generateSyntheticData(dataset, n.vars, samples.per.cond, n.diffexp, repl.id = 1, seqdepth = 1e+07, minfact = 0.7, maxfact = 1.4, relmeans = "auto", dispersions = "auto", fraction.upregulated = 1, between.group.diffdisp = FALSE, filter.threshold.total = 1, filter.threshold.mediancpm = 0, fraction.non.overdispersed = 0, random.outlier.high.prob = 0, random.outlier.low.prob = 0, single.outlier.high.prob = 0, single.outlier.low.prob = 0, effect.size = 1.5, output.file = NULL)filter.threshold.total and filter.threshold.mediancpm), the number of genes in the final data set may be lower than this number.minfact and maxfact for each sample to generate data with different actual sequencing depths.seqdepth to generate individual sequencing depths for the simulated samples."auto". Note that these values may be scaled in order to comply with the given sequencing depth. With the default value ("auto"), the mean values are sampled from values estimated from the Pickrell and Cheung data sets. If relmeans is a vector, the provided values will be used as mean values in the simulation for the samples in the first condition. The mean values for the samples in the second condition are generated by combining the relmeans and effect.size arguments."auto". With the default value ("auto"), the dispersion values are sampled from values estimated from the Pickrell and Cheung data sets. If both relmeans and dispersions are set to "auto", the means and dispersion values are sampled in pairs from the values in these data sets. If dispersions is a single vector, the provided dispersions will be used for simulating data from both conditions. If it is a matrix with two columns, the values in the first column are used for condition 1, and the values in the second column are used for condition 2.dispersions is "auto".effect.size. For genes that are upregulated in the second condition, the mean in the first condition is multiplied by the effect size. For genes that are downregulated in the second condition, the mean in the first condition is divided by the effect size. It is also possible to provide a vector of effect sizes (one for each gene), which will be used as provided. In this case, the fraction.upregulated and n.diffexp arguments will be ignored and the values will be derived from the effect.size vector.NULL, the path to the file where the data object should be saved. The extension should be .rds, if not it will be changed.compData object. If output.file is not NULL, the object is saved in the given output.file (which should have an .rds extension).
dataset parameter will be compared. Hence, it is important to give the same value of this parameter e.g. to different replicates generated with the same simulation settings.For more detailed information regarding the different types of outliers, see Soneson and Delorenzi (2013).
Mean and dispersion parameters (if relmeans and/or dispersions is set to "auto") are sampled from values estimated from the data sets by Pickrell et al (2010) and Cheung et al (2010). The data sets were downloaded from the ReCount web page (Frazee et al (2011)) and processed as detailed by Soneson and Delorenzi (2013).
To get the actual mean value for the Negative Binomial distribution used for the simulation of counts for a given sample, take the column truemeans.S1 (or truemeans.S2, if the sample is in condition S2) of the variable.annotations slot, divide by the sum of the same column and multiply with the base sequencing depth (provided in the info.parameters list) and the depth factor for the sample (given in the sample.annotations data frame). Thus, if you have a vector of mean values that you want to provide as the relmeans argument and make sure to use it 'as-is' in the simulation (for condition S1), make sure to set the seqdepth argument to the sum of the values in the relmeans vector, and to set minfact and maxfact equal to 1.
Cheung VG, Nayak RR, Wang IX, Elwyn S, Cousins SM, Morley M and Spielman RS (2010): Polymorphic cis- and trans-regulation of human gene expression. PLoS Biology 8(9):e1000480
Frazee AC, Langmead B and Leek JT (2011): ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics 12:449
Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, Veyrieras JB, Stephens M, Gilad Y and Pritchard JK (2010): Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768-772
Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ and Taylor JM (2012): Efficient experimental design and analysis strategies for the detection of differential expression using RNA-sequencing. BMC Genomics 13:484
mydata.obj <- generateSyntheticData(dataset = "mydata", n.vars = 1000,
samples.per.cond = 5, n.diffexp = 100)
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