A R package for Grade of Membership (GoM) model fit and Visualization of counts data-
CountClust version 1.6.1 is now out. Check the NEWS file for the updates!.
How to cite
If you are find CountClust useful, please cite:
Dey K, Hsiao C, and Stephens M (2017). Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genetics 13: e1006599
Taddy M (2012). On Estimation and Selection for Topic Models. AISTATS, JMLR 22.
CountClust requires the following CRAN-R packages:
parallel along with the Bioconductor package:
limma. Also the user needs to install the latest
maptpx package from Github.
Then one can install
CountClust from Bioc as follows
For installing the working version of this package from Github please run
To replicate the data example in this README, please install the following data package.
Then load the
CountClust package in R:
Application of CountClust
We load the single cell RNA-seq data due to Deng et al 2014. The data contains RNA-seq read counts for single cells at different stages of mouse embryo development (from zygote to blastocyst).
library(singleCellRNASeqMouseDeng2014) deng.counts <- exprs(Deng2014MouseESC) deng.meta_data <- pData(Deng2014MouseESC) deng.gene_names <- rownames(deng.counts)
GoM Model fit
We apply the
StructureObj function (which is a wrapper of the
topics function in the maptpx package) for a vector of number of clusters, ranging from 2 to 7.
FitGoM(t(deng.counts), K=c(3,6), tol=0.1, path_rda="data/MouseDeng2014.FitGoM.rda")
This function will output a list, each element representing a GoM model fit output for a particular cluster number.
One can plot the
omega from the
StructureObj fit using a Structure plot. Here we provide an example of the Structure plot for K=6 for the above GoM model fit.
data("MouseDeng2014.FitGoM") names(MouseDeng2014.FitGoM) omega <- MouseDeng2014.FitGoM$clust_6$omega annotation <- data.frame( sample_id = paste0("X", c(1:NROW(omega))), tissue_label = factor(rownames(omega), levels = rev( c("zy", "early2cell", "mid2cell", "late2cell", "4cell", "8cell", "16cell", "earlyblast","midblast", "lateblast") ) ) ) rownames(omega) <- annotation$sample_id; StructureGGplot(omega = omega, annotation = annotation, palette = RColorBrewer::brewer.pal(8, "Accent"), yaxis_label = "Amplification batch", order_sample = TRUE, axis_tick = list(axis_ticks_length = .1, axis_ticks_lwd_y = .1, axis_ticks_lwd_x = .1, axis_label_size = 7, axis_label_face = "bold"))
We can extract the features that drive the clusters for K=6 as follows
theta_mat <- MouseDeng2014.topicFit$clust_6$theta; top_features <- ExtractTopFeatures(theta_mat, top_features=100, method="poisson", options="min"); gene_list <- do.call(rbind, lapply(1:dim(top_features), function(x) deng.gene_names[top_features[x,]]))
It will provide you with a list of top 100 variables/features per cluster that are relatively most highly expressed in that cluster compared to the other clusters, or in other words, plays the most important role in driving or separating out that cluster from the rest.
The CountClust package is distributed under [GPL - General Public License (>= 2)]
For any questions or comments, please contact firstname.lastname@example.org
- Raman Shah
- Peter Carbonetto