# Clustering high throughput sequencing (HTS) data 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 ## Author(s) Andrea Rau, Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau Maintainer: Andrea Rau ## References 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 [https://inria.hal.science/inria-00638082](https://inria.hal.science/inria-00638082). ## Examples ```r 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") ## } ```

HTSCluster-package