summary: Summarize results from coseq clustering

In andreamrau/coseq: Co-Expression Analysis of Sequencing Data

Description

A function to summarize the clustering results obtained from a Poisson or Gaussian mixture model estimated using coseq. In particular, the function provides the number of clusters selected for the ICL model selection approach (or alternatively, for the capushe non-asymptotic approach if K-means clustering is used), number of genes assigned to each cluster, and if desired the per-gene cluster means.

Usage

## S4 method for signature 'coseqResults'
summary(object, y_profiles, digits = 3, ...)

Arguments

object	An object of class "coseqResults"
y_profiles	Data used for clustering if per-cluster means are desired
digits	Integer indicating the number of decimal places to be used for mixture model parameters
...	Additional arguments

Details

Provides the following summary of results:

1) Number of clusters and model selection criterion used, if applicable.

2) Number of observations across all clusters with a maximum conditional probability greater than 90 observations) for the selected model.

3) Number of observations per cluster with a maximum conditional probability greater than 90 cluster) for the selected model.

4) If desired, the μ values and π values for the selected model in the case of a Gaussian mixture model.

Value

Summary of the coseqResults object.

Author(s)

Andrea Rau

References

Rau, A. and Maugis-Rabusseau, C. (2017) Transformation and model choice for co-expression analayis of RNA-seq data. Briefings in Bioinformatics, doi: http://dx.doi.org/10.1101/065607.

Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2017) Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. arXiv:1704.06150.

See Also

coseq

Examples

## Simulate toy data, n = 300 observations
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
conds <- rep(c("A","B","C","D"), each=2)

## Run the Normal mixture model for K = 2,3,4
run_arcsin <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin",
                    model="Normal", seed=12345)
run_arcsin

## Plot and summarize results
plot(run_arcsin)
summary(run_arcsin)

## Compare ARI values for all models (no plot generated here)
ARI <- compareARI(run_arcsin, plot=FALSE)

## Compare ICL values for models with arcsin and logit transformations
run_logit <- coseq(object=countmat, K=2:4, iter=5, transformation="logit",
                   model="Normal")
compareICL(list(run_arcsin, run_logit))

## Use accessor functions to explore results
clusters(run_arcsin)
likelihood(run_arcsin)
nbCluster(run_arcsin)
ICL(run_arcsin)

## Examine transformed counts and profiles used for graphing
tcounts(run_arcsin)
profiles(run_arcsin)

## Run the K-means algorithm for logclr profiles for K = 2,..., 20
run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr",
                    model="kmeans")
run_kmeans

andreamrau/coseq documentation built on July 25, 2021, 10:17 a.m.