| Title: | A Flexible Microarray Data Simulation Model |
|---|---|
| Description: | This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified. |
| Authors: | Doulaye Dembele |
| Maintainer: | Doulaye Dembele <[email protected]> |
| License: | GPL (>= 2.0) |
| Version: | 1.2.1 |
| Built: | 2025-02-14 03:24:31 UTC |
| Source: | https://github.com/cran/madsim |
madsim allows to generate two conditions biological synthetic microarray dataset whith known characteristics. These data have similar behavior as those obtained with current microarray platforms.
| Package: | madsim |
| Type: | Package |
| Version: | 1.2.1 |
| Date: | 2016-12-07 |
| License: | GPL (>=2.0) |
This package has only one function
Doulaye Dembele Maintainer: Doulaye Dembele <[email protected]>
Dembele D. (2013), A Flexible Microarray Data Simulation Model, Microarrays, 2013, 2(2):115-130
# set parameters settings fparams <- data.frame(m1 = 7, m2 = 7, shape2 = 4, lb = 4, ub = 14, pde = 0.02, sym = 0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate synthetic data without using real microarray data as seed mydata <- madsim(mdata=NULL, n=10000, ratio=0, fparams, dparams, sdn, rseed); # calculate MMplot variables using samples 1 and 12 A <- 0.5*(mydata$xdata[,12] + mydata$xdata[,1]); M <- mydata$xdata[,12] - mydata$xdata[,1]; # draw MA plot using samples 1 and 12 plot(A,M)# set parameters settings fparams <- data.frame(m1 = 7, m2 = 7, shape2 = 4, lb = 4, ub = 14, pde = 0.02, sym = 0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate synthetic data without using real microarray data as seed mydata <- madsim(mdata=NULL, n=10000, ratio=0, fparams, dparams, sdn, rseed); # calculate MMplot variables using samples 1 and 12 A <- 0.5*(mydata$xdata[,12] + mydata$xdata[,1]); M <- mydata$xdata[,12] - mydata$xdata[,1]; # draw MA plot using samples 1 and 12 plot(A,M)
function madsim() allows to generate two biological conditions synthetic microarray dataset with known characteristics. These data have similar behavior as those obtained with current microarray platforms. Hence, they can be used for performance evaluation of data meta-analysis methods.
madsim(mdata = NULL, n = 10000, ratio = 0, fparams = data.frame(m1=7,m2=7,shape2=4,lb=4,ub=14,pde=0.02,sym=0.5), dparams = data.frame(lambda1=0.13, lambda2=2, muminde=1, sdde=0.5), sdn = 0.4, rseed = 50)madsim(mdata = NULL, n = 10000, ratio = 0, fparams = data.frame(m1=7,m2=7,shape2=4,lb=4,ub=14,pde=0.02,sym=0.5), dparams = data.frame(lambda1=0.13, lambda2=2, muminde=1, sdde=0.5), sdn = 0.4, rseed = 50)
mdata |
a data frame with numerical values to be used as seed,
its length should be greater than 100. When set to
NULL (default) data generated are fully synthetic:
|
n |
an integer specifying the number of genes in the data generated:
|
ratio |
a flag (0,1) allowing to have log2 intensitie or log2 ratio:
|
fparams |
a data frame containing 7 components defining the data
lower (lb) and upper bound (ub), the beta distribution
shape (shape2) parameter, the percentage of differentially
expressed (pde) number of genes and the partition of the
number of down and up regulated (sym) genes: |
dparams |
a data frame containing 4 components defining how low and
high expressed genes are distributed (lambda1), and
how changes are for DE genes (lambda2, muminde, sdde): |
sdn |
a positive scalar used as standard deviation for the
additive gaussian noise: |
rseed |
an integer used as seed for generating random number
by the computer in use: |
User provides a subset of parameters. A detailed description of these parameters is available in the reference given below. Default parameters settings (in arguments above) can be modified.
Returned is a data frame containing 3 components
xdata |
a dataset with sizes, the number of rows and columns, specified by input parameters n and m1+m2, respectively |
xid |
a vector of indexes with values are from the set (0, -1, 1). These values are used for non differentially expressed, down- and up-regulated genes |
xsd |
a scalar containing the standard deviation of first column of the dataset generated |
Doulaye Dembele
Dembele D. (2013), A Flexible Microarray Data Simulation Model. Microarrays, 2013, 2(2):115-130
# load a sample of real microarray data data(madsim_test) # set parameters settings mdata <- madsim_test$V1; fparams <- data.frame(m1 = 7, m2 = 7, shape2 = 4, lb = 4, ub = 14,pde=0.02,sym=0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate fully synthetic data mydata1 <- madsim(mdata = NULL, n = 10000, ratio = 0, fparams, dparams, sdn, rseed); # use true affymetrix data to generate synthetic data mydata2 <- madsim(mdata = madsim_test, n=10000, ratio=0,fparams,dparams,sdn,rseed); A1 <- 0.5*(mydata1$xdata[,12] + mydata1$xdata[,1]); M1 <- mydata1$xdata[,12] - mydata1$xdata[,1]; A2 <- 0.5*(mydata2$xdata[,12] + mydata2$xdata[,1]); M2 <- mydata2$xdata[,12] - mydata2$xdata[,1]; # draw MA plot using samples 1 and 12 op <- par(mfrow = c()) plot(A1,M1) plot(A2,M2) par(op)# load a sample of real microarray data data(madsim_test) # set parameters settings mdata <- madsim_test$V1; fparams <- data.frame(m1 = 7, m2 = 7, shape2 = 4, lb = 4, ub = 14,pde=0.02,sym=0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate fully synthetic data mydata1 <- madsim(mdata = NULL, n = 10000, ratio = 0, fparams, dparams, sdn, rseed); # use true affymetrix data to generate synthetic data mydata2 <- madsim(mdata = madsim_test, n=10000, ratio=0,fparams,dparams,sdn,rseed); A1 <- 0.5*(mydata1$xdata[,12] + mydata1$xdata[,1]); M1 <- mydata1$xdata[,12] - mydata1$xdata[,1]; A2 <- 0.5*(mydata2$xdata[,12] + mydata2$xdata[,1]); M2 <- mydata2$xdata[,12] - mydata2$xdata[,1]; # draw MA plot using samples 1 and 12 op <- par(mfrow = c()) plot(A1,M1) plot(A2,M2) par(op)
A text file containing an example of real microarray which can be used as seed. This dataset is from a Affymetrix GeneChip array (Human Gene 1.0 ST)
data(madsim_test)data(madsim_test)
A data frame with 33297 observations on the following variable.
V1a numeric vector
# load a sample of real microarray data data(madsim_test) # set parameter settings mdata <- madsim_test$V1; fparams <- data.frame(m1=7, m2=7, shape2=4, lb=4, ub=14, pde=0.02, sym=0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate data using microarray as seed mydata <- madsim(mdata, n = 10000, ratio = 0, fparams, dparams, sdn, rseed); # calculate MMplot variables using samples 1 and 12 A <- 0.5*(mydata$xdata[,12] + mydata$xdata[,1]); M <- mydata$xdata[,12] - mydata$xdata[,1]; # draw MAplot representation using samples 1 and 12 plot(A,M)# load a sample of real microarray data data(madsim_test) # set parameter settings mdata <- madsim_test$V1; fparams <- data.frame(m1=7, m2=7, shape2=4, lb=4, ub=14, pde=0.02, sym=0.5); dparams <- data.frame(lambda1 = 0.13, lambda2 = 2, muminde = 1, sdde = 0.5); sdn <- 0.4; rseed <- 50; # generate data using microarray as seed mydata <- madsim(mdata, n = 10000, ratio = 0, fparams, dparams, sdn, rseed); # calculate MMplot variables using samples 1 and 12 A <- 0.5*(mydata$xdata[,12] + mydata$xdata[,1]); M <- mydata$xdata[,12] - mydata$xdata[,1]; # draw MAplot representation using samples 1 and 12 plot(A,M)