Illumina Connected Software

scRNA

The DRAGEN Single-Cell RNA (scRNA) Pipeline can process multiplexed single-cell RNA-Seq data sets from reads to a cell-by-gene UMI count gene expression matrix. The pipeline is compatible with library designs that have one read in a fragment matched to a transcript and the other containing a cell-barcode and UMI. The pipeline includes the following functions:

  • RNA-Seq (splice-aware) alignment and matching to annotated genes for the transcript reads.

  • Cell-barcode and UMI error correction for the barcode read.

  • UMI counting per cell and gene to measure gene expression.

  • Sparse matrix output and QC metrics.

  • Feature counting, such as with cell-surface proteins.

The functionality and options related to alignment and gene annotation are identical to the RNA-Seq pipeline. For information, see DRAGEN RNA Pipeline. Other RNA-Seq modules, such as gene fusion calling or transcript-level gene expression quantification are not supported for Single-Cell RNA.

Input Files

Alignment Reference

Use a standard DRAGEN RNA reference genome or hashtable for the scRNA Pipeline. For example, build using --ht-build-rna-hashtable=true. The pipeline also requires a gene annotation file in GTF format, provided with the --annotation (-a) option.

Read Input

The DRAGEN scRNA Pipeline requires both the transcript sequence and the barcode+UMI sequence for each fragment (read) as input. The transcript sequence is aligned to the genome to determine the expressed gene, the barcode+UMI sequence is split into the single-cell barcode to identify the unique cell, and a single-cell UMI for unique molecule quantification. When starting from FASTQ, you can either include the UMI in the read name or provide separate UMI FASTQ files.

UMI in Read Name

Provide the transcriptome reads as a single-end FASTQ file with the Barcode+UMI sequence in the eighth field of the read-name line. Separate sequences using a colon. The following example uses read2 (sample.R2.fastq.gz) as the transcript read.

@D00626:253:CAT5WANXX:4:1302:8433:85343:GAAACTCGTTCAGCGCTCATCTCGGC  
ACAGGTCAGCTGGGAGCTTCTGCCCCCACTGCCTAGGGACCAACAGGGGCAGGAGGCAGTCACTGACCCCGAGAAGTTTGCATCCTGCACAGCTAGAGATC  
+  
CCCCBFGGGGGGGGGGGGGGGGGGBDCGGGE>1BGDFGG0F@FBGGGBCDGGGGGGGGGGGGGGGGGGGGGGGDGGGGGGGGGGGGGFGGGGFGEGGGGGE

In the example, the GAA sequence is the barcode+UMI read and the ACAG sequence is the transcriptome read.

These FASTQ files can be generated by bclConvert and bcl2fastq using the UMI settings to define the single-cell barcode+UMI read. If using bclConvert, enter the barcode/UMI information using the OverrideCycles1 setting. For more information, see the BCL Convert Software Guide (document # 1000000094725).

Note: bclConvert refers to the entire single-cell barcode+UMI sequence as UMI.

Enter the following command line option to use the generated FASTQ files from bclConvert:

dragen -1 <file name> --umi-source=qname

The option is also compatible with the --fastq-list input options and with read input from BAM files.

Single-cell RNA Fastq-list Files

A single-cell RNA fastq-list file is a CSV file with the following mandatory columns:

ColumnDescription

Lane

Sequencing lane

RGID

Read group ID

RGSM

Read group sample

RGLB

Read group library

Read1File

Read 1 FASTQ file (usually FASTQ file with reads containing cell-barcodes and UMIs)

Read2File

Read 2 FASTQ file (usually transcriptomic reads)

In this case, DRAGEN needs to accept --umi-source=read1 (or --umi-source=read2 if swapped) command-line option. For example, a fastq-list file with the contents shown below must be used in combination with --umi-source=read1 option:

Lane,RGID,RGSM,RGLB,Read1File,Read2File
1,rna,sample1,illumina1,scRNA1.R1.fastq.gz,scRNA1.R2.fastq.gz
2,rna,sample1,illumina2,scRNA2.R1.fastq.gz,scRNA2.R2.fastq.gz

Alternatively, the FASTQ file with transcriptomic reads can be specified under Read1File column and the FASTQ file with cell-barcodes and/or UMIs - under UmiFile column. For example, a fastq-list file with the contents shown below must be used in combination with --umi-source=umifile option:

Lane,RGID,RGSM,RGLB,Read1File,UmiFile
1,rna,sample1,illumina1,scRNA1.R2.fastq.gz,scRNA1.R1.fastq.gz
2,rna,sample1,illumina2,scRNA2.R2.fastq.gz,scRNA2.R1.fastq.gz

Separate UMI Fastq Files

An alternative option is to provide the transcript and barcode+UMI sequences as two separate FASTQ files. One file contains only the transcriptome reads and one contains the corresponding barcode-reads in the same order. This file is similar to how read-pairs are normally handled. If using separate UMI files, the sequencing system run setup and bclConvert are not aware of the UMI and treat it as normal read sequence by default.

Enter the following command line option to use the separate UMI FASTQ files:

dragen -1 <file name> --umi-fastq=<file name> --umi-source=fastq

To use this method with multiple FASTQ files:

  1. Enter the barcode+UMI FASTQ files as read1 in the fastq-list file, and then enter the transcriptome read FASTQ files, matching the default fastq_list.csv generated by bclConvert, as read2.

  2. Use the following command:

dragen --fastq-list fastq_list.csv --umi-source=read1

Using Multiple Libraries

The scRNA pipeline can process a single biological sample per DRAGEN run. To process multiple single-cell libraries together, split the single sample into multiple single-cell libraries with a unique set of cells in each. DRAGEN keeps the cells (barcodes and UMIs) from each library separate and provides merged outputs across all. Read groups are used to specify the library for each FASTQ file using the RGLB attribute.

DRAGEN Single-cell Settings

To use the scRNA workflow, use the options --enable-rna=true --enable-single-cell-rna=true. This section includes information on additional scRNA settings.

Barcode Position

By default the scRNA workflow assumes that the overall barcode/UMI sequence is made up of a single-cell barcode (possibly split into multiple blocks) and a single UMI. Enter the following command to define the location of the single-cell barcode and single-cell UMI in the barcode read:

--scrna-barcode-position <blockPos>[+<blockPos>+<blockPos>...][(:-)|(:+)] --scrna-umi-position <blockPos>

blockPos describes the offset of the first and last inclusive base of the block and is formatted as <startPos>_<endPos>. For example, for a library with a 16 bp cell-barcode followed by a 10 bp UMI, use: --scrna-barcode-position 0_15 --scrna-umi-position 16_25. For a library with the cell-barcode split into three blocks of 9 bp separated by fixed linker sequences and an 8 bp UMI, use: --scrna-barcode-position=0_8+21_29+43_51 --scrna-umi-position=52_59.

By default, the barcode position is assumed to be indicated on the forward strand. To explicitly specify the forward strand, use: --scrna-barcode-position 0_15:+ or --scrna-barcode-position=0_8+21_29+43_51:+. To explicitly specify the reverse strand, use: --scrna-barcode-position 0_15:- or --scrna-barcode-position=0_8+21_29+43_51:-.

UMI position can also be specified for feature reads, which are reads with a sequence tag specific to a feature (eg, cell-surface protein or antibody). When the feature-specific UMIs are located on Read 2, you can use --scrna-feature-barcode-r2umi=0_11 to specify a 12 bp feature UMI at the beginning of each feature read.

Known Barcode Lists

You can provide a list of cell barcode sequences to include using the following command:

--scrna-barcode-sequence-list </path/to/barcodeAllowlist.txt>

In the case where the --scrna-barcode-position parameter is not split into multiple blocks (see Barcode Position section) the file must contain one possible cell barcode sequence per line. Differently, when the barcode position is split into multiple blocks, the file must contain a list composed by multiple sections (one for each block): each section must indicate the possible cell barcode block sequences for the corresponding block. Each section should start with a line with prefix #-, e.g.:

#-Block1
<barcode_block_1_sequence_1>
<barcode_block_1_sequence_2>
...
#-Block2
<barcode_block_2_sequence_1>
<barcode_block_2_sequence_2>
...

The input file can also be provided compressed with gzip (*.txt.gz).

During cell-barcode error correction any observed barcodes that do not match a sequence specified in the file are considered errors. If possible, the barcodes are corrected to a similar allowed sequence. See Barcode Error Correction for more information. If the barcodes cannot be corrected, they are filtered out.

Cell Filtering

DRAGEN uses a threshold on the total count of unique UMIs (or reads) per cell barcode, to determine which barcodes are likely to correspond to single-cells in the original sample, instead of background noise. The threshold is determined based on the distribution of counts along barcodes and on the expected number of true cells in the sample.

  • --single-cell-number-cells --- [Optional] Set the expected number of cells. The default is 3000. Adjust only if the expected number of cells is so far from the default that DRAGEN does not call the correct cell filtering threshold automatically.

  • --single-cell-threshold --- Specify the method for determining the count threshold value. The available values are fixed, ratio, or inflection.

    • If using ratio, DRAGEN estimates the expected number of cells as max(T_e, T_m). T_m is a threshold based on a fraction of the counts seen in most abundant cell-barcodes. T_e is a threshold based on a fraction of the least abundant expected cell.

    • If using inflection, DRAGEN estimates the count threshold by analyzing inflection points in the cumulative distribution of counts.

    • If using fixed, the count threshold is set to force the expected number of cells (--single-cell-number-cells option), rather than estimating it from the data. The exact number of passing cells might be slightly larger than the number of requested single-cells because several cells in the tail of the count distribution can have the same count.

  • --single-cell-threshold-filterby --- [Optional] Set the count distribution to consider for cell filtering. Can be either "umi" (default) or "read".

To set a specific, fixed number of cells, rather than use the automatically determined threshold, use the following command:

--single-cell-threshold=fixed --single-cell-number-cells=X

The command forces DRAGEN to select the top X cells and extra cells with the same number of counts of the last selected cell.

Additional Options

The following are additional options you can use to configure the Single-Cell RNA Pipeline settings.

  • --rna-library-type --- Set the orientation of transcript reads relative to the genomes. Enter SF for forward, SR for reverse, or U for unstranded. The default is SF.

  • --scrna-count-introns --- Include intronic reads in gene expression estimation. The default is false.

  • --qc-enable-depth-metrics --- Set to false to disable depth metrics for faster run time. The default is true.

  • --bypass-anchor-mapping --- Set to true to disable RNA anchor (two-pass) mapping for increased performance. The default is false.

Command-line Example

The following is an example command line to run the DRAGEN Single Cell RNA Pipeline.

dragen \
--enable-rna=true \
--enable-single-cell-rna=true \
--umi-source=fastq \
--scrna-barcode-position 0_15 \
--scrna-umi-position 16_25 \
-r reference_genomes/Mus_musculus/mm10/DRAGEN/8 \
-a reference_genomes/Mus_musculus/mm10/gtf/gencode.vM23.annotation.gtf.gz \
-1 lib1_S7_L001_R2_001.fastq.gz \
--umi-fastq lib1_S7_L001_R1_001.fastq.gz \
--RGID=1 \
--RGSM=sample1 \
--output-dir=/staging/out \
--output-file-prefix=sample1

Outputs

Single-cell RNA outputs are found in the standard DRAGEN output location using the prefix <sample>. in case of a single library and the prefix <sample>.<libId>. in case of multiple libraries. All single-cell RNA output files contain word scRNA in their names.

Counts

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

  • <prefix>.scRNA.matrix.mtx.gz

    • Count of unique UMIs for each cell/gene pair in sparse matrix format.

  • <prefix>.scRNA.barcodes.tsv.gz

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

  • <prefix>.scRNA.genes.tsv.gz

    • Gene name and ID for each gene in the matrix.

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

The output includes filtered matrix files which only include the per-cell gene expression for the PASS cells in matrix market (*.mtx) format. The scRNA.genes.tsv.gz files is common for the unfiltered and filtered matrices:

  • <prefix>.scRNA.filtered.matrix.mtx.gz

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

  • <prefix>.scRNA.filtered.barcodes.tsv.gz

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

Loading output in a dense matrix

Some users might want to explore the output matrix in a human-readable format. To do so, a possible way would be to load the matrix in a "dense" dataframe in python (similar methodologies can be used in alternative programming languages). It is important to remember, however, that when possible a "sparse" representation of the matrix is preferable, 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 (e.g., scanpy in python).

The matrix can be converted into a "dense" representation through two python modules: scanpy and pandas. This has been tested with python 3.10.0, scanpy 1.9.3, pandas 1.5.3.

First, it is necessary to install the required libraries:

> pip install -U scanpy pandas

Within python, the matrix can be loaded in "dense" representation using the following commands:

# import libraries
import pandas as pd
import scanpy as sc

# define path to input files
matrix_path = "path/to/matrix.mtx.gz"
genes_path = "path/to/genes.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(genes_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 (e.g., CSV), although this is not recommended due to high disk usage.

Alignments

Alignments of the transcript reads are sorted by coordinate and output as a BAM file. Each alignment is annotated with an XB tag containing the cell-barcode and an RX tag containing the UMI. The alignments use the original sequences without any errors corrected. Fragments that do not have an associated barcode read, for example fragments trimmed on the input data, do not have XB and RX tags.

Overall Metrics

The <prefix>.scRNA_metrics.csv file contains per sample scRNA metrics. In scRNA, mapped reads are currently reported by default under R1 metrics, irrespective of whether R1 is the read aligned to the transcriptome or the one containing cell barcode and UMI.

Barcode Read Metrics

  • Invalid barcode read: Overall barcode sequence (cell barcode + UMI) failed basic checks. For example, the barcode read was missing or too short.

  • Error free cell-barcode: Reads with cell-barcode sequences that were not altered during error correction. For example, if the read was an exact match to the allow list.

  • Error corrected cell-barcode: Reads with cell-barcode sequences successfully corrected to a valid sequence.

  • Filtered cell-barcode: Reads with cell-barcode sequences that could not be corrected to a valid sequence. For example, the sequence does not match allow list with at most one mismatch.

Transcript/Feature Read Metrics

  • Unique exon match: Reads with valid cell-barcode and UMI that match a unique gene.

  • Unique intron match: Reads do not match exons, but introns of exactly one gene. For example, if using the command --scrna-count-introns=true.

  • Ambiguous match: Reads match to multiple genes.

  • Wrong strand: Reads overlap a gene on the opposite strand defined by library type.

  • Mitochondrial reads: Reads map to the mitochondrial example, if there is a matching gene.

  • No gene match: Reads do not match to any gene. Includes intronic reads unless using --scrna-count-introns=true.

  • Filtered multimapper: Reads excluded due to multiple alignment positions in the genome.

  • Feature reads: Reads matching to features, when using feature counting.

UMI count metrics

  • Total counted reads: Reads with valid cell-barcode and UMI, matching a unique gene

  • Reads with error-corrected UMI: Counted reads where the UMI was error-corrected to match another similar UMI sequence.

  • Reads with invalid UMI: Reads that were not counted due to invalid UMI sequence. For example, pure homopolymer reads or reads containing Ns.

  • Sequencing saturation: Fraction of reads with duplicate UMIs. 1 - ( UMIs / Reads).

  • Unique cell-barcodes: Overall number of unique cell-barcode sequences in counted reads only.

  • Unique UMIs: Overall number of unique cell-barcode and UMI combinations counted.

Cell Metrics

  • UMI threshold for passing cells: Number of UMIs required for a cell-barcode to pass filtering.

  • Passing cells: Number of cell-barcodes that passed the filters.

  • Fraction genic reads in cells: Counted reads assigned to cells that passed the filters.

  • Fraction reads in putative cells: All counted reads assigned to cells that passed the filters.

  • Median reads per cells: Total counted reads per cell that passed the filters.

  • Median UMIs per cells: Total counted UMIs per cell that passed the filters.

  • Median genes per cells: Genes with at least one UMI per cell that passed the filters.

  • Total genes detected: Genes with at least one UMI in at least one cell that passed the filters.

Per-cell metrics

The <prefix>.scRNA.barcodeSummary.tsv contains summary statistics for each unique cell-barcode per cell after error correction.

  • ID: Unique numeric ID for the cell-barcode. The ID corresponds to the line number of that barcode in the UMI count matrix (*.mtx) output.

  • Barcode: The cell-barcode sequence.

  • TotalReads: Total reads with the cell-barcode sequence. This includes error corrected reads.

  • GeneReads: Reads (primary read alignments) counted towards a gene.

  • UMIs: Total number of UMIs in counted reads.

  • Genes: Unique genes detected.

  • Mitochondrial Reads: Reads mapped to mitochondrial genome.

  • Filter: The following are the available filter values:

    • PASS: Cell-barcode passes the filter.

    • LOW: UMI count is below threshold.

Barcode error correction

Cell-barcode sequences from the input reads are error corrected based on their frequency, and optionally through a list of expected cell-barcode sequences. A cell-barcode sequence is corrected into another cell-barcode sequence if they differ only by one base (Hamming distance 1) and:

  • Either the corrected cell-barcode is at least two times more frequent across all input reads

  • Or the corrected cell-barcode is on the list of expected cell-barcode sequences, but the original cell-barcode is not

When corrected, all the original cell-barcode reads are assigned to the corrected cell-barcode. The sequence error correction scheme is similar to the directional algorithm described in (Smith, Heger and Sudbery, 2020)¹.

¹Smith, T., Heger, A. and Sudbery, I., 2020. UMI-Tools: Modeling Sequencing Errors In Unique Molecular Identifiers To Improve Quantification Accuracy. [PDF] Cold Spring Harbor Laboratory Press. Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340976/> [Accessed 15 October 2020].

To avoid overcounting UMIs based on sequence errors, UMI error correction is performed among all reads with the same cell-barcode mapping to the same gene. UMI sequences that are likely errors of another UMI are not counted.

Genotype Sample Demultiplexing

DRAGEN implements several strategies for demultiplexing data sets that represent mixtures of cells from different individuals, such as cells pooled in one library prep or microfluidic run. Two of these strategies include a genotype-based and genotype-free demultiplexing. In genotype demultiplexing methods, DRAGEN can assign sample identity to cells based on alleles observed in reads in each cell (only SNVs are considered). DRAGEN flags any doublets, such as droplets that contain multiple cells from different individuals.

To use genotype-based sample demultiplexing, you must provide a VCF file with genotypes for each sample in the data set. To use genotype-free sample demultiplexing, you must provide a VCF file with a set of external samples preferably coming from a population with the same genetic background. The GT field represents the sample genotypes.

For information on the cell-hashing demultiplexing method, see Cell-Hashing.

Command-Line Options

You can use the following command line options for scRNA demultiplexing.

  • One of the two:

    • --scrna-demux-sample-vcf — If using genotype-based sample demultiplexing, specify the VCF file that contains the sample genotypes.

    • --scrna-demux-reference-vcf — If using genotype-free sample demultiplexing, specify the VCF file that contains the genotypes of a population with a similar genetic background to the samples you are using.

  • --scrna-demux-detect-doublets — Enable the doublet detection in genotype-based sample demultiplexing. The default value is false.

  • --scrna-demux-number-samples — The number of samples you are using. This option is only applicable when using an external VCF reference specified with the scrna-demux-reference-vcf option, for genotype-free sample demultiplexing.

The following is an example command line to run the DRAGEN Single Cell RNA Pipeline with genotype-based demultiplexing.

dragen \
--enable-rna=true \
--enable-single-cell-rna=true \
--umi-source=fastq \
--scrna-barcode-position 0_15 \
--scrna-umi-position 16_25 \
-r reference_genomes/Mus_musculus/mm10/DRAGEN/8 \
-a reference_genomes/Mus_musculus/mm10/gtf/gencode.vM23.annotation.gtf.gz \
-1 lib1_S7_L001_R2_001.fastq.gz \
--umi-fastq lib1_S7_L001_R1_001.fastq.gz \
--RGID=1 \
--RGSM=sample1 \
--output-dir=/staging/out \
--output-file-prefix=sample1 \
--scrna-demux-detect-doublet=true \
--scrna-demux-sample-vcf=sample.vcf

The following is an example command line to run the DRAGEN Single Cell RNA Pipeline with genotype-free demultiplexing.

dragen \
--enable-rna=true \
--enable-single-cell-rna=true \
--umi-source=fastq \
--scrna-barcode-position 0_15 \
--scrna-umi-position 16_25 \
-r reference_genomes/Mus_musculus/mm10/DRAGEN/8 \
-a reference_genomes/Mus_musculus/mm10/gtf/gencode.vM23.annotation.gtf.gz \
-1 lib1_S7_L001_R2_001.fastq.gz \
--umi-fastq lib1_S7_L001_R1_001.fastq.gz \
--RGID=1 \
--RGSM=sample1 \
--output-dir=/staging/out \
--output-file-prefix=sample1 \
--scrna-demux-detect-doublet=true \
--scrna-demux-reference-vcf=sample.vcf \
--scrna-demux-number-samples=4

Outputs

You can find information related to the output of genotype-based scRNA sample demultiplexing in the following three files.

The <prefix>.scRNA.barcodeSummary.tsv file contains per-cell metrics, including cell barcodes. The following columns contain information on demultiplexing per-cell. See Outputs for more information on <prefix>.scRNA.barcodeSummary.tsv metrics.

ColumnDescription

SampleIdentity

The SampleIdentity column can contain the following values::

  • sampleX—The particular cell (barcode) is uniquely assigned to a sample.

  • AMB(sampleX,sampleY)—The algorithm cannot determine the sample to assign the barcode to.

  • MIX(mixing_coef*sampleX+(100-mixing_coef)*sampleY)—The cell barcode is classified as doublet. For example, MIX(50*sampleX+50*sampleY).

IdentityQscore

The IdentityQscore column contains the value used to estimate the confidence of the sample identity call. After DRAGEN determines the doublet status of the cell as singlet, ambiguous, or doublet, the identity Q-score is defined as -10 * log10(Probability that the assigned identity is correct, given the second most likely identity and the doublet status). The higher values of identity Q-score correspond to more confident sample identity calls.

The <prefix>.scRNA.demux.tsv file contains sample demultiplexing statistics that were used to infer sample identity of each cell.

ColumnDescription

Barcode

The cell barcode associated with the cell.

DemuxSNPCount

The number of SNPs that the reads of the cell barcode intersect.

DemuxReadCount

Number of UMIs of the cell barcode that intersect at least one SNP.

Pure samples

Samples from the VCF file.

BestMixtureIdentity

Mixture sample with the highest log likelihood. Only available if --single-cell-demux-detect-doublets=true.

BestMixtureLogLikelihood

The log likelihood of the best mixture sample. Only available if --single-cell-demux-detect-doublets=true.

The <prefix>.scRNA.demuxSamples_metrics.csv file contains per-cell metrics, similar to the metrics reported for the overall dataset in <prefix>.scRNA_metrics.csv.

ColumnDescription

Passing cells

The number of cell barcodes that passed.

Fraction genic reads in cells

Counted reads assigned to the cells that passed.

Median reads per cell

Total counted reads per cell that passed the filters.

Median UMIs per cell

Total counted UMIs per cell that passed the filters.

Median genes per cell

Genes with at least one UMI per cell that passed the filters.

Cell-Hashing

DRAGEN implements several strategies for demultiplexing of data sets that represent mixtures of cells from different individuals, such as cells from different individuals pooled in one library prep or microfluidic run. One of these methods is a sample oligo-tag based method, referred to as cell-hashing.

To use cell-hashing, you must provide a cell-hashing CSV or FASTA reference file. In CSV format, the feature barcode reference file uses the following header: id,name,read,position,sequence,feature_type.

  • id — Identifier of the feature. For example, ADT_A1018.

  • name — Name of the feature. For example, ADT_Hu.HLA.DR.DP.DQ_A1018.

  • read — Read 1 (R1) or Read 2 (R2).

  • position — Position on the specified read, including starting position and the length of the feature barcode. For example, a position of 0_15 represents a feature barcode that starts at position 0 and has a length of 15.

  • sequence — DNA sequence of the feature barcode. For example, CAGCCCGATTAAGGT.

  • feature_type — Type of the feature. For example, Antibody Capture.

Command-Line Options

To enable cell-hashing sample demultiplexing, specify the following command line options.

  • --scrna-cell-hashing-reference --- Specify a CSV or FASTA cell-hashing reference file that contains sample-specific oligo-tags.

  • --scrna-demux-detect-doublets --- Enable doublet detection in cell-hashing sample demultiplexing. The default value is false.

  • --scrna-demux-sample-fastq --- Output sample-specific FASTQ files. See Sample-Specific FASTQ Output Files for more information.

Outputs

The <prefix>.scRNA.barcodeSummary.tsv file contains per-cell metrics, including cell barcodes. The following column in the <prefix>.scRNA.barcodeSummary.tsv contains cell-hashing per-cell information. For more information on the <prefix>.scRNA.barcodeSummary.tsv file, see Single Cell RNA Outputs.

ColumnDescription

SampleIdentity

The SampleIdentity column can contain the following values::

  • sampleX—The particular cell (barcode) is uniquely assigned to a sample.

  • AMB(sampleX,sampleY)—The algorithm cannot determine the sample to assign the barcode to.

  • MIX(mixing_coef*sampleX+(100-mixing_coef)*sampleY)—The cell barcode is classified as doublet. For example, MIX(50*sampleX+50*sampleY).

The <prefix>.scRNA.demux.tsv file contains sample demultiplexing statistics that were used to infer sample identity of each cell.

ColumnDescription

Barcode

The cell barcode associated with the cell.

Pure samples

Cell-hashing read count for each sample.

Sample-Specific FASTQ Output Files

If you have enabled either of the sample demultiplexing algorithms, you can output sample-specific FASTQ files after the sample identities for each cell is available using the command line option --scrna-demux-sample-fastq.

If gzip is specified, then the sample-specific output FASTQ files are compressed in gzip format. If fastq is specified, then the sample-specific FASTQ files are not compressed. The default option is none, which indicates that no sample-specific FASTQ files are produced.

Feature Counting

Feature counting is a technique to profile the expression of proteins (e.g., cell surface proteins or antibodies). Feature reads differ from RNA reads in that their read R2 is not a transcriptomic sequence, but rather an oligo tag corresponding to a particular type of protein.

To enable feature counting, specify the following command-line options:

  • --scrna-feature-barcode-reference — Specify a CSV or FASTA feature reference file that contains feature barcodes. The CSV file format is similar to the format used for Cell-Hashing.

  • --scrna-feature-barcode-groups — If feature reads are specified as separate FASTQ files, specify a comma-separated list of read groups that correspond to feature FASTQ files.

The output of feature counting is appended to the output expression matrix. The extended expression matrix contains additional rows corresponding to the features.

PreviousDRAGEN Single-Cell PipelineNextscATAC

Last updated