Outputs

Single-cell ATAC outputs are found in the standard DRAGEN output location using the prefix scATAC.

The following three files provide information per-cell gene expression level in matrix market (*.mtx) format:

Option

Description

<prefix>.multiomics.matrix.mtx.gz

Count of unique UMIs or fragments for each cell/feature pair in sparse matrix format.

<prefix>.multiomics.barcodes.tsv.gz

Cell-barcode sequence for each cell from the matrix. This includes all cell-barcodes.

<prefix>.multiomics.features.tsv.gz

Feature name and ID for each feature in the matrix.

The subset of barcodes corresponding to passing cells can be found under the Filter column in <prefix>.multiomics.barcodeSummary.tsv indicated by values PASS and FAIL.

The output includes filtered matrix files which only include the per-cell feature count level for the filtered cells in matrix market (*.mtx) format. The multiomics.features.tsv.gz file is common for the unfiltered and filtered matrices:

Option

Description

<prefix>.multiomics.filtered.matrix.mtx.gz

Count of unique UMIs for each filtered cell/feature pair in sparse matrix format.

<prefix>.multiomics.filtered.barcodes.tsv.gz

Cell-barcode sequence for each filtered cell from the matrix.

Loading the matrix in a dense dataframe using python allows you to create an output in a readable format. Illumina recommends a sparse representation of the matrix due to the significant usage of memory and disk space of dense matrices. Several tools are available to work efficiently with "sparse" representations of single cell matrices. Illumina tested the loading the matrix in python 3.10.0 using scanpy 1.9.3 and pandas 1.5.3 tools.

Follow the steps below:

1. Enter the following command to install the required libraries:

> pip install -U scanpy pandas

2. Use the following python commands to load the matrix in dense representation:

# import libraries

import pandas as pd

import scanpy as sc

# define path to input files

matrix_path = "path/to/matrix.mtx.gz"

features_path = "path/to/features.tsv.gz"

barcodes_path = "path/to/barcodes.tsv.gz"

# load matrix through scanpy

adata = sc.read_mtx(matrix_path).T

adata.var_names = pd.read_csv(features_path, sep="\t", header=None, compression="gzip")[1]

adata.obs_names = pd.read_csv(barcodes_path, sep="\t", header=None, compression="gzip")[0]

# convert scanpy internal format (AnnData) to dense pandas DataFrame

df = pd.DataFrame(adata.X.todense(), index=adata.obs_names, columns=adata.var_names)

# save it as CSV file

df.to_csv("output_matrix.csv")

The matrix can be saved through different output formats (eg, CSV), although it is not recommended due to high disk usage.

DRAGEN Single-Cell Multiomic outputs two BAM files sorted by coordinate - one with suffix scRNA.bam and one with suffix scATAC.bam. Consult the corresponding section, scATAC or scRNA. of the user guide for more details.

Because the DRAGEN aligner processes RNA and ATAC reads, there will be a separate mapper metrics summary for each modality when running in multiomic mode. The RGID field will be either RNA or ATC based on the read. There is not a common map/align metric summary for all input reads because it would merge the two modalities. The <prefix>.mapping_metrics.csv file will also reflect the modality.

The following is an example of the alignment metrics printed at the end of a DRAGEN Single-Cell Multiomic run:

MAPPING/ALIGNING PER RG rna Total reads in RG 52319680 100.00

MAPPING/ALIGNING PER RG rna Number of duplicate marked reads 0 0.00

MAPPING/ALIGNING PER RG rna Number of duplicate marked and mate reads removed NA

MAPPING/ALIGNING PER RG rna Number of unique reads (excl. duplicate marked reads) 52319680 100.00

...

MAPPING/ALIGNING PER RG atac Total reads in RG 203484254 100.00

MAPPING/ALIGNING PER RG atac Number of duplicate marked reads 0 0.00

MAPPING/ALIGNING PER RG atac Number of duplicate marked and mate reads removed NA

MAPPING/ALIGNING PER RG atac Number of unique reads (excl. duplicate marked reads) 203484254 100.00

...

The <prefix>.multiomics.metrics.csv file contains per sample scATAC and scRNA metrics. For more details, please consult with the corresponding section from scATAC/scRNA user guide.

Here is an example of how a <prefix>.multiomics.metrics.csv file can look like:

SINGLE-CELL ATAC METRICS,lib1,Invalid barcode fragments,0

SINGLE-CELL ATAC METRICS,lib1,Error free cell-barcode,308656

SINGLE-CELL ATAC METRICS,lib1,Error corrected cell-barcode,251364

...

SINGLE-CELL RNA METRICS,lib1,Median genes per cell,1456

SINGLE-CELL RNA METRICS,lib1,Total genes detected,14128

SINGLE-CELL METRICS,lib1,Passing cells,1920

The <prefix>.multiomics.barcodeSummary.tsv contains summary statistics for each unique cell-barcode per cell after error correction. Here is an example of how a <prefix>.multiomics.barcodeSummary.tsv file can look like:

Copy 
ID  Barcode           BarcodeScAtac     BarcodeScRna      UniqueFragments  TotalFragments  PeakFragments  NonPrimaryContigFragments  ChimericFragments  LowMapqFragments  MitochondrialFragments  Peaks  TotalReads  GeneReads  UMIs  Genes  MitochondrialReads  Filter
1   AAACAAGCAAACAAAG  AAACAAGCAAACAAAG  GGCTAGTGTTCGGTAA  22               48              13             17                         0                  2                 7                       9      59          0          0     0      0                   LOW
2   AAACAAGCAAACCAGC  AAACAAGCAAACCAGC  GGCTAGTGTACTGAAT  7                7               3              0                          0                  0                 0                       3      8           0          0     0      0                   LOW