HTSCluster-package: Clustering high throughput sequencing (HTS) data

In HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data

Clustering high throughput sequencing (HTS) data

Description

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).

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.

Examples

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 documentation built on Sept. 8, 2023, 5:56 p.m.