Description Details Author(s) References Examples
A Poisson mixture model is implemented to cluster genes from high-throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).
Package: | HTSCluster |
Type: | Package |
Version: | 2.0.8 |
Date: | 2016-05-26 |
License: | GPL (>=3) |
LazyLoad: | yes |
Andrea Rau, Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau
Maintainer: Andrea Rau <andrea.rau@jouy.inra.fr>
Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux G. (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9):1420-1427.
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 http://hal.inria.fr/inria-00638082.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | set.seed(12345)
## Simulate data as shown in Rau et al. (2011)
## Library size setting "A", high cluster separation
## n = 2000 observations
simulate <- PoisMixSim(n = 200, libsize = "A", separation = "high")
y <- simulate$y
conds <- simulate$conditions
## Run the PMM model for g = 3
## "TC" library size estimate, EM algorithm
run <- PoisMixClus(y, g=3, conds=conds, norm="TC")
## Estimates of pi and lambda for the selected model
pi.est <- run$pi
lambda.est <- run$lambda
## Not run: PMM for 4 total clusters, with one fixed class
## "TC" library size estimate, EM algorithm
##
## run <- PoisMixClus(y, g = 3, norm = "TC", conds = conds,
## fixed.lambda = list(c(1,1,1)))
##
##
## Not run: PMM model for 4 clusters, with equal proportions
## "TC" library size estimate, EM algorithm
##
## run <- PoisMixClus(y, g = 4, norm = "TC", conds = conds,
## equal.proportions = TRUE)
##
##
## Not run: PMM model for g = 1, ..., 10 clusters, Split Small-EM init
##
## run1.10 <- PoisMixClusWrapper(y, gmin = 1, gmax = 10, conds = conds,
## norm = "TC")
##
##
## Not run: PMM model for g = 1, ..., 10 clusters, Small-EM init
##
## run1.10bis <- <- PoisMixClusWrapper(y, gmin = 1, gmax = 10, conds = conds,
## norm = "TC", split.init = FALSE)
##
##
## Not run: previous model equivalent to the following
##
## for(K in 1:10) {
## run <- PoisMixClus(y, g = K, conds = conds, norm = "TC")
## }
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