DoubletDecon is a transcription-based doublet detection software that uses deconvolution to identify doublets using the R statistical software. We have provided a wrapper script that takes common arguments for DoubletDecon 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 DoubletDecon:
Required
- A QC-filtered and normalized seurat object saved as an
rdsobject ($SEURAT_RDS)- For example, using the Seurat Vignette
- If you run DoubletDecon manually, you can use any data format of interest and read in with a method that works for your data.
- Output directory (
$DOUBLETDECON_OUTDIR)
You can either run DoubletDecon 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
.. admonition:: Note
Since it is hard to predict the correct *rhop* to use for each dataset, we typically run a range.
For example: 0.6, 0.7, 0.8, 0.9, 1, and 1.1.
Then we select the results that predict the number of doublets closest to the expected doublet number.
You can estimate that number with our **doublet calculator**
The *rhop* parameter can be set with ``-r`` or ``--rhop`` in the command below.
.. code-block:: bash
singularity exec Demuxafy.sif DoubletDecon.R -o $DOUBLETDECON_OUTDIR -s $SEURAT_RDS
You can provide many other parameters as well which can be seen from running a help request:
.. code-block:: bash
singularity exec image DoubletDecon.R -h
usage: DoubletDecon.R [-h] -o OUT -s SEURAT_OBJECT [-g NUM_GENES] [-r RHOP]
[-p SPECIES] [-n NCORES] [-c REMOVECC] [-m PMF]
[-f HEATMAP] [-t CENTROIDS] [-d NUM_DOUBS] [-5 ONLY50]
[-u MIN_UNIQ]
optional arguments:
-h, --help show this help message and exit
-o OUT, --out OUT The output directory where results will be saved
-s SEURAT_OBJECT, --seurat_object SEURAT_OBJECT
A QC, normalized seurat object with classifications/clusters as Idents() saved as an rds object.
-g NUM_GENES, --num_genes NUM_GENES
Number of genes to use in 'Improved_Seurat_Pre_Process' function.
-r RHOP, --rhop RHOP rhop to use in DoubletDecon - the number of SD from the mean to identify upper limit to blacklist
-p SPECIES, --species SPECIES
The species of your sample. Can be scientific species name, KEGG ID, three letter species abbreviation, or NCBI ID.
-n NCORES, --nCores NCORES
The number of unique cores you would like to use to run DoubletDecon. By default, uses one less than available detected.
-c REMOVECC, --removeCC REMOVECC
Whether to remove clusters enriched in cell cycle genes.
-m PMF, --pmf PMF Whether to use unique gene expression in doublet determination.
-f HEATMAP, --heatmap HEATMAP
Whether to generate heatmaps.
-t CENTROIDS, --centroids CENTROIDS
Whether to use centroids instead of medoids for doublet detecting.
-d NUM_DOUBS, --num_doubs NUM_DOUBS
The number of doublets to simulate for each cluster pair.
-5 ONLY50, --only50 ONLY50
Whether to only compute doublets as 50:50 ratio. Default is to use other ratios as well.
-u MIN_UNIQ, --min_uniq MIN_UNIQ
Minimum number of unique genes to rescue a cluster identified as doublets.
.. tab:: Run in R
First, you will have to start R.
We have built R and all the required software to run DoubletDecon_ 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 DoubletDecon_.
.. 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(DoubletDecon)
library(tidyverse)
library(Seurat)
library(ggplot2)
library(data.table)
## Set up variables ##
out <- "/path/to/doubletdecon/outdir"
SEURAT_RDSect <- "/path/to/preprocessed/SEURAT_RDSect.rds"
## make sure the directory exists ###
dir.create(out, recursive = TRUE)
## Read in Data ##
seurat <- readRDS(SEURAT_RDSect)
## Preprocess ##
processed <- Improved_Seurat_Pre_Process(seurat, num_genes=50, write_files=FALSE)
## Run Doublet Decon ##
results <- Main_Doublet_Decon(rawDataFile = processed$newExpressionFile,
groupsFile = processed$newGroupsFile,
filename = "DoubletDecon_results",
location = paste0(out, "/"),
fullDataFile = NULL,
removeCC = FALSE,
species = "hsa",
rhop = 0.9, ## We recommend testing multiple rhop parameters to find which fits your data the best
write = TRUE,
PMF = TRUE,
useFull = FALSE,
heatmap = FALSE,
centroids=FALSE,
num_doubs=100,
only50=FALSE,
min_uniq=4,
nCores = 1)
doublets <- read.table(paste0(out, "/Final_doublets_groups_DoubletDecon_results.txt"))
doublets$Barcode <- gsub("\\.", "-",rownames(doublets))
doublets$DoubletDecon_DropletType <- "doublet"
doublets$V1 <- NULL
doublets$V2 <- NULL
singlets <- read.table(paste0(out, "/Final_nondoublets_groups_DoubletDecon_results.txt"))
singlets$Barcode <- gsub("\\.", "-",rownames(singlets))
singlets$DoubletDecon_DropletType <- "singlet"
singlets$V1 <- NULL
singlets$V2 <- NULL
doublets_singlets <- rbind(singlets,doublets)
fwrite(doublets_singlets, paste0(out, "/DoubletDecon_doublets_singlets.tsv"), sep = "\t", append = FALSE)
### Make a summary of the number of singlets and doublets
summary <- as.data.frame(table(doublets_singlets$DoubletDecon_DropletType))
colnames(summary) <- c("Classification", "Droplet N")
fwrite(summary, paste0(out,"/DoubletDecon_doublet_summary.tsv"), sep = "\t", append = FALSE)
After running the DoubletDecon, you will have multiple files in the $DOUBLETDECON_OUTDIR:
.
├── data_processed_DoubletDecon_results.txt
├── data_processed_reclust_DoubletDecon_results.txt
├── DoubletDecon_doublets_singlets.tsv
├── DoubletDecon_doublet_summary.tsv
├── DoubletDecon_results.log
├── DRS_doublet_table_DoubletDecon_results.txt
├── DRS_results_DoubletDecon_results.txt
├── Final_doublets_exp_DoubletDecon_results.txt
├── Final_doublets_groups_DoubletDecon_results.txt
├── Final_nondoublets_exp_DoubletDecon_results.txt
├── Final_nondoublets_groups_DoubletDecon_results.txt
├── groups_processed_DoubletDecon_results.txt
├── groups_processed_reclust_DoubletDecon_results.txt
├── new_PMF_results_DoubletDecon_results.txt
├── resultsreadable_synths.txt
└── Synth_doublet_info_DoubletDecon_results.txtDoubletDecon puts most of the results in multiple separate files. However, the wrapper script and the example code has some steps to combine these results together into a single file, which will likely be the most informative output. These are the files that we think will be the most helpful for users:
DoubletDecon_doublet_summary.tsvA summary of the number of singlets and doublets predicted by DoubletDecon.
Classification
Droplet N
doublet
1510
singlet
19470
- To check whether the numbe of doublets identified by DoubletDecon 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.
DoubletDecon_doublets_singlets.tsvThe per-barcode singlet and doublet classification from DoubletDecon.
Barcode
DoubletDecon_DropletType
AAACCTGAGCAGCGTA-1
singlet
AAACCTGAGCGATGAC-1
singlet
AAACCTGAGCGTAGTG-1
singlet
AAACCTGAGGCTCATT-1
singlet
AAACCTGAGTAGCCGA-1
singlet
...
...
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 paper (REFERENCE) as well as DoubletDecon.