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https://github.com/kkdey/CountClust
A R package for Grade of Membership model and Visualization of counts data:
https://github.com/kkdey/CountClust
Last synced: about 1 month ago
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A R package for Grade of Membership model and Visualization of counts data:
- Host: GitHub
- URL: https://github.com/kkdey/CountClust
- Owner: kkdey
- Created: 2015-09-04T18:22:22.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2021-01-15T13:22:50.000Z (almost 4 years ago)
- Last Synced: 2024-08-03T12:15:52.413Z (4 months ago)
- Language: R
- Homepage:
- Size: 96.8 MB
- Stars: 31
- Watchers: 7
- Forks: 11
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-single-cell - CountClust - [R] - Functions for fitting Grade-of-Membership models, also known as "Topic models", to RNA-seq counts. These models generalize clustering methods to allow that each cell may belong to more than one cluster/topic. (Software packages / RNA-seq)
README
# CountClust
An R package for Grade of Membership (GoM) model fit and visualization
of counts data.
[Kushal K Dey](http://kkdey.github.io), [Chiaowen Joyce Hsiao](http://jhsiao999.github.io/), [Matthew Stephens](http://stephenslab.uchicago.edu/)
**IMPORTANT UPDATE:** We encourage CountClust users to use our new R
package, [fastTopics](https://github.com/stephenslab/fastTopics).
This new package has most of CountClust's features, plus several
important improvements, including model fitting algorithms that are
faster and more accurate.
## 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.
## Installation
`CountClust` requires the following CRAN-R packages: `slam`, `ggplot2`, `cowplot`, `parallel` along with the Bioconductor package: `limma`.
One can install `CountClust` from Bioc as follows
```
source("http://bioconductor.org/biocLite.R")
biocLite("CountClust")
```
For installing the working version of this package from Github please run
```
install_github('kkdey/CountClust')
```
To avoid bug issues for large datasets, we recommend the user to install the latest `maptpx` package from Github.
```
library(devtools)
install_github('TaddyLab/maptpx')
```
To replicate the data example in this README, please install the following data package.
```
install_github('kkdey/singleCellRNASeqMouseDeng2014')
```
Then load the `CountClust` package in R:
```
library(CountClust)
```
## Preprocessing
We load the single cell RNA-seq data due to [Deng et al 2014](http://www.ncbi.nlm.nih.gov/pubmed/24408435). 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)
```
## CountClust Model
We apply the `FitGoM` function (which is a wrapper of the `topics` function in the maptpx package) for a user specified number of clusters (K=6 for the example below).
```
FitGoM(t(deng.counts),K=3,path_rda="data/MouseDeng2014.FitGoM.rda")
```
This function will output a list - of which the key components are the two matrices `omega` and `theta`. `omega` denotes the matrix of cluster memberships for each sample and `theta` denotes the matrix of proportional contribution of each feature to a cluster.
## CountClust Visualization
### Structure Bar Plot
One can plot the `omega` from the `FitGoM` functionusing a Structure Bar 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$sample_id;
StructureGGplot(omega = omega,
annotation = annotation,
palette = RColorBrewer::brewer.pal(8, "Accent"),
yaxis_label = "Amplification batch",
order_sample = TRUE,
axis_tick = list(axis_ticks_length = .1,
axis_ticks_lwd_y = .1,
axis_ticks_lwd_x = .1,
axis_label_size = 7,
axis_label_face = "bold"))
```
### STRUCTURE Pie + tSNE/PCA
One can also visualize the STRUCTURE grades of membership colorings aggregated
with the t-SNE or the PCA plots, as follows.
t-SNE + CountClust grades coloring
```
StructurePie(t(deng.counts), input_type="apply_tsne",
use_voom=FALSE, omega = omega, xlab="TSNE1",
ylab = "TSNE2",
main = "STRUCTURE K=6 pie on tSNE",
control = list(bg = "lightcyan"))
```
PCA + CountClust grades coloring
```
StructurePie(t(deng.counts), input_type="apply_pca",
use_voom = TRUE, omega = omega, xlab="PCA1",
ylab = "PCA2",
main = "STRUCTURE K=6 pie on PCA",
control = list(bg = "lightcyan"))
```
## CountClust Cluster Annotations
We can extract the features that drive the clusters for K=6 as follows
```
theta_mat <- MouseDeng2014.topicFit$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)[1],
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.
## Licenses
The CountClust package is distributed under [GPL - General Public License (>= 2)]
## Contact
For any questions or comments, please contact [[email protected]]([email protected])
## Acknowledgements
- Raman Shah
- Peter Carbonetto