PoisMixSim {HTSCluster} | R Documentation |
Simulate data from a Poisson mixture model
Description
This function simulates data from a Poisson mixture model, as described by Rau et al. (2011). Data are simulated with varying expression level () for 4 clusters. Clusters may be simulated with “high” or “low” separation, and three different options are available for the library size setting: “equal”, “A”, and “B”, as described by Rau et al. (2011).
Usage
PoisMixSim(n = 2000, libsize, separation)
Arguments
n |
Number of observations |
libsize |
The type of library size difference to be simulated (“ |
separation |
Cluster separation (“ |
Value
y |
(n x q) matrix of simulated counts for n observations and q variables |
labels |
Vector of length n defining the true cluster labels of the simulated data |
pi |
Vector of length 4 (the number of clusters) containing the true value of |
lambda |
(d x 4) matrix of |
w |
Row sums of |
conditions |
Vector of length q defining the condition (treatment group) for each variable (column) in |
Note
If one or more observations are simulated such that all variables have a value of 0, those rows are removed from the data matrix; as such, in some cases the simulated data y
may have less than n
rows.
The PMM-I model includes the parameter constraint , where
is the number of replicates in condition (treatment group)
. Similarly, the parameter constraint in the PMM-II model is
, where
is the library size for replicate l of condition j. The value of
lambda
corresponds to that used to generate the simulated data, where the library sizes were set as described in Table 2 of Rau et al. (2011). However, due to variability in the simulation process, the actually library sizes of the data y
are not exactly equal to these values; this means that the value of lambda
may not be directly compared to an estimated value of as obtained from the
PoisMixClus
function.
Author(s)
Andrea Rau
References
Rau, A., Celeux, G., Martin-Magniette, M.-L., Maugis-Rabusseau, C. (2011). Clustering high-throughput sequencing data with Poisson mixture models. Inria Research Report 7786. Available at https://inria.hal.science/inria-00638082.
Examples
set.seed(12345)
## Simulate data as shown in Rau et al. (2011)
## Library size setting "A", high cluster separation
## n = 200 observations
simulate <- PoisMixSim(n = 200, libsize = "A", separation = "high")
y <- simulate$y
conds <- simulate$conditions