Next Steps

After you’ve run the Demultiplexing and/or Doublet Detecting softwares you would like, you can easily add the results to your single cell data with your analysis program of choice. This works best using the combined results files since they are well formatted for this function. Some common softwares used to analyze single cell data include Seurat (in R), Scanpy (in python) and the Loupe Browser (10x Genomics).

We’ve provided some example methods to add these results to single cell data structures in with each of these packages below.


The results can be added to a Seurat object in R.

First, open R from the Singularity image:

singularity exec Demuxafy.sif R

This is some basic code in R that will add the combined results to a Seurat object.

.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

## Read in the data
counts_matrix <- Read10X(data.dir = "/path/to/10x/matrix/directory/")

## Create a seurat object that contains the counts
seurat <- CreateSeuratObject(counts = counts_matrix, min.cells = 3, min.features = 200)

## Read in the demultiplexing and doublet detecting results
demuxafy <- read.table("/path/to/combined/results/combined_results_w_combined_assignments.tsv", sep = "\t", header=TRUE)
rownames(demuxafy) <- demuxafy$Barcode

## Add the demuxafy data to the Seurat object
seurat <- AddMetaData(seurat, demuxafy)

## Check that the data was correctly added

If the data was correctly added to the Seurat object, you should be able to see the data in the slot:

                      orig.ident nCount_RNA nFeature_RNA            Barcode
CGTTAGATCTAGAGTC-1 SeuratProject       3001          934 CGTTAGATCTAGAGTC-1
CAGGTGCAGGTCATCT-1 SeuratProject       1778          658 CAGGTGCAGGTCATCT-1
GTGCATAGTATAGGGC-1 SeuratProject       2224          925 GTGCATAGTATAGGGC-1
TTCGAAGTCCAGTAGT-1 SeuratProject       1268          428 TTCGAAGTCCAGTAGT-1
TAGTTGGGTCTCATCC-1 SeuratProject        981          476 TAGTTGGGTCTCATCC-1
CGCGGTAAGGATTCGG-1 SeuratProject       2982          710 CGCGGTAAGGATTCGG-1
                  Souporcell_DropletType Souporcell_Cluster
CGTTAGATCTAGAGTC-1                singlet                  7
CAGGTGCAGGTCATCT-1                singlet                  7
GTGCATAGTATAGGGC-1                singlet                  7
TTCGAAGTCCAGTAGT-1                singlet                  7
TAGTTGGGTCTCATCC-1                singlet                  7
CGCGGTAAGGATTCGG-1                singlet                  7
                  Souporcell_Individual_Assignment scds_score scds_DropletType
CGTTAGATCTAGAGTC-1                                7 0.70178412          singlet
CAGGTGCAGGTCATCT-1                                7 0.04977119          singlet
GTGCATAGTATAGGGC-1                                7 0.26765665          singlet
TTCGAAGTCCAGTAGT-1                                7 0.06354318          singlet
TAGTTGGGTCTCATCC-1                                7 0.12028167          singlet
CGCGGTAAGGATTCGG-1                                7 0.11765345          singlet
                  solo_DropletType solo_DropletScore
CGTTAGATCTAGAGTC-1          singlet           doublet
CAGGTGCAGGTCATCT-1          singlet           doublet
GTGCATAGTATAGGGC-1          singlet           doublet
TTCGAAGTCCAGTAGT-1          singlet           doublet
TAGTTGGGTCTCATCC-1          singlet           doublet
CGCGGTAAGGATTCGG-1          singlet           doublet
CGTTAGATCTAGAGTC-1                     singlet
CAGGTGCAGGTCATCT-1                     singlet
GTGCATAGTATAGGGC-1                     singlet
TTCGAAGTCCAGTAGT-1                     singlet
TAGTTGGGTCTCATCC-1                     singlet
CGCGGTAAGGATTCGG-1                     singlet
CGTTAGATCTAGAGTC-1                                     7
CAGGTGCAGGTCATCT-1                                     7
GTGCATAGTATAGGGC-1                                     7
TTCGAAGTCCAGTAGT-1                                     7
TAGTTGGGTCTCATCC-1                                     7
CGCGGTAAGGATTCGG-1                                     7


The results can be added to a AnnData object for analysis with Scanpy.

First, open python from the Singularity image:

singularity exec Demuxafy.sif python

This is some basic code in python that will add the combined results to a Scanpy.

import pandas as pd
import scanpy as sc
import numpy as np

### Read in the data to an AnnData object
adata = sc.read_10x_mtx("/path/to/10x/matrix/directory/")

### Read in the demultiplexing and doublet detecting results
demuxafy = pd.read_table("/path/to/combined/results/combined_results.tsv", sep="\t")

### Filter the AnnData object for droplet barcodes
adata = adata[np.isin(adata.obs.index,demuxafy["Barcode"])]

### Order the demuxafy droplets in same orderas the AnnDataa
adata_obs = pd.DataFrame(adata.obs)
adata_obs['Barcode'] = adata.obs.index

demuxafy_ordered = adata_obs.merge(demuxafy, on = "Barcode")
demuxafy_ordered.index = demuxafy_ordered["Barcode"]

### Add demuxafy data to the AnnData
adata.obs = demuxafy_ordered


The Demuxafy results from Combine_Results.R can be directly uploaded to the Loupe browser in the ‘Categories mode’. Simpley select ‘Import Categories’ and select the combined_results.tsv file to upload it and explore the annotation on your data. More detailed instructions are provided by 10x Genomics in the ‘Categories mode’ section of their Software Support


If you used the Demuxafy platform for analysis, please reference our preprint.