expression/ATGenExpress.jsp)
Gene expression data (500 genes and 38 tumor mRNA samples) from the leukemia microarray study of Golub et al. (1999). The original dataset contains 3051 genes
data(golub)data(golub)
golub |
matrix of gene expression levels for the 38 tumor mRNA samples. Rows correspond to genes and columns to mRNA samples. |
golub.cl |
numeric vector encoding the tumor classes, 27 acute lymphoblastic leukemia (ALL) cases (code 0) and 11 acute myeloid leukemia (AML) cases (code 1). |
golub.gnames |
a matrix containing the names of
the 500 genes for the expression matrix |
Golub et al. (1999). Molecular classification of cancer: class
discovery and class prediction by gene expression
monitoring, Science, Vol. 286:531-537.
http://www-genome.wi.mit.edu/MPR/
.
S. Dudoit, J. Fridlyand, and T. P. Speed (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, Vol. 97, No. 457, p. 77–87.
8 cDNA chips from Alizadeh lymphoma paper
data(lymphoma)data(lymphoma)
lymphoma is an exprSet
containing the data of 8 chips
from the lymphoma dataset by Alizadeh et al. (see references). Each
chip represents two samples: on color channel 1 (CH1, Cy3, green) the
common reference sample, and on color channel 2 (CH2, Cy5, red) the
various disease samples. See pData(lymphoma). The 9216x16
matrix exprs(lymphoma) contains the background-subtracted spot
intensities (CH1I-CH1B and CH2I-CH2B, respectively).
The chip intensity files were downloaded from the Stanford microarray database. Starting from the link below, this was done by following the links Published Data -> Alizadeh AA, et al. (2000) Nature 403(6769):503-11 -> Data in SMD -> Display Data, and selecting the following 8 slides:
| lc7b019 |
| lc7b047 |
| lc7b048 |
| lc7b056 |
| lc7b057 |
| lc7b058 |
| lc7b069 |
| lc7b070 |
Then, the script makedata.R from the scripts subdirectory
of this package was run to generate the R data object.
lym.exp |
8 cDNA chips from Alizadeh lymphoma paper |
lynx |
Is a time series with numbers of annual numbers of lynx trapping in Canada from 1821-1934. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker(1977) |
http://genome-www5.stanford.edu/MicroArray/SMD
A. Alizadeh et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503-11, Feb 3, 2000.
Plot a graph of the estimated pfp vs the number of identified genes
plotRP(x, cutoff=NULL)plotRP(x, cutoff=NULL)
x |
the value returned by function RP, RPadvance, RSadvance, RankProducts or RP.advance |
cutoff |
The pfp threshold value used to select genes |
A graphical display of the estimated pfp vs number of identified genes, which is also the gene rank of its original rank product/sum across all comparison. If cutoff is sepcified, a horizontal line will be plotted on the graphic to indicate the positon of the cutoff point, and all genes identified will be marked in red.
Two plots will be displayed, one for the identification of up-regulated genes in class 2, one for the identification of down-regulated genes in class 2
Fangxin Hong, [email protected]
Francesco Del
Carratore, [email protected]
topGene RP
RPadvance RSadvance
# Load the data of Golub et al. (1999). data(golub) #contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl #that consists of the 38 class labels, # and a matrix called golub.gnames whose third column contains the gene names. data(golub) #use a subset of data as example, apply the rank product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, #identify genes that are up-regulated in class 2 #(class label =1) RP.out <- RP(golub[,subset],golub.cl[subset], rand=123) #plot the results plotRP(RP.out,cutoff=0.05)# Load the data of Golub et al. (1999). data(golub) #contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl #that consists of the 38 class labels, # and a matrix called golub.gnames whose third column contains the gene names. data(golub) #use a subset of data as example, apply the rank product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, #identify genes that are up-regulated in class 2 #(class label =1) RP.out <- RP(golub[,subset],golub.cl[subset], rand=123) #plot the results plotRP(RP.out,cutoff=0.05)
The function performs the Rank Product (or Rank Sum) method to identify differentially expressed genes. It is possible to do either a one-class or two-class analysis.
RankProducts(data, cl, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, calculateProduct = TRUE, MinNumOfValidPairs = NA, RandomPairs = NA, huge = FALSE, fast = TRUE, tail.time = 0.05)RankProducts(data, cl, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, calculateProduct = TRUE, MinNumOfValidPairs = NA, RandomPairs = NA, huge = FALSE, fast = TRUE, tail.time = 0.05)
data |
the data set that should be analyzed. Every row of this dataset must correspond to a gene |
cl |
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one class data, the label for each sample should be 1 |
logged |
if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE" |
na.rm |
if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than "MinNumOfValidPairs" are still not considered in the analysis |
gene.names |
if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names |
plot |
if "TRUE", plot the estimated pfp vs the rank of each gene |
rand |
if specified, the random number generator will be put in a reproducible state |
calculateProduct |
if calculateProduct="TRUE" (default) the rank product method is performed. Otherwise the rank sum method is performed |
MinNumOfValidPairs |
a parameter that indicates the minimum number of NAs accepted per each gene. If it is set to NA (default) the half of the number of replicates is used |
RandomPairs |
number of random pairs generated in the function, if set to NA (default), the odd integer closer to the square of the number of replicates is used |
huge |
if "TRUE" not all the outputs are evaluated in order to save space |
fast |
if "FALSE" the exact p-values for the Rank Sum are evaluated for any size of the dataset. Otherwise (default), if the size of the dataset is too big, only the p-values that can be computed in "tail.time" minutes (starting from the tail) are evaluated with the exact method. The others are estimated with the Gaussian approximation. If calculateProduct="TRUE" this parameter is ignored |
tail.time |
the time (default 0.05 min) dedicated to evaluate the exact p-values for the Rank Sum. If calculateProduct="TRUE" this parameter is ignored |
A summary of the results obtained by the Rank Product (or Rank Sum) method.
pfp |
Estimated percentage of false positive predictions (pfp), both considering upregulated an downregulated genes |
pval |
Estimated pvalues per each gene being up- and down-regulated |
RPs/RSs |
The rank-product/rank-sum statistics evaluated per each gene |
RPrank/RSrank |
rank of the Rank Product (or Rank Sum) of each gene in ascending order |
Orirank |
Ranks obtained when considering each possible pairing. In this version of the package, this is not used to compute Rank Product (or Rank Sum), but it is kept for backward compatibility |
AveFC |
Fold change of average expressions (class1/class2). log fold-change if data has been log transformed, original fold change otherwise |
allrank1 |
Fold change of class 1/class 2 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
allrank2 |
Fold change of class 2/class 1 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
nrep |
Total number of replicates |
groups |
Vector of labels (as cl) |
RandomPairs_ranks |
a matrix containing the ranks evaluated for each RandomPair |
Francesco Del Carratore,
[email protected]
Andris Janckevics, [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
topGene RP RPadvance
plotRP RP.advance RSadvance
# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, RP.out <- RankProducts(golub[,subset],golub.cl[subset],rand=123) # class 2: label =1, class 1: label = 0 #pfp for identifying genes that are up-regulated in class 2 #pfp for identifying genes that are down-regulated in class 2 head(RP.out$pfp) #Rank Sum RS.out <-RankProducts(golub[,subset],golub.cl[subset],rand=123, calculateProduct=FALSE) head(RS.out$pfp)# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, RP.out <- RankProducts(golub[,subset],golub.cl[subset],rand=123) # class 2: label =1, class 1: label = 0 #pfp for identifying genes that are up-regulated in class 2 #pfp for identifying genes that are down-regulated in class 2 head(RP.out$pfp) #Rank Sum RS.out <-RankProducts(golub[,subset],golub.cl[subset],rand=123, calculateProduct=FALSE) head(RS.out$pfp)
The function performs the Rank Product method to
identify differentially expressed genes. It is possible to do either a
one-class or two-class analysis. This function has been kept only to guarantee
backward compatibility, in fact the same results can be obtained by
RankProducts.
RP(data, cl, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE)RP(data, cl, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE)
data |
the function performs the Rank Product (or Rank Sum) method to identify differentially expressed genes. It is possible to do either a one-class or two-class analysis |
cl |
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one class data, the label for each sample should be 1 |
num.perm |
in this version of the package, this parameter is not used any more, but it is kept for backward compatibility |
logged |
if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE" |
na.rm |
if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than 50% are still not considered in the analysis |
gene.names |
if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names |
plot |
if "TRUE", plot the estimated pfp vs the rank of each gene |
rand |
if specified, the random number generator will be put in a reproducible state |
huge |
if "TRUE" not all the outputs are evaluated in order to save space |
A summary of the results obtained by the Rank Product method.
pfp |
estimated percentage of false positive predictions (pfp), both considering upregulated an downregulated genes |
pval |
estimated pvalues per each gene being up- and down-regulated |
RPs |
the Rank Product statistics evaluated per each gene |
RPrank |
rank of the Rank Product of each gene in ascending order |
Orirank |
ranks obtained when considering each possible pairing. In this version of the package, this is not used to compute Rank Product (or Rank Sum), but it is kept for backward compatibility |
AveFC |
fold changes of average expressions (class1/class2). log fold-change if data has been log transformed, original fold change otherwise |
allrank1 |
fold change of class 1/class 2 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
allrank2 |
fold change of class 2/class 1 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
nrep |
total number of replicates |
groups |
vector of labels (as cl) |
RandomPairs_ranks |
a matrix containing the ranks evaluated for each RandomPair |
Percentage of false prediction (pfp), in theory, is equivalent of false discovery rate (FDR), and it is possible to be large than 1.
The function looks for up- and down- regulated genes in two seperate steps, thus two pfps and pvalues are computed and used to identify gene that belong to each group.
This function is suitable to deal with data from a single origin, e.g. single experiment. If the data has different origin, e.g. generated at different laboratories, please refer RP.advance.
Francesco Del Carratore,
[email protected]
Andris Janckevics, [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products:A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
topGene RSadvance RPadvance
plotRP RP.advance RankProducts
# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, RP.out <- RP(golub[,subset],golub.cl[subset],rand=123) # class 2: label =1, class 1: label = 0 #pfp for identifying genes that are up-regulated in class 2 #pfp for identifying genes that are down-regulated in class 2 head(RP.out$pfp)# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, RP.out <- RP(golub[,subset],golub.cl[subset],rand=123) # class 2: label =1, class 1: label = 0 #pfp for identifying genes that are up-regulated in class 2 #pfp for identifying genes that are down-regulated in class 2 head(RP.out$pfp)
The function performs the Rank Product (or Rank Sum) method to identify differentially expressed genes. It is possible to do either a one-class or two-class analysis. It is also possible to combine data from different studies (e.g. datasets generated by different laboratories)
RP.advance(data, cl, origin, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, calculateProduct = TRUE, MinNumOfValidPairs = NA, RandomPairs = NA, huge = FALSE, fast = TRUE, tail.time = 0.05)RP.advance(data, cl, origin, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, calculateProduct = TRUE, MinNumOfValidPairs = NA, RandomPairs = NA, huge = FALSE, fast = TRUE, tail.time = 0.05)
data |
the data set that should be analyzed. Every row of this dataset must correspond to a gene |
cl |
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one class data, the label for each sample should be 1 |
origin |
a vector containing the origin labels of the samples. The label is the same for samples within one lab and different for samples from different labs. |
logged |
if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE" |
na.rm |
if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than "MinNumOfValidPairs" are still not considered in the analysis |
gene.names |
if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names |
plot |
if "TRUE", plot the estimated pfp vs the rank of each gene |
rand |
if specified, the random number generator will be put in a reproducible state |
calculateProduct |
if calculateProduct="TRUE" (default) the rank product method is performed. Otherwise the rank sum method is performed |
MinNumOfValidPairs |
a parameter that indicates the minimum number of NAs accepted per each gene. If it is set to NA (default) the half of the number of replicates is used |
RandomPairs |
number of random pairs generated in the function, if set to NA (default), the odd integer closer to the square of the number of replicates is used |
huge |
if "TRUE" not all the outputs are evaluated in order to save space |
fast |
if "FALSE" the exact p-values for the Rank Sum are evaluated for any size of the dataset. Otherwise (default), if the size of the dataset is too big, only the p-values that can be computed in "tail.time" minutes (starting from the tail) are evaluated with the exact method. The others are estimated with the Gaussian approximation. If calculateProduct="TRUE" this parameter is ignored |
tail.time |
the time (default 0.05 min) dedicated to evaluate the exact p-values for the Rank Sum.If calculateProduct="TRUE" this parameter is ignored. |
A summary of the results obtained by the Rank Product (or Rank Sum) method.
pfp |
estimated percentage of false positive predictions (pfp), both considering upregulated an downregulated genes |
pval |
estimated pvalues per each gene being up- and down-regulated |
RPs/RSs |
the Rank Product (or Rank Sum) statistics evaluated per each gene |
RPrank/RSrank |
rank of the Rank Product (or Rank Sum) of each gene in ascending order |
Orirank |
ranks obtained when considering each possible pairing. In this version of the package, this is not used to compute Rank Product (or Rank Sum), but it is kept for backward compatibility |
AveFC |
fold changes of average expressions (class1/class2). log fold-change if data has been log transformed, original fold change otherwise |
allrank1 |
fold change of class 1/class 2 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
allrank2 |
fold change of class 2/class 1 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
nrep |
total number of replicates |
groups |
vector of labels (as cl) |
RandomPairs_ranks |
a matrix containing the ranks evaluated for each RandomPair |
Francesco Del Carratore,
[email protected]
Andris Janckevics, [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
topGene RP
RPadvance plotRP
RankProducts RSadvance
# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) ##For data with single origin subset <- c(1:4,28:30) origin <- rep(1,7) #identify genes RP.out <- RP.advance(golub[,subset],golub.cl[subset], origin,plot=FALSE,rand=123) #For data from multiple origins # Load the data arab in the package, which contains # the expression of 22,081 genes # of control and treatment group from the experiments # indenpently conducted at two #laboratories. data(arab) arab.origin #1 1 1 1 1 1 2 2 2 2 arab.cl #0 0 0 1 1 1 0 0 1 1 RP.adv.out <- RP.advance(arab,arab.cl,arab.origin, gene.names=arab.gnames,logged=TRUE,rand=123) attributes(RP.adv.out) head(RP.adv.out$pfp) head(RP.adv.out$RPs) head(RP.adv.out$AveFC) #Suppose we want to check the consistence of the data #sets generated in two different #labs. For example, we would look for genes that were \ # measured to be up-regulated in #class 2 at lab 1, but down-regulated in class 2 at lab 2.\ data(arab) arab.cl2 <- arab.cl arab.cl2[arab.cl==0 &arab.origin==2] <- 1 arab.cl2[arab.cl==1 &arab.origin==2] <- 0 arab.cl2 ##[1] 0 0 0 1 1 1 1 1 0 0 #look for genes differentially expressed #between hypothetical class 1 and 2 arab.sub=arab[1:500,] ##using subset for fast computation arab.gnames.sub=arab.gnames[1:500] Rsum.adv.out <- RP.advance(arab.sub,arab.cl2,arab.origin,calculateProduct =FALSE,logged=TRUE,gene.names=arab.gnames.sub,rand=123) attributes(Rsum.adv.out)# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) ##For data with single origin subset <- c(1:4,28:30) origin <- rep(1,7) #identify genes RP.out <- RP.advance(golub[,subset],golub.cl[subset], origin,plot=FALSE,rand=123) #For data from multiple origins # Load the data arab in the package, which contains # the expression of 22,081 genes # of control and treatment group from the experiments # indenpently conducted at two #laboratories. data(arab) arab.origin #1 1 1 1 1 1 2 2 2 2 arab.cl #0 0 0 1 1 1 0 0 1 1 RP.adv.out <- RP.advance(arab,arab.cl,arab.origin, gene.names=arab.gnames,logged=TRUE,rand=123) attributes(RP.adv.out) head(RP.adv.out$pfp) head(RP.adv.out$RPs) head(RP.adv.out$AveFC) #Suppose we want to check the consistence of the data #sets generated in two different #labs. For example, we would look for genes that were \ # measured to be up-regulated in #class 2 at lab 1, but down-regulated in class 2 at lab 2.\ data(arab) arab.cl2 <- arab.cl arab.cl2[arab.cl==0 &arab.origin==2] <- 1 arab.cl2[arab.cl==1 &arab.origin==2] <- 0 arab.cl2 ##[1] 0 0 0 1 1 1 1 1 0 0 #look for genes differentially expressed #between hypothetical class 1 and 2 arab.sub=arab[1:500,] ##using subset for fast computation arab.gnames.sub=arab.gnames[1:500] Rsum.adv.out <- RP.advance(arab.sub,arab.cl2,arab.origin,calculateProduct =FALSE,logged=TRUE,gene.names=arab.gnames.sub,rand=123) attributes(Rsum.adv.out)
The function performs the Rank Product method to
identify differentially expressed genes. It is possible to do either a
one-class or two-class analysis. It is also possible to combine data from
different studies (e.g. datasets generated by different laboratories. This
function has been kept only to guarantee backward compatibility, in fact the
same results can be obtained by RankProducts.
RPadvance(data, cl, origin, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE)RPadvance(data, cl, origin, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE)
data |
the data set that should be analyzed. Every row of this dataset must correspond to a gene |
cl |
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one class data, the label for each sample should be 1 |
origin |
a vector containing the origin labels of the samples. The label is the same for samples within one lab and different for samples from different labs. |
num.perm |
in this version of the package, this parameter is not used any more, but it is kept for backward compatibility |
logged |
if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE" |
na.rm |
if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than 50% are still not considered in the analysis |
gene.names |
if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names |
plot |
if "TRUE", plot the estimated pfp vs the rank of each gene |
rand |
if specified, the random number generator will be put in a reproducible state |
huge |
if "TRUE" not all the outputs are evaluated in order to save space |
A summary of the results obtained by the Rank Product method.
pfp |
estimated percentage of false positive predictions (pfp), both considering upregulated an downregulated genes |
pval |
estimated pvalues per each gene being up- and down-regulated |
RPs |
the Rank Product statistics evaluated per each gene |
RPrank |
rank of the Rank Product of each gene in ascending order |
Orirank |
ranks obtained when considering each possible pairing. In this version of the package, this is not used to compute Rank Product (or Rank Sum), but it is kept for backward compatibility |
AveFC |
fold changes of average expressions (class1/class2). log fold-change if data has been log transformed, original fold change otherwise |
allrank1 |
fold change of class 1/class 2 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
allrank2 |
fold change of class 2/class 1 under each origin. log fold-change if data has been log transformed, original fold change otherwise |
nrep |
total number of replicates |
groups |
vector of labels (as cl) |
Percentage of false prediction (pfp), in theory, is equivalent of false discovery rate (FDR), and it is possible to be large than 1.
The function looks for up- and down- regulated genes in two seperate steps, thus two pfps are computed and used to identify gene that belong to each group. The function is able to replace function RP in the same library. it is a more general version, as it is able to handle data from differnt origins.
Francesco Del Carratore,
[email protected]
Andris Janckevics, [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
topGene RP
RSadvance plotRP
RP.advance RankProducts
# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) ##For data with single origin subset <- c(1:4,28:30) origin <- rep(1,7) #identify genes RP.out <- RPadvance(golub[,subset],golub.cl[subset], origin,plot=FALSE,rand=123) #For data from multiple origins #Load the data arab in the package, which contains # the expression of 22,081 genes # of control and treatment group from the experiments #indenpently conducted at two #laboratories. data(arab) arab.origin #1 1 1 1 1 1 2 2 2 2 arab.cl #0 0 0 1 1 1 0 0 1 1 RP.adv.out <- RPadvance(arab,arab.cl,arab.origin, num.perm=100,gene.names=arab.gnames,logged=TRUE,rand=123) attributes(RP.adv.out) head(RP.adv.out$pfp) head(RP.adv.out$RPs) head(RP.adv.out$AveFC)# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) ##For data with single origin subset <- c(1:4,28:30) origin <- rep(1,7) #identify genes RP.out <- RPadvance(golub[,subset],golub.cl[subset], origin,plot=FALSE,rand=123) #For data from multiple origins #Load the data arab in the package, which contains # the expression of 22,081 genes # of control and treatment group from the experiments #indenpently conducted at two #laboratories. data(arab) arab.origin #1 1 1 1 1 1 2 2 2 2 arab.cl #0 0 0 1 1 1 0 0 1 1 RP.adv.out <- RPadvance(arab,arab.cl,arab.origin, num.perm=100,gene.names=arab.gnames,logged=TRUE,rand=123) attributes(RP.adv.out) head(RP.adv.out$pfp) head(RP.adv.out$RPs) head(RP.adv.out$AveFC)
The function performs the Rank Sum method to
identify differentially expressed genes. It is possible to do either a
one-class or two-class analysis. It is also possible to combine data from
different studies (e.g. datasets generated by different laboratories.
This function has been kept only to guarantee
backward compatibility, in fact the same results can be obtained by
RankProducts.
RSadvance(data, cl, origin, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE, fast = TRUE, tail.time = 0.05)RSadvance(data, cl, origin, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE, fast = TRUE, tail.time = 0.05)
data |
the data set that should be analyzed. Every row of this dataset must correspond to a gene |
cl |
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one class data, the label for each sample should be 1 |
origin |
a vector containing the origin labels of the samples. The label is the same for samples within one lab and different for samples from different labs. |
num.perm |
in this version of the package, this parameter is not used any more, but it is kept for backward compatibility |
logged |
if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE" |
na.rm |
if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than 50% are still not considered in the analysis |
gene.names |
if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names |
plot |
if "TRUE", plot the estimated pfp vs the rank of each gene |
rand |
if specified, the random number generator will be put in a reproducible state |
huge |
if "TRUE" not all the outputs are evaluated in order to save space |
fast |
if "FALSE" the exact p-values for the Rank Sum are evaluated for any size of the dataset. Otherwise (default), if the size of the dataset is too big, only the p-values that can be computed in "tail.time" minutes (starting from the tail) are evaluated with the exact method. The others are estimated with the Gaussian approximation. If calculateProduct="TRUE" this parameter is ignored |
tail.time |
the time (default 0.05 min) dedicated to evaluate the exact p-values for the Rank Sum. If calculateProduct="TRUE" this parameter is ignored |
A result of identifying differentially expressed genes between two classes. The identification consists of two parts, the identification of up-regulated and down-regulated genes in class 2 compared to class 1, respectively.
pfp |
Estimated percentage of false positive predictions (pfp) up to the position of each gene under two identificaiton each |
pval |
Estimated pvalues for each gene being up- and down-regulated |
RSs |
Rank-sum (average rank) of each genes |
RSrank |
Rank of the rank sum of each gene in ascending order |
Orirank |
Ranks in each possible pairing, in this version of the function this is not used to compute rank sum. It is here only for backward compatibility |
AveFC |
Fold change of average expression under class 1 over that under class 2, if multiple origin, than avraged across all origin. Log-fold change if data is in log scaled, original fold change if data is unlogged |
allrank1 |
Fold change of class 1/class 2 under each origin. Log-fold change if data is in log scaled |
allrank2 |
Fold change of class 2/class 1 under each origin. Log-fold change if data is in log scaled |
nrep |
Total number of replicates considering all the different origins |
groups |
Vector of labels (as cl). |
Percentage of false prediction (pfp), in theory, is equivalent of false discovery rate (FDR), and it is possible to be large than 1.
The function looks for up- and down- regulated genes in two seperate steps, thus two pfps are computed and used to identify gene that belong to each group.
The function is able to deal with single or multiple-orgin studies. It is similar to funcion RP.advance expect a rank sum is computed instead of rank product. This method is more sensitive to individual rank values, while rank product is more robust to outliers (refer RankProd vignette for details)
Francesco Del Carratore,
[email protected]
Andris Janckevics, [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
topGene RP RPadvance
plotRP RP.advance RankProducts
#Suppose we want to check the consistence of the data #sets generated in two different #labs. For example, we would look for genes that were \ # measured to be up-regulated in #class 2 at lab 1, but down-regulated in class 2 at lab 2.\ data(arab) arab.cl2 <- arab.cl arab.cl2[arab.cl==0 &arab.origin==2] <- 1 arab.cl2[arab.cl==1 &arab.origin==2] <- 0 arab.cl2 ##[1] 0 0 0 1 1 1 1 1 0 0 #look for genes differentially expressed #between hypothetical class 1 and 2 arab.sub=arab[1:500,] ##using subset for fast computation arab.gnames.sub=arab.gnames[1:500] Rsum.adv.out <- RSadvance(arab.sub,arab.cl2,arab.origin, num.perm=100,logged=TRUE, gene.names=arab.gnames.sub,rand=123) attributes(Rsum.adv.out)#Suppose we want to check the consistence of the data #sets generated in two different #labs. For example, we would look for genes that were \ # measured to be up-regulated in #class 2 at lab 1, but down-regulated in class 2 at lab 2.\ data(arab) arab.cl2 <- arab.cl arab.cl2[arab.cl==0 &arab.origin==2] <- 1 arab.cl2[arab.cl==1 &arab.origin==2] <- 0 arab.cl2 ##[1] 0 0 0 1 1 1 1 1 0 0 #look for genes differentially expressed #between hypothetical class 1 and 2 arab.sub=arab[1:500,] ##using subset for fast computation arab.gnames.sub=arab.gnames[1:500] Rsum.adv.out <- RSadvance(arab.sub,arab.cl2,arab.origin, num.perm=100,logged=TRUE, gene.names=arab.gnames.sub,rand=123) attributes(Rsum.adv.out)
Indentify diiffentially expressed genes using rank product method
topGene(x,cutoff=NULL,method="pfp",num.gene=NULL,logged=TRUE, logbase=2,gene.names=NULL)topGene(x,cutoff=NULL,method="pfp",num.gene=NULL,logged=TRUE, logbase=2,gene.names=NULL)
x |
the value returned by function RP, RPadvance, RSadvance, RankProducts or RP.advance |
cutoff |
The pfp threshold value used to select genes |
method |
if cutoff is provided, the method needs to be selected to identify genes. "pfp" uses percentage of false prediction, which is a default setting. "pval" uses p-values which is less stringent than pfp |
logged |
if "TRUE", data has been logged, otherwise set it to "FALSE" |
logbase |
base used when taking log, used to restore the fold change.The default value is 2, this will be ignored if logged=FALSE |
gene.names |
if "NULL", no gene name will be attached to the output table |
num.gene |
number of candidates genes of interests, if cutoff is provided, this will be ignored |
Two tables of identified genes with gene.index: index of gene in the original data set RP/Rsum: Computed rank product/sum for each gene FC:(class1/class2): Expression Fold change of class 1/ class 2. pfp: estimated pfp for each gene if the gene is used as cutoff point P.value: estimated p-value for each gene Table 1 list genes that are up-regulated under class 2, Table 1 ist genes that are down-regulated under class 2
Fangxin Hong [email protected]
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, #identify genes RP.out <- RP(golub[,subset],golub.cl[subset],rand=123) #get two lists of differentially expressed genes #by setting FDR (false discivery rate) =0.05 table=topGene(RP.out,cutoff=0.05,method="pfp",logged=TRUE,logbase=2, gene.names=golub.gnames[,3]) table$Table1 table$Table2 #using pvalue<0.05 topGene(RP.out,cutoff=0.05,method="pval",logged=TRUE,logbase=2, gene.names=golub.gnames[,3]) #by selecting top 10 genes topGene(RP.out,num.gene=10,gene.names=golub.gnames[,3])# Load the data of Golub et al. (1999). data(golub) # contains a 3051x38 gene expression # matrix called golub, a vector of length called golub.cl # that consists of the 38 class labels, # and a matrix called golub.gnames whose third column # contains the gene names. data(golub) #use a subset of data as example, apply the rank #product method subset <- c(1:4,28:30) #Setting rand=123, to make the results reproducible, #identify genes RP.out <- RP(golub[,subset],golub.cl[subset],rand=123) #get two lists of differentially expressed genes #by setting FDR (false discivery rate) =0.05 table=topGene(RP.out,cutoff=0.05,method="pfp",logged=TRUE,logbase=2, gene.names=golub.gnames[,3]) table$Table1 table$Table2 #using pvalue<0.05 topGene(RP.out,cutoff=0.05,method="pval",logged=TRUE,logbase=2, gene.names=golub.gnames[,3]) #by selecting top 10 genes topGene(RP.out,num.gene=10,gene.names=golub.gnames[,3])