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Marijn 2020-03-26 14:09:20 +01:00
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README.md
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@ -30,42 +30,39 @@ library(Matrix)
library(Seurat) library(Seurat)
library(qlcMatrix) library(qlcMatrix)
library(FastCAR) library(FastCAR)
```
Specify the locations of the expression matrices
``` ```
```
cellExpressionFolder = c("Cellranger_output/sample1/filtered_feature_bc_matrix/") cellExpressionFolder = c("Cellranger_output/sample1/filtered_feature_bc_matrix/")
fullMatrixFolder = c("Cellranger_output/sample1/raw_feature_bc_matrix/") fullMatrixFolder = c("Cellranger_output/sample1/raw_feature_bc_matrix/")
```
Set a location for storing the corrected cell/gene matrix
``` ```
```
# This folder will contain the corrected cell matrix
correctedMatrixFolder = c("Cellranger_output/sample1/corrected_feature_bc_matrix") correctedMatrixFolder = c("Cellranger_output/sample1/corrected_feature_bc_matrix")
```
Load both the cell matrix and the full matrix
```
cellMatrix = read.cell.matrix(cellExpressionFolder) cellMatrix = read.cell.matrix(cellExpressionFolder)
fullMatrix = read.full.matrix(fullMatrixFolder) fullMatrix = read.full.matrix(fullMatrixFolder)
``` ```
The following functions give an idea of the effect that different settings have on the ambient RNA profile.
The following functions give an idea of the effect that different settings have on the ambient RNA profile. These are optional as they do take a few minutes and the default settings work fine
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, 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) ambProfile = describe.ambient.RNA.sequence(fullCellMatrix = fullMatrix,
start = 10,
stop = 500,
by = 10,
contaminationChanceCutoff = 0.05)
plot.ambient.profile(ambProfile) plot.ambient.profile(ambProfile)
``` ```
![picture](Images/Example_profile.png) ![picture](Images/Example_profile.png)
Set the empty droplet cutoff and the contamination chance cutoff 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. The empty droplet cutoff is the number of UMIs a droplet can contain at the most to be considered empty.
@ -79,17 +76,14 @@ In a cluster of a thousand cells divided into two groups there would be 2-3 cell
Such low cell numbers are disregarded for differential expression analyses. Such low cell numbers are disregarded for differential expression analyses.
``` ```
emptyDropletCutoff = 100 emptyDropletCutoff = 100
contaminationChanceCutoff = 0.05 contaminationChanceCutoff = 0.05
``` ```
Determine the ambient RNA profile and remove the ambient RNA from each cell
``` ```
ambientProfile = determine.background.to.remove(fullMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff) ambientProfile = determine.background.to.remove(fullMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff)
cellMatrix = remove.background(cellMatrix, ambientProfile) 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. 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.
@ -100,14 +94,12 @@ 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 ## Authors
* **Marijn Berg** - *Initial work* * **Marijn Berg** - m.berg@umcg.nl
## License ## License