scDblFinder is a transcriptome-based doublet detecting method that uses doublet simulation from droplets in the dataset to identify doublets. We have provided a wrapper script that takes common arguments for ScDblFinder and also provide example code for you to run manually if you prefer.
This is the data that you will need to have prepare to run ScDblFinder:
Required
A counts matrix (
$COUNTS)The directory path containing your cellranger counts matrix files (directory containing
barcodes.tsv,genes.tsvandmatrix.mtxorbarcodes.tsv.gz,features.tsv.gzandmatrix.mtx.gz)or
h5 file (
filtered_feature_bc_matrix.h5)- If you don't have your data in this format, you can run ScDblFinder manually in R and load the data in using a method of your choosing.
Optional
- Output directory (
$SCDBLFINDER_OUTDIR)- If you don't provide an
$SCDBLFINDER_OUTDIR, the results will be written to the present working directory.
- If you don't provide an
- Filtered barcode file
- A list of barcodes that are a subset of the barcodes in your h5 or matrix.mtx files. This is useful if you have run other QC softwares such as CellBender or DropletQC to remove empty droplets or droplets with damaged cells.
- Expectation is that there is no header in this file
You can either run ScDblFinder with the wrapper script we have provided or you can run it manually if you would prefer to alter more parameters.
.. tabs::
.. tab:: With Wrapper Script
To run ScDblFinder_ with our wrapper script, simply execute the following in your shell:
.. code-block:: bash
singularity exec Demuxafy.sif scDblFinder.R -o $SCDBLFINDER_OUTDIR -t $COUNTS
.. admonition:: HELP! It says my file/directory doesn't exist!
:class: dropdown
If you receive an error indicating that a file or directory doesn't exist but you are sure that it does, this is likely an issue arising from Singularity.
This is easy to fix.
The issue and solution are explained in detail in the :ref:`Notes About Singularity Images <Singularity-docs>`
To see all the parameters that this wrapper script will accept, run:
.. code-block:: bash
singularity exec Demuxafy.sif scDblFinder.R -h
usage: scDblFinder.R file.
[-h] -o OUT -t TENX_MATRIX [-b BARCODES_FILTERED]
optional arguments:
-h, --help show this help message and exit
-o OUT, --out OUT The output directory where results will be saved
-t TENX_MATRIX, --tenX_matrix TENX_MATRIX
Path to the 10x filtered matrix directory or h5 file.
-b BARCODES_FILTERED, --barcodes_filtered BARCODES_FILTERED
Path to a list of filtered barcodes to use for doublet
detection.
.. tab:: Run in R
This section demonstrates how to run ScDblFinder_ manually in R.
First, you will have to start R.
We have built R and all the required software to run ScDblFinder_ into the singularity image so you can run it directly from the image.
.. code-block:: bash
singularity exec Demuxafy.sif R
That will open R in your terminal.
Next, you can load all the libraries and run ScDblFinder_.
.. code-block:: R
.libPaths("/usr/local/lib/R/site-library") ### This is required so that R uses the libraries loaded in the image and not any local libraries
library(scDblFinder)
library(Seurat)
library(SingleCellExperiment)
library(tidyverse)
## Set up variables and parameters ##
out <- "/path/to/scds/outdir/"
tenX_matrix <- "/path/to/counts/matrix/dir/"
dir.create(out, recursive = TRUE)
print(paste0("Using the following counts directory: ", tenX_matrix))
### Read in data as an sce object ###
counts <- Read10X(tenX_matrix, gene.column = 1) ## or Read10X_h5 if using h5 file as input
sce <- SingleCellExperiment(list(counts=counts))
## Calculate doublet ratio ###
doublet_ratio <- ncol(sce)/1000*0.008
### Calculate Singlets and Doublets ###
sce <- scDblFinder(sce, dbr=doublet_ratio)
### Make a dataframe of the results ###
results <- data.frame("Barcode" = rownames(colData(sce)), "scDblFinder_DropletType" = sce$scDblFinder.class, "scDblFinder_Score" = sce$scDblFinder.score)
write_delim(results, path = paste0(out,"/scDblFinder_doublets_singlets.tsv"), delim = "\t")
### Calculate number of doublets and singlets ###
summary <- as.data.frame(table(results$scDblFinder_DropletType))
colnames(summary) <- c("Classification", "Droplet N")
write_delim(summary, paste0(out,"/scDblFinder_doublet_summary.tsv"), "\t")
.. tab:: Run in R with Filtered Barcodes
This section demonstrates how to run ScDblFinder_ manually in R and includes code to help filter for a subset of barcodes that are in the single cell data.
First, you will have to start R.
We have built R and all the required software to run ScDblFinder_ into the singularity image so you can run it directly from the image.
.. code-block:: bash
singularity exec Demuxafy.sif R
That will open R in your terminal.
Next, you can load all the libraries and run ScDblFinder_.
.. code-block:: R
.libPaths("/usr/local/lib/R/site-library") ### This is required so that R uses the libraries loaded in the image and not any local libraries
library(scDblFinder)
library(Seurat)
library(SingleCellExperiment)
library(tidyverse)
## Set up variables and parameters ##
out <- "/path/to/scds/outdir/"
tenX_matrix <- "/path/to/counts/matrix/dir/"
filtered_barcodes_file <- "/path/to/counts/filtered/barcodes/file.tsv" ## can also be gzipped
dir.create(out, recursive = TRUE)
print(paste0("Using the following counts directory: ", tenX_matrix))
### Read in data as an sce object and filtered barcodes ###
counts <- Read10X(tenX_matrix, gene.column = 1) ## or Read10X_h5 if using h5 file as input
filtered_barcodes <- read_delim(args$barcodes_filtered, delim = "\t", col_names = "Barcodes")
## Filter for the barcodes list of interest
if (is.list(counts)){
counts <- counts[[grep("Gene", names(counts))]][, colnames(counts[[grep("Gene", names(counts))]]) %in% filtered_barcodes$Barcodes]
} else {
counts <- counts[, colnames(counts) %in% filtered_barcodes$Barcodes]
}
## Create ingle cell experiment object
sce <- SingleCellExperiment(list(counts=counts))
## Calculate doublet ratio ###
doublet_ratio <- ncol(sce)/1000*0.008
### Calculate Singlets and Doublets ###
sce <- scDblFinder(sce, dbr=doublet_ratio)
### Make a dataframe of the results ###
results <- data.frame("Barcode" = rownames(colData(sce)), "scDblFinder_DropletType" = sce$scDblFinder.class, "scDblFinder_Score" = sce$scDblFinder.score)
write_delim(results, path = paste0(out,"/scDblFinder_doublets_singlets.tsv"), delim = "\t")
### Calculate number of doublets and singlets ###
summary <- as.data.frame(table(results$scDblFinder_DropletType))
colnames(summary) <- c("Classification", "Droplet N")
write_delim(summary, paste0(out,"/scDblFinder_doublet_summary.tsv"), "\t")
After running the ScDblFinder with the wrapper script or manually you should have two files in the $SCDBLFINDER_OUTDIR:
.
├── scDblFinder_doublets_singlets.tsv
└── scDblFinder_doublet_summary.tsvHere's a more detaild description of each of those files:
scDblFinder_doublet_summary.tsvA sumamry of the number of singlets and doublets predicted by ScDblFinder.
Classification
Droplet N
doublet
3323
singlet
17659
- To check whether the numbe of doublets identified by ScDblFinder is consistent with the expected doublet rate expected based on the number of droplets that you captured, you can use our Expected Doublet Estimation Calculator.
scDblFinder_doublets_singlets.tsvThe per-barcode singlet and doublet classification from ScDblFinder.
Barcode
scDblFinder_DropletType
scDblFinder_Score
AAACCTGAGATAGCAT-1
singlet
0.0033526041079312563
AAACCTGAGCAGCGTA-1
doublet
0.9937564134597778
AAACCTGAGCGATGAC-1
singlet
5.045032594352961e-
AAACCTGAGCGTAGTG-1
singlet
0.007504515815526247
AAACCTGAGGAGTTTA-1
singlet
0.00835108570754528
AAACCTGAGGCTCATT-1
singlet
0.028838597238063812
AAACCTGAGGGCACTA-1
doublet
0.9985504746437073
AAACCTGAGTAATCCC-1
singlet
0.005869860760867596
...
...
...
We have provided a script that will help merge and summarize the results from multiple softwares together. See :ref:`Combine Results <Combine-docs>`.
If you used the Demuxafy platform for analysis, please reference our preprint as well as ScDblFinder.