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1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations
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2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq
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3. These types of models have shown good performance with sparse data.
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+4. **Celda now includes DecontX, a computational algorithm for decontamination of droplet based scRNA-seq data.**
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## Installation Instructions
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-To install the most recent release of celda (used in the preprint version of the celda paper) via devtools:
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+To install the most recent release of celda via devtools:
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```
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library(devtools)
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-install_github("campbio/celda@v0.6")
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-```
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-The most up-to-date (but potentially less stable) version of celda can similarly be installed with:
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-```
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-install_github("campbio/celda@devel")
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+install_github("campbio/celda")
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```
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**NOTE** On OSX, devtools::install_github() requires installation of **libgit2.** This can be installed via homebrew:
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```
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brew install libgit2
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```
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-**NOTE** If you install celda in Rstudio and get an error:could not find tools necessary to compile a package, you can try this:
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+**NOTE** If you are trying to install celda using Rstudio and get this error: "could not find tools necessary to compile a package", you can try this:
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```
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options(buildtools.check = function(action) TRUE)
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```
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## Examples and vignettes
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-Vignettes are available in the package.
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-
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-An analysis example using celda with RNASeq via vignette('celda-analysis')
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-
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-### Decontamination with DecontX
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-Highly expressed genes from various cells clusters will be expressed at low levels in other clusters in droplet-based systems due to contamination. DecontX will decompose an observed count matrix into a decontaminated expression matrix and a contamination matrix. The only other parameter needed is a vector of cell cluster labels.
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-To simulate two 300 (gene) x 100 (cell) count matrices from 3 different cell types with total reads per cell ranged from 5000 to 40000: one matrix being ture expression matrix (rmat), the other matrix being contamination count matrix (cmat)
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-```
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-sim.con = simulateContaminatedMatrix( C = 100, G = 300, K = 3, N.Range= c(5000, 40000), seed = 9124)
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-true.contamination.percentage = colSums( sim.con$cmat ) / colSums( sim.con$cmat + sim.con$rmat )
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-str(sim.con)
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-# N.by.C: total transcripts per cell
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-# z: cell type label
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-```
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-Use DecontX to decompose the observed (contaminated) count matrix back into true expression matrix and a contamination matrix with specified cell label
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-```
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-observedCounts = sim.con$observedCounts
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-cell.label = sim.con$z
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-new.counts = DecontX( counts = observedCounts, z = cell.label, max.iter = 200, seed = 123)
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-str(new.counts)
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-# Decontaminated matrix: new.counts$res.list$est.rmat
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-# Percentage of contamination per cell: new.counts$res.list$est.conp
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-```
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-DecontX Performance check
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-```
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-estimated.contamination.percentage = new.counts$res.list$est.conp
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-plot( true.contamination.percentage, estimated.contamination.percentage) ; abline(0,1)
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-```
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-## New Features and announcements
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-The v0.4 release of celda represents a useable implementation of the various celda clustering models.
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-Please submit any usability issues or bugs to the issue tracker at https://github.com/campbio/celda
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+Uncompiled vignettes are available in the package.
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-You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)
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+Examples of doing single-cell RNA-seq data analysis using celda and DecontX is available in files vignettes/celda-analysis.Rmd and vignettes/DecontX-analysis.Rmd.
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## For developers
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Check out our [Wiki](https://github.com/campbio/celda/wiki) for [coding style guide](https://github.com/campbio/celda/wiki/Celda-Development-Coding-Style-Guide) if you want to contribute!
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