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scDblFinder Tutorial

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.

Data

This is the data that you will need to have preparede to run scDblFinder:

Required

  • A counts matrix ($MATRIX)
    • DoubletDetection expects counts to be in the cellranger output format (directory containint barcodes.tsv, genes.tsv and matrix.mtx or barcodes.tsv.gz, features.tsv.gz and matrix.mtx.gz)
      • If you don't have your data in this format, you can run scDblFinder manually in R and load the data in using a method of your choosing.
  • Output directory ($SCDBLFINDER_OUTDIR)
    • If you don't provide an $SCDBLFINDER_OUTDIR, the results will be written to the present working directory.

Run scDblFinder

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.

With Wrapper Script

singularity exec Demuxafy.sif scDblFinder.R -o $SCDBLFINDER_OUTDIR -t $MATRIX

Run 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.

singularity exec Demuxafy.sif R

That will open R in your terminal. Next, you can load all the libraries and run scDblFinder.

.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)
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")

scDblFinder Results and Interpretation

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.tsv

Here's a more detaild description of each of those files:

  • scDblFinder_doublet_summary.tsv

    • A sumamry of the number of singlets and doublets predicted by scDblFinder.

      Classification Droplet N
      doublet 3323
      singlet 17659
  • scDblFinder_doublets_singlets.tsv

    • The 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
      ... ... ...

Merging Results with Other Software Restults

We have provided a script that will help merge and summarize the results from multiple softwares together. See Combine Results <Combine-docs>.

Citation

If you used this workflow for analysis, please reference our paper (REFERENCE) as well as scDblFinder.