Updated Readme

FastCAR

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-Subproject commit 2121cbd359630af53c002dd3e082621010111346

FastCAR.Rproj

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+Version: 1.0
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+Encoding: UTF-8
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+LaTeX: pdfLaTeX
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+BuildType: Package
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+PackageInstallArgs: --no-multiarch --with-keep.source

NAMESPACE

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README.md

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 # FastCAR
 
-Repo for the FastCAR (Fast Correction of Ambient RNA) R package and maybe  eventual python library
+FastCAR is an R package to remove ambient RNA from cells in droplet based single cell RNA sequencing data.
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
+
+### Prerequisites
+
+What things you need to install the software and how to install them
+
+```
+Give examples
+```
+
+### Installing
+
+FastCAR can be install from git with the following command.
+
+```
+devtools::install_git("https://git.web.rug.nl/P278949/FastCAR")
+```
+
+Running FastCAR is quite simple.
+First load the library and dependencies.
+
+```
+library(Matrix)
+library(Seurat)
+library(qlcMatrix)
+library(FastCAR)
+
+```
+
+
+
+```
+
+cellExpressionFolder  = c("Cellranger_output/sample1/filtered_feature_bc_matrix/")
+fullMatrixFolder      = c("Cellranger_output/sample1/raw_feature_bc_matrix/")
+
+```
+
+
+
+```
+# This folder will contain the corrected cell matrix
+correctedMatrixFolder = c("Cellranger_output/sample1/corrected_feature_bc_matrix")
+
+
+cellMatrix     = read.cell.matrix(cellExpressionFolder)
+fullMatrix     = read.full.matrix(fullMatrixFolder)
+
+```
+
+The following functions give an idea of the effect that different settings have on the ambient RNA profile.
+Plotting the number of empty droplets, the number of genes identified in the ambient RNA, and the number of genes that will be corrected for at different UMI cutoffs,
+
+```
+ambProfile = describe.ambient.RNA.sequence(fullCellMatrix = fullMatrix, start = 10, stop = 500, by = 10, contaminationChanceCutoff = 0.05)
+plot.ambient.profile(ambProfile)
+
+``` 
+![picture](https://git.web.rug.nl/P278949/FastCAR/src/branch/master/Images/Example_profile.png)
+
+
+
+Set the empty droplet cutoff and the contamination chance cutoff
+
+The empty droplet cutoff is the number of UMIs a droplet can contain at the most to be considered empty.
+100 works fine but we tested this method in only one tissue. For other tissues this might not be the.
+Increasing this number also increases the highest possible value of expression of a given gene.
+As the correction will remove this value from every cell it is adviced not to set this too high and thereby overcorrect the expression in lowly expressing cells.
+
+The contamination chance cutoff is the allowed probability of a gene contaminating a cell. 
+As we developed FastCAR specifically for differential expression analyses between groups we suggest setting this such that not enough cells could be contaminated to affect this.
+In a cluster of a thousand cells divided into two groups there would be 2-3 cells per group with ambient RNA contamination of any given gene.
+Such low cell numbers are disregarded for differential expression analyses.
+
+```
+
+emptyDropletCutoff        = 100 
+contaminationChanceCutoff = 0.05
+
+```
+
+```
+ambientProfile = determine.background.to.remove(fullMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff)
+cellMatrix     = remove.background(cellMatrix, ambientProfile)
+
+
+```
+
+Finally write the corrected cell/gene matrix to a file, this matrix can be used in Seurat the same way as any other cell/gene matrix.
+
+```
+
+write.corrected.matrix(cellMatrix, correctedMatrixFolder, ambientProfile)
+
+```
+
+End with an example of getting some data out of the system or using it for a little demo
+
+## Running the tests
+
+
+## Authors
+
+* **Marijn Berg** - *Initial work* 
+
+## License
+
+This project is licensed under the GPL-3 License - see the [LICENSE.md](LICENSE.md) file for details
+