These functions implement the EM and CEM algorithms for parameter estimation in a Poisson mixture model for clustering high throughput sequencing observations (e.g., genes) for a single number of clusters (PoisMixClus) or a sequence of cluster numbers (PoisMixClusWrapper). Parameters are initialized using a Small-EM strategy as described in Rau et al. (2011) or the splitting small-EM strategy described in Papastamoulis et al. (2014), and model selection is performed using the ICL criteria. Note that these functions implement the PMM-I and PMM-II models described in Rau et al. (2011).
PoisMixClus(y, g, conds, norm = "TMM",
init.type = "small-em", init.runs = 1, init.iter = 10,
alg.type = "EM", cutoff = 10e-6, iter = 1000, fixed.lambda = NA,
equal.proportions = FALSE, prev.labels = NA,
prev.probaPost = NA, verbose = FALSE, interpretation = "sum",
EM.verbose = FALSE, wrapper = FALSE, subset.index = NA)PoisMixClusWrapper(y, gmin = 1, gmax, conds,
norm = "TMM", gmin.init.type = "small-em",
init.runs = 1, init.iter = 10, split.init = TRUE, alg.type = "EM",
cutoff = 10e-6, iter = 1000, fixed.lambda = NA,
equal.proportions = FALSE, verbose = FALSE, interpretation = "sum",
EM.verbose = FALSE, subset.index = NA)
(d x g) matrix containing the estimate of \(\hat{\boldsymbol{\lambda}}\)
Vector of length g containing the estimate of \(\hat{\boldsymbol{\pi}}\)
Vector of length n containing the cluster assignments of the n observations
Matrix containing the conditional probabilities of belonging to each cluster for all observations
Value of log likelihood
Value of BIC criterion
Value of ICL criterion
Estimation algorithm used; matches the argument alg.type above)
Library size normalization factors used
Conditions specified by user
Number of iterations run
Difference in log-likelihood between the last and penultimate iterations of the algorithm
If provided by the user, the indices of subset of genes used for co-expression analyses
Log likelihoods calculated for each of the fitted models for cluster sizes gmin, ..., gmax
Results of capushe model selection, an object of class "Capushe"
ICL values calculated for each of the fitted models for cluster sizes gmin, ..., gmax
Object of class HTSCluster giving the results from the model chosen via the ICL criterion
Object of class HTSCluster giving the results from the model chosen via the BIC
Object of class HTSCluster giving the results from the model chosen via the DDSE slope heuristics criterion
Object of class HTSCluster giving the results from the model chosen via the Djump slope heuristics criterion
List of objects of class HTSCluster giving the results for all models for cluster sizes gmin, ..., gmax
Type of criteria used for model selection, equal to NA for direct calls to PoisMixClus or
"DDSE", "Djump", "BIC", or "ICL" for the respective selected models for calls to PoisMixClusWrapper
(n x q) matrix of observed counts for n observations and q variables
Number of clusters (a single value). If fixed.lambda contains a list of lambda values to be fixed,
g corresponds to the number of clusters in addition to those fixed.
The minimum number of clusters in a sequence to be tested. In cases where clusters are included with a fixed value
of lambda, gmin corresponds to the minimum number of clusters in addition to those that are fixed.
The maximum number of clusters in a sequence to be tested. In cases where clusters are included with a fixed value
of lambda, gmax corresponds to the maximum number of clusters in addition to those that are fixed.
Vector of length q defining the condition (treatment group) for each variable (column) in y
The type of estimator to be used to normalize for differences in library size: (“TC” for total count, “UQ” for upper quantile, “Med” for median,
“DESeq” for the normalization method in the DESeq package, and “TMM” for the TMM normalization method (Robinson and Oshlack, 2010). Can also
be a vector (of length q) containing pre-estimated library size estimates for each sample. Note that if the user provides
pre-calculated normalization factors, the package will make use of norm/sum(norm) as normalization factors.
Type of initialization strategy to be used (“small-em” for the Small-EM strategy described in Rau et al. (2011), and “kmeans” for a simple K-means initialization)
Type of initialization strategy to be used for the minimum number of clusters in a sequence (gmin):
(“small-em” for the Small-EM strategy described in Rau et al. (2011), and “kmeans” for a simple K-means initialization)
Number of runs to be used for the Small-EM strategy described in Rau et al. (2011), with a default value of 1
Number of iterations to be used within each run for the Small-EM strategry, with a default value of 10
If TRUE, the splitting initialization strategy of Papastamoulis et al. (2014) will be used for cluster sizes
(gmin+1, ..., gmax). If FALSE, the initialization strategy specified in gmin.init.type
is used for all cluster sizes in the sequence.
Algorithm to be used for parameter estimation (“EM” or “CEM”)
Cutoff to declare algorithm convergence (in terms of differences in log likelihoods from one iteration to the next)
Maximum number of iterations to be run for the chosen algorithm
If one (or more) clusters with fixed values of lambda is desired, a list containing vectors of length d (the number of conditions). specifying the fixed values of lambda for each fixed cluster.
If TRUE, the cluster proportions are set to be equal for all clusters. Default is FALSE (unequal cluster proportions).
A vector of length n of cluster labels obtained from the previous run (g-1 clusters) to be used with the splitting small-EM strategy described in described in Papastamoulis et al. (2014). For other initialization strategies, this parameter takes the value NA
An n x (g-1) matrix of the conditional probabilities of each observation belonging to each of the g-1 clusters from the previous run, to be used with the splitting small-EM strategy of described in Papastamoulis et al. (2012). For other initialization strategies, this parameter takes the value NA
If TRUE, include verbose output
If "sum", cluster behavior is interpreted with respect to overall gene expression level (sums per gene),
otherwise for "mean", cluster behavior is interpreted with respect to mean gene expression (means per gene).
If TRUE, more informative output is printed about the EM algorithm, including the number of iterations run and
the difference between log-likelihoods at the last and penultimate iterations.
Optional vector providing the indices of a subset of genes that should be used for the co-expression analysis (i.e., row indices
of the data matrix y.
TRUE if the PoisMixClus function is run from within the PoisMixClusWrapper main function, and FALSE
otherwise. This mainly helps to avoid recalculating parameters several times that are used throughout the algorithm (e.g., library
sizes, etc.)
Andrea Rau
Output of PoisMixClus is an S3 object of class HTSCluster, and output of PoisMixClusWrapper is an S3 object
of class HTSClusterWrapper.
In a Poisson mixture model, the data \(\mathbf{y}\) are assumed to come from g distinct subpopulations (clusters), each of which is modeled separately; the overall population is thus a mixture of these subpopulations. In the case of a Poisson mixture model with g components, the model may be written as
$$f(\mathbf{y};g,\boldsymbol{\Psi}_g) = \prod_{i=1}^n \sum_{k=1}^g \pi_k \prod_{j=1}^{d}\prod_{l=1}^{r_j} P(y_{ijl} ; \boldsymbol{\theta}_k)$$
for \(i = 1, \ldots, n\) observations in \(l = 1, \ldots, r_j\) replicates of \(j = 1, \ldots, d\) conditions (treatment groups), where \(P(\cdot)\) is the standard Poisson density, \(\boldsymbol{\Psi}_g = (\pi_1,\ldots,\pi_{g-1}, \boldsymbol{\theta}^\prime)\), \(\boldsymbol{\theta}^\prime\) contains all of the parameters in \(\boldsymbol{\theta}_1,\ldots,\boldsymbol{\theta}_g\) assumed to be distinct, and \(\boldsymbol{\pi} = (\pi_1,\ldots,\pi_g)^\prime\) are the mixing proportions such that \(\pi_k\) is in (0,1) for all k and \(\sum_k \pi_k = 1\).
We consider the following parameterization for the mean \(\boldsymbol{\theta}_k = (\mu_{ijlk})\). We consider $$\mu_{ijlk} = w_i s_{jl} \lambda_{jk}$$ where \(w_i\) corresponds to the expression level of observation i, \(\boldsymbol{\lambda}_k = (\lambda_{1k},\ldots,\lambda_{dk})\) corresponds to the clustering parameters that define the profiles of the genes in cluster k across all variables, and \(s_{jl}\) is the normalized library size (a fixed constant) for replicate l of condition j.
There are two approaches to estimating the parameters of a finite mixture model and obtaining a clustering of the data: the estimation approach (via the EM algorithm) and the clustering approach (via the CEM algorithm). Parameter initialization is done using a Small-EM strategy as described in Rau et al. (2011) via the emInit function. Model selection may be performed using the BIC or ICL criteria, or the slope heuristics.
Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biology, 11(R106), 1-28.
Papastamoulis, P., Martin-Magniette, M.-L., and Maugis-Rabusseau, C. (2014). On the estimation of mixtures of Poisson regression models with large number of components. Computational Statistics and Data Analysis: 3rd special Issue on Advances in Mixture Models, DOI: 10.1016/j.csda.2014.07.005.
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.
probaPost for the calculation of the conditional probability of belonging to a cluster;
PoisMixMean for the calculation of the per-cluster conditional mean of each observation;
logLikePoisMixDiff for the calculation of the log likelihood of a Poisson mixture model;
emInit and kmeanInit for the Small-EM parameter initialization strategy
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
## 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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