@@ -18,12 +18,19 @@ if (!requireNamespace("BiocManager", quietly = TRUE))

 BiocManager::install("celda")

 ```

-To install the development version (R >= 3.6) of **celda** from GitHub using `devtools`:

+The latest stable version  of **celda** can be installed from GitHub using `devtools`:

+

 ```

 library(devtools)

 install_github("campbio/celda")

 ```

+The development version of **celda** can also be installed from GitHub using `devtools`:

+```

+library(devtools)

+install_github("campbio/celda@devel")

+```

+

 **NOTE** For MAC OSX users, `devtools::install_github()` requires installation of **libgit2.** This can be installed via homebrew:

 ```

 brew install libgit2

@@ -1,5 +1,7 @@

-[![Build Status](https://travis-ci.com/campbio/celda.svg?branch=master)](https://travis-ci.com/campbio/celda)

+<!-- badges: start -->

+[![R-CMD-check](https://github.com/campbio/celda/workflows/R-CMD-check/badge.svg)](https://github.com/campbio/celda/actions)

 [![Coverage Status](https://coveralls.io/repos/github/campbio/celda/badge.svg?branch=master)](https://coveralls.io/github/campbio/celda?branch=master)

+<!-- badges: end -->

 # celda: CEllular Latent Dirichlet Allocation

@@ -1,4 +1,4 @@

-[![Build Status](https://travis-ci.org/campbio/celda.svg?branch=master)](https://travis-ci.org/campbio/celda)

+[![Build Status](https://travis-ci.com/campbio/celda.svg?branch=master)](https://travis-ci.com/campbio/celda)

 [![Coverage Status](https://coveralls.io/repos/github/campbio/celda/badge.svg?branch=master)](https://coveralls.io/github/campbio/celda?branch=master)

 # celda: CEllular Latent Dirichlet Allocation

@@ -31,6 +31,10 @@ Also, if you receive installation errors when Rcpp is being installed and compil

 https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/

+If you are running R 4.0.0 or later version on MacOS Catalina and you see error `'wchar.h' file not found`, you can try the method in this link:

+

+https://discourse.mc-stan.org/t/dealing-with-catalina-iii/12731/5

+

 **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:

 ```

 options(buildtools.check = function(action) TRUE)

@@ -3,12 +3,7 @@

 # celda: CEllular Latent Dirichlet Allocation

-"celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation". It is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

-

-1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

-2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

-3. These types of models have shown good performance with sparse data.

-4. **Celda now includes DecontX, a computational algorithm for decontamination of droplet based scRNA-seq data.**

+"celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation". It is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. This package also includes a method called **DecontX** which can be used to estimate and remove contamination in single cell genomic data. 

 ## Installation Instructions

@@ -27,32 +22,34 @@ library(devtools)

 install_github("campbio/celda")

 ```

-For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`.

+**NOTE** For MAC OSX users, `devtools::install_github()` requires installation of **libgit2.** This can be installed via homebrew:

 ```

-library(devtools)

-install_github("campbio/celda@R_3_5")

+brew install libgit2

 ```

-There has recently been a major update to variable/function names in the **celda** package. For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of **celda**, please install from the mirror branch `20190409_master` which is the release before package reformatting:

-```

-library(devtools)

-install_github("campbio/celda@20190409_master")

-```

+Also, if you receive installation errors when Rcpp is being installed and compiled, try following the steps outlined here to solve the issue:

+

+https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/

-**NOTE** On OSX, `devtools::install_github()` requires installation of **libgit2.** This can be installed via homebrew:

-```

-brew install libgit2

-```

 **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:

 ```

 options(buildtools.check = function(action) TRUE)

 ```

-## Examples and vignettes

+## Vignettes and examples

-The vignette in HTML format showing how to use **celda** is available on Bioconductor [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/celda-analysis.html).

+To build the vignettes for Celda and DecontX during installation from GitHub, use the following command:

+

+```

+library(devtools)

+install_github("campbio/celda", build_vignettes = TRUE)

+```

-Example vignette of doing single-cell RNA-seq data decontamination using DecontX is available [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/DecontX-analysis.html).

+Note that installation may take an extra 5-10 minutes for building of the vignettes. The Celda and DecontX vignettes can then be accessed via the following commands:

+```

+vignette("celda")

+vignette("decontX")

+```

 ## For developers

 Check out our [Wiki](https://github.com/campbio/celda/wiki) for developer's guide if you want to contribute!

@@ -55,4 +55,9 @@ The vignette in HTML format showing how to use **celda** is available on Biocond

 Example vignette of doing single-cell RNA-seq data decontamination using DecontX is available [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/DecontX-analysis.html).

 ## For developers

-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!

+Check out our [Wiki](https://github.com/campbio/celda/wiki) for developer's guide if you want to contribute!

+- [Celda Development Coding Style Guide](https://github.com/campbio/celda/wiki/Celda-Development-Coding-Style-Guide)

+- [Celda Development Robust and Efficient Code](https://github.com/campbio/celda/wiki/Celda-Development-Robust-and-Efficient-Code)

+- [Celda Development Rstudio configuration](https://github.com/campbio/celda/wiki/Celda-Development-Rstudio-configuration)

+- [FAQ on how to use celda](https://github.com/campbio/celda/wiki/FAQ-on-how-to-use-celda)

+- [FAQ on package development](https://github.com/campbio/celda/wiki/FAQ-on-package-development)

@@ -27,7 +27,7 @@ library(devtools)

 install_github("campbio/celda")

 ```

-For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`. **NOTE:** This branch is no longer updated. Please use `R-3.6` versions.

+For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`.

 ```

 library(devtools)

 install_github("campbio/celda@R_3_5")

@@ -27,7 +27,7 @@ library(devtools)

 install_github("campbio/celda")

 ```

-For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`.

+For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`. **NOTE:** This branch is no longer updated. Please use `R-3.6` versions.

 ```

 library(devtools)

 install_github("campbio/celda@R_3_5")

@@ -50,7 +50,7 @@ options(buildtools.check = function(action) TRUE)

 ## Examples and vignettes

-The vignette in HTML format is available on Bioconductor [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/celda-analysis.html).

+The vignette in HTML format showing how to use **celda** is available on Bioconductor [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/celda-analysis.html).

 Example vignette of doing single-cell RNA-seq data decontamination using DecontX is available [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/DecontX-analysis.html).

@@ -13,19 +13,27 @@

 ## Installation Instructions

-To install the most recent release of celda (R >= 3.6) via devtools:

+To install the latest stable release of **celda** from [Bioconductor](http://bioconductor.org/packages/celda/) (requires R version >= 3.6):

+

+```

+if (!requireNamespace("BiocManager", quietly = TRUE))

+    install.packages("BiocManager")

+BiocManager::install("celda")

+```

+

+To install the development version (R >= 3.6) of **celda** from GitHub using `devtools`:

 ```

 library(devtools)

 install_github("campbio/celda")

 ```

-For R3.5 users, please install from the R_3_5 branch. This version of celda is identical to the most recent release of celda except it also works on R3.5.

+For `R-3.5` users, please install from the `R_3_5` branch. This version of **celda** is identical to the most recent release of **celda** (`master` branch) except it also works on `R-3.5`.

 ```

 library(devtools)

 install_github("campbio/celda@R_3_5")

 ```

-There has recently been a major update to variable/function names in the celda package. For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of celda, please install from the mirror branch `20190409_master` which is the release before package reformatting:

+There has recently been a major update to variable/function names in the **celda** package. For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of **celda**, please install from the mirror branch `20190409_master` which is the release before package reformatting:

 ```

 library(devtools)

 install_github("campbio/celda@20190409_master")

@@ -35,16 +43,16 @@ install_github("campbio/celda@20190409_master")

 ```

 brew install libgit2

 ```

-**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:

+**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:

 ```

 options(buildtools.check = function(action) TRUE)

 ```

 ## Examples and vignettes

-Uncompiled vignettes are available in the package. 

+The vignette in HTML format is available on Bioconductor [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/celda-analysis.html).

-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.

+Example vignette of doing single-cell RNA-seq data decontamination using DecontX is available [here](http://bioconductor.org/packages/release/bioc/vignettes/celda/inst/doc/DecontX-analysis.html).

 ## For developers

 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!

@@ -19,7 +19,7 @@ library(devtools)

 install_github("campbio/celda")

 ```

-For R3.5 users, please install from the R_3_5 branch. This version of celda is identical to the most recent release of celda except it works on R3.5.

+For R3.5 users, please install from the R_3_5 branch. This version of celda is identical to the most recent release of celda except it also works on R3.5.

 ```

 library(devtools)

 install_github("campbio/celda@R_3_5")

@@ -3,7 +3,7 @@

 # celda: CEllular Latent Dirichlet Allocation

-"celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

+"celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation". It is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

 1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

 2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

@@ -19,13 +19,13 @@ library(devtools)

 install_github("campbio/celda")

 ```

-For R3.5 users, please install from the R_3_5 branch:

+For R3.5 users, please install from the R_3_5 branch. This version of celda is identical to the most recent release of celda except it works on R3.5.

 ```

 library(devtools)

 install_github("campbio/celda@R_3_5")

 ```

-For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of celda, please install from the mirror branch 20190409_master:

+There has recently been a major update to variable/function names in the celda package. For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of celda, please install from the mirror branch `20190409_master` which is the release before package reformatting:

 ```

 library(devtools)

 install_github("campbio/celda@20190409_master")

@@ -13,12 +13,24 @@

 ## Installation Instructions

-To install the most recent release of celda via devtools:

+To install the most recent release of celda (R >= 3.6) via devtools:

 ```

 library(devtools)

 install_github("campbio/celda")

 ```

+For R3.5 users, please install from the R_3_5 branch:

+```

+library(devtools)

+install_github("campbio/celda@R_3_5")

+```

+

+For backward compatibility with results (`celda_CG` and `celda_list` objects) generated from older versions of celda, please install from the mirror branch 20190409_master:

+```

+library(devtools)

+install_github("campbio/celda@20190409_master")

+```

+

 **NOTE** On OSX, `devtools::install_github()` requires installation of **libgit2.** This can be installed via homebrew:

 ```

 brew install libgit2

@@ -19,7 +19,7 @@ library(devtools)

 install_github("campbio/celda")

 ```

-**NOTE** On OSX, devtools::install_github() requires installation of **libgit2.** This can be installed via homebrew:

+**NOTE** On OSX, `devtools::install_github()` requires installation of **libgit2.** This can be installed via homebrew:

 ```

 brew install libgit2

 ```

@@ -8,71 +8,31 @@

 1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

 2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

 3. These types of models have shown good performance with sparse data.

+4. **Celda now includes DecontX, a computational algorithm for decontamination of droplet based scRNA-seq data.**

 ## Installation Instructions

-To install the most recent release of celda (used in the preprint version of the celda paper) via devtools:

+To install the most recent release of celda via devtools:

 ```

 library(devtools)

-install_github("campbio/celda@v0.6")

-```

-The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

-```

-install_github("campbio/celda@devel")

+install_github("campbio/celda")

 ```

 **NOTE** On OSX, devtools::install_github() requires installation of **libgit2.** This can be installed via homebrew:

 ```

 brew install libgit2

 ```

-**NOTE** If you install celda in Rstudio and get an error:could not find tools necessary to compile a package, you can try this:

+**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:

 ```

 options(buildtools.check = function(action) TRUE)

 ```

 ## Examples and vignettes

-Vignettes are available in the package. 

-

-An analysis example using celda with RNASeq via vignette('celda-analysis')

-

-

-### Decontamination with DecontX

-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. 

-

-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)

-```

-sim.con = simulateContaminatedMatrix( C = 100, G = 300, K = 3, N.Range= c(5000, 40000), seed = 9124) 

-true.contamination.percentage = colSums( sim.con$cmat ) / colSums( sim.con$cmat + sim.con$rmat ) 

-str(sim.con)   

-# N.by.C: total transcripts per cell 

-# z: cell type label 

-

-```

-Use DecontX to decompose the observed (contaminated) count matrix back into true expression matrix and a contamination matrix with specified cell label

-```

-observedCounts = sim.con$observedCounts

-cell.label = sim.con$z

-new.counts = DecontX( counts = observedCounts, z = cell.label,  max.iter = 200, seed = 123) 

-str(new.counts) 

-# Decontaminated matrix: new.counts$res.list$est.rmat

-# Percentage of contamination per cell: new.counts$res.list$est.conp

-

-```

-DecontX Performance check 

-```

-estimated.contamination.percentage = new.counts$res.list$est.conp

-plot( true.contamination.percentage, estimated.contamination.percentage) ; abline(0,1) 

-``` 

-

-

-

-## New Features and announcements

-The v0.4 release of celda represents a useable implementation of the various celda clustering models.

-Please submit any usability issues or bugs to the issue tracker at https://github.com/campbio/celda

+Uncompiled vignettes are available in the package. 

-You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

+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.

 ## For developers

 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!

@@ -73,3 +73,6 @@ The v0.4 release of celda represents a useable implementation of the various cel

 Please submit any usability issues or bugs to the issue tracker at https://github.com/campbio/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

+

+## For developers

+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!

@@ -26,6 +26,10 @@ install_github("campbio/celda@devel")

 ```

 brew install libgit2

 ```

+**NOTE** If you install celda in Rstudio and get an error:could not find tools necessary to compile a package, you can try this:

+```

+options(buildtools.check = function(action) TRUE)

+```

 ## Examples and vignettes

@@ -1,5 +1,5 @@

-[![Build Status](https://travis-ci.org/compbiomed/celda.svg?branch=master)](https://travis-ci.org/compbiomed/celda)

-[![Coverage Status](https://coveralls.io/repos/github/compbiomed/celda/badge.svg?branch=devel)](https://coveralls.io/github/compbiomed/celda?branch=devel)

+[![Build Status](https://travis-ci.org/campbio/celda.svg?branch=master)](https://travis-ci.org/campbio/celda)

+[![Coverage Status](https://coveralls.io/repos/github/campbio/celda/badge.svg?branch=master)](https://coveralls.io/github/campbio/celda?branch=master)

 # celda: CEllular Latent Dirichlet Allocation

@@ -66,6 +66,6 @@ plot( true.contamination.percentage, estimated.contamination.percentage) ; ablin

 ## New Features and announcements

 The v0.4 release of celda represents a useable implementation of the various celda clustering models.

-Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

+Please submit any usability issues or bugs to the issue tracker at https://github.com/campbio/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -42,17 +42,15 @@ To simulate two 300 (gene) x 100 (cell) count matrices from 3 different cell typ

 sim.con = simulateContaminatedMatrix( C = 100, G = 300, K = 3, N.Range= c(5000, 40000), seed = 9124) 

 true.contamination.percentage = colSums( sim.con$cmat ) / colSums( sim.con$cmat + sim.con$rmat ) 

 str(sim.con)   

-# rmat: simulated true expression (gene by cell) count matrix

-# cmat: simulated contamination (gene by cell) count matrix 

 # N.by.C: total transcripts per cell 

 # z: cell type label 

 ```

 Use DecontX to decompose the observed (contaminated) count matrix back into true expression matrix and a contamination matrix with specified cell label

 ```

-observed.mat = sim.con$rmat + sim.con$cmat 

+observedCounts = sim.con$observedCounts

 cell.label = sim.con$z

-new.counts = DecontX( counts = observed.mat, z = cell.label,  max.iter = 200, seed = 123) 

+new.counts = DecontX( counts = observedCounts, z = cell.label,  max.iter = 200, seed = 123) 

 str(new.counts) 

 # Decontaminated matrix: new.counts$res.list$est.rmat

 # Percentage of contamination per cell: new.counts$res.list$est.conp

@@ -38,7 +38,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 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. 

 ```

-new.counts = DecontX( omat = counts, z = cell.label) 

+new.counts = DecontX( counts = counts, z = cell.label) 

 # Decontaminated matrix: new.counts$res.list$est.rmat

 # Percentage of contamination per cell: new.counts$res.list$est.conp

 ```

@@ -15,11 +15,11 @@

 To install the most recent release of celda (used in the preprint version of the celda paper) via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.6")

+install_github("campbio/celda@v0.6")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

-install_github("compbiomed/celda")

+install_github("campbio/celda@devel")

 ```

 **NOTE** On OSX, devtools::install_github() requires installation of **libgit2.** This can be installed via homebrew:

@@ -37,11 +37,34 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ### Decontamination with DecontX

 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. 

+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)

 ```

-new.counts = DecontX( omat = counts, z = cell.label) 

+sim.con = simulateObservedMatrix( C = 100, G = 300, K = 3, N.Range= c(5000, 40000), seed = 9124) 

+true.contamination.percentage = colSums( sim.con$cmat ) / colSums( sim.con$cmat + sim.con$rmat ) 

+str(sim.con)   

+# rmat: simulated true expression (gene by cell) count matrix

+# cmat: simulated contamination (gene by cell) count matrix 

+# N.by.C: total transcripts per cell 

+# z: cell type label 

+

+```

+Use DecontX to decompose the observed (contaminated) count matrix back into true expression matrix and a contamination matrix with specified cell label

+```

+observed.mat = sim.con$rmat + sim.con$cmat 

+cell.label = sim.con$z

+new.counts = DecontX( omat = observed.mat, z = cell.label,  max.iter = 200, seed = 123) 

+str(new.counts) 

 # Decontaminated matrix: new.counts$res.list$est.rmat

 # Percentage of contamination per cell: new.counts$res.list$est.conp

+

 ```

+DecontX Performance check 

+```

+estimated.contamination.percentage = new.counts$res.list$est.conp

+plot( true.contamination.percentage, estimated.contamination.percentage) ; abline(0,1) 

+``` 

+

+

 ## New Features and announcements

 The v0.4 release of celda represents a useable implementation of the various celda clustering models.

@@ -34,6 +34,14 @@ Vignettes are available in the package.

 An analysis example using celda with RNASeq via vignette('celda-analysis')

+### Decontamination with DecontX

+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. 

+

+```

+new.counts = DecontX( omat = counts, z = cell.label) 

+# Decontaminated matrix: new.counts$res.list$est.rmat

+# Percentage of contamination per cell: new.counts$res.list$est.conp

+```

 ## New Features and announcements

 The v0.4 release of celda represents a useable implementation of the various celda clustering models.

@@ -12,10 +12,10 @@

 ## Installation Instructions

-To install the most recent beta release of celda via devtools:

+To install the most recent release of celda (used in the preprint version of the celda paper) via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.5")

+install_github("compbiomed/celda@v0.6")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -15,7 +15,7 @@

 To install the most recent beta release of celda via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.4")

+install_github("compbiomed/celda@v0.5")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -1,4 +1,5 @@

 [![Build Status](https://travis-ci.org/compbiomed/celda.svg?branch=master)](https://travis-ci.org/compbiomed/celda)

+[![Coverage Status](https://coveralls.io/repos/github/compbiomed/celda/badge.svg?branch=devel)](https://coveralls.io/github/compbiomed/celda?branch=devel)

 # celda: CEllular Latent Dirichlet Allocation

@@ -21,6 +21,11 @@ The most up-to-date (but potentially less stable) version of celda can similarly

 install_github("compbiomed/celda")

 ```

+**NOTE** On OSX, devtools::install_github() requires installation of **libgit2.** This can be installed via homebrew:

+```

+brew install libgit2

+```

+

 ## Examples and vignettes

 Vignettes are available in the package. 

@@ -14,7 +14,7 @@

 To install the most recent beta release of celda via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.3.1")

+install_github("compbiomed/celda@v0.4")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -30,7 +30,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

-The v0.3.1 release of celda represents a useable implementation of the various celda clustering models.

+The v0.4 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -14,7 +14,7 @@

 To install the most recent beta release of celda via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.3")

+install_github("compbiomed/celda@v0.3.1")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -30,7 +30,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

-The v0.3 release of celda represents a useable implementation of the various celda clustering models.

+The v0.3.1 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

old mode 100644

new mode 100755

@@ -30,7 +30,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

-The v0.2 release of celda represents a useable implementation of the various celda clustering models.

+The v0.3 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -30,7 +30,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

-The v0.2 release of celda represents a useable implementation of the various celda clustering models.

+The v0.3 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -11,10 +11,10 @@

 ## Installation Instructions

-To install the beta release of celda via devtools:

+To install the most recent beta release of celda via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.2")

+install_github("compbiomed/celda@v0.3")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -14,7 +14,7 @@

 To install the beta release of celda via devtools:

 ```

 library(devtools)

-install_github("compbiomed/celda@v0.1")

+install_github("compbiomed/celda@v0.2")

 ```

 The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

 ```

@@ -30,7 +30,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

-The v0.1 release of celda represents a useable implementation of the various celda clustering models.

+The v0.2 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

 You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -16,7 +16,10 @@ To install the beta release of celda via devtools:

 library(devtools)

 install_github("compbiomed/celda@v0.1")

 ```

-

+The most up-to-date (but potentially less stable) version of celda can similarly be installed with:

+```

+install_github("compbiomed/celda")

+```

 ## Examples and vignettes

@@ -1,3 +1,5 @@

+[![Build Status](https://travis-ci.org/compbiomed/celda.svg?branch=master)](https://travis-ci.org/compbiomed/celda)

+

 # celda: CEllular Latent Dirichlet Allocation

 "celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

@@ -27,3 +27,5 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

 The v0.1 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

+

+You can discuss celda, or ask the developers usage questions, in our [Google Group.](https://groups.google.com/forum/#!forum/celda-list)

@@ -1,10 +1,10 @@

 # celda: CEllular Latent Dirichlet Allocation

-"celda" stands for "***CE***llular ***L***atent ***D***irichlet ***A***llocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

+"celda" stands for "**CE**llular **L**atent **D**irichlet **A**llocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

-# Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

-# Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

-# These types of models have shown good performance with sparse data.

+1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

+2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

+3. These types of models have shown good performance with sparse data.

 ## Installation Instructions

@@ -7,13 +7,6 @@

 # These types of models have shown good performance with sparse data.

-## Examples and vignettes

-

-Vignettes are available in the package. 

-

-An analysis example using celda with RNASeq via vignette('celda-analysis')

-

-

 ## Installation Instructions

 To install the beta release of celda via devtools:

@@ -22,6 +15,15 @@ library(devtools)

 install_github("compbiomed/celda@v0.1")

 ```

+

+## Examples and vignettes

+

+Vignettes are available in the package. 

+

+An analysis example using celda with RNASeq via vignette('celda-analysis')

+

+

+

 ## New Features and announcements

 The v0.1 release of celda represents a useable implementation of the various celda clustering models.

 Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

@@ -1,13 +1,16 @@

-# celda: CELlular Latent Dirichlet Allocation

+# celda: CEllular Latent Dirichlet Allocation

+

+"celda" stands for "***CE***llular ***L***atent ***D***irichlet ***A***llocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

+

+# Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations

+# Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq

+# These types of models have shown good performance with sparse data.

-Description

 ## Examples and vignettes

 Vignettes are available in the package. 

-An Introduction of the package via vignette('celda-intro').

-

 An analysis example using celda with RNASeq via vignette('celda-analysis')

@@ -10,12 +10,15 @@ An Introduction of the package via vignette('celda-intro').

 An analysis example using celda with RNASeq via vignette('celda-analysis')

-## New Features and announcements

-

 ## Installation Instructions

+To install the beta release of celda via devtools:

 ```

 library(devtools)

-install_github("definitelysean/celda")

+install_github("compbiomed/celda@v0.1")

 ```

+

+## New Features and announcements

+The v0.1 release of celda represents a useable implementation of the various celda clustering models.

+Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda

@@ -1,5 +1,3 @@

-# celda

-

 # celda: CELlular Latent Dirichlet Allocation

 Description

@@ -17,5 +15,7 @@ An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## Installation Instructions

+```

 library(devtools)

 install_github("definitelysean/celda")

+```

@@ -8,9 +8,9 @@ Description

 Vignettes are available in the package. 

-See Introduction of the package via vignette('celda-intro').

+An Introduction of the package via vignette('celda-intro').

-See a analysis example using celda with RNASeq via vignette('celda-analysis')

+An analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

@@ -6,8 +6,10 @@ Description

 ## Examples and vignettes

-Vignettes are available in the package. \n

+Vignettes are available in the package. 

+

 See Introduction of the package via vignette('celda-intro').

+

 See a analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

@@ -6,7 +6,7 @@ Description

 ## Examples and vignettes

-Vignettes are available in the package. 

+Vignettes are available in the package. \n

 See Introduction of the package via vignette('celda-intro').

 See a analysis example using celda with RNASeq via vignette('celda-analysis')

@@ -6,7 +6,9 @@ Description

 ## Examples and vignettes

-Vignettes are available in the package via vignette('celda-intro').

+Vignettes are available in the package. 

+See Introduction of the package via vignette('celda-intro').

+See a analysis example using celda with RNASeq via vignette('celda-analysis')

 ## New Features and announcements

@@ -1,2 +1,17 @@

 # celda