10x Chromium Single Cell Multiome ATAC + Gene Expression

Check this GitHub page to see how 10x Chromium Single Cell Multiome ATAC + Gene Expression libraries are generated experimentally. This is a droplet-based method, where transposed nuclei are captured inside droplets. At the same time, gel beads with two types of barcoded oligos are also captured inside the droplet. One type of oligo contains oligo-dT to capture mRNA, and the other type of oligo contains an 8-bp linker sequence to capture ATAC fragments. See this GitHub page for more details. After droplet capture, reverse transcription and ATAC fragments capture are performed. The cells and gel beads are loaded on the microfluidic device at certain concentrations, such that a fraction of droplets contain only one cell AND one bead. Then, all droplets from one sample is collected. After that, seven cycles of pre-amplification are performed. Then the reaction is split into two portions: one for gene expression library preparation and the other for ATAC library preparation.

For Your Own Experiments

If you follow the user manual from 10x Genomics, you should have two libraries per sample: one for gene expression and the other for ATAC. Normally, you prepare them independently and sequence them separately. See below the sequencing read configurations.

Gene Expression Read Configuration

Regardless of the sequencing machines, you always use the following configuration:

Order	Read	Cycle	Description
1	Read 1	28	R1_001.fastq.gz, 16 bp cell barcodes + 12 bp UMI
2	Index 1 (i7)	10	I1_001.fastq.gz, Sample index
3	Index 2 (i5)	10	I2_001.fastq.gz, Sample index (if using dual index)
4	Read 2	>50	R2_001.fastq.gz, cDNA reads

If you sequence your data via your core facility or a company, you will need to provide the sample index sequence, which is the primer (PN-1000215) taken from the commercial kit from 10x Genomics (dual index in this case), to them and they will demultiplex for you. Tell them it is 10x Multiome Gene Expression library and they will know what to do.

If you sequence by yourself, you need to run bcl2fastq by yourself with a SampleSheet.csv. Here is an example of SampleSheet.csv of a NextSeq run with two different samples using the indexing primers from the A1 and B1 wells, respectively:

[Header],,,,,,,,,,,
IEMFileVersion,5,,,,,,,,,,
Date,17/12/2019,,,,,,,,,,
Workflow,GenerateFASTQ,,,,,,,,,,
Application,NextSeq FASTQ Only,,,,,,,,,,
Instrument Type,NextSeq/MiniSeq,,,,,,,,,,
Assay,AmpliSeq Library PLUS for Illumina,,,,,,,,,,
Index Adapters,AmpliSeq CD Indexes (384),,,,,,,,,,
Chemistry,Amplicon,,,,,,,,,,
,,,,,,,,,,,
[Reads],,,,,,,,,,,
50,,,,,,,,,,,
50,,,,,,,,,,,
,,,,,,,,,,,
[Settings],,,,,,,,,,,
,,,,,,,,,,,
[Data],,,,,,,,,,,
Sample_ID,Sample_Name,Sample_Plate,Sample_Well,Index_Plate,Index_Plate_Well,I7_Index_ID,index,I5_Index_ID,index2,Sample_Project,Description
Sample01_GEX,,,,,,SI-TT-A1_i7,GTAACATGCG,SI-TT-A1_i5,AGGTAACACT,,
Sample02_GEX,,,,,,SI-TT-B1_i7,ACAGTAACTA,SI-TT-B1_i5,AACGAACTGT,,

Simply run bcl2fastq like this:

bcl2fastq --no-lane-splitting \
          --ignore-missing-positions \
          --ignore-missing-controls \
          --ignore-missing-filter \
          --ignore-missing-bcls \
          -r 4 -w 4 -p 4

After this, you will have R1_001.fastq.gz and R2_001.fastq.gz for each sample:

Sample01_GEX_S1_R1_001.fastq.gz # 28 bp: cell barcodes (16 bp) + UMI (12 bp)
Sample01_GEX_S1_R2_001.fastq.gz # >50 bp: cDNA reads
Sample02_GEX_S2_R1_001.fastq.gz # 28 bp: cell barcodes (16 bp) + UMI (12 bp)
Sample02_GEX_S2_R2_001.fastq.gz # >50 bp: cDNA reads

You are ready to go from here.

ATAC Read Configuration

The ATAC library is slightly more complicated than the gene expression library. Make sure you understand how sequencing is done for this assay by checking this GitHub page. Then it is relatively straightforward to see there are two configurations.

Using more recent machines and chemistries, like iSeq 100, MiniSeq (Standard), NextSeq, HiSeq X, HiSeq 3000, HiSeq 4000 and NovaSeq 600 (v1.5), it should be (I call this Configuration 1):

Order	Read	Cycle	Description
1	Read 1	>50	This normally yields R1_001.fastq.gz, Genomic insert
2	Index 1 (i7)	8	This normally yields I1_001.fastq.gz, Sample index
3	Index 2 (i5)	8 dark + 16 normal cycles	This normally yields I2_001.fastq.gz, Cell barcodes (16 bp)
4	Read 2	>50	This normally yields R2_001.fastq.gz, Genomic insert

Using older machines and chemistries like MiSeq, HiSeq 2000, HiSeq 2500, MiniSeq (Rapid) and NovaSeq 6000 (v1.0), it should be (I call this Configuration 2):

Order	Read	Cycle	Description
1	Read 1	>50	This normally yields R1_001.fastq.gz, Genomic insert
2	Index 1 (i7)	8	This normally yields I1_001.fastq.gz, Sample index
3	Index 2 (i5)	16	This normally yields I2_001.fastq.gz, Cell barcodes (16 bp)
4	Read 2	>50	This normally yields R2_001.fastq.gz, Genomic insert

If you sequence your data via your core facility or a company, you will need to provide the sample index sequence, which is the primer (PN-1000212) taken from the commercial kit from 10x Genomics, to them and they will demultiplex for you. Tell them it is 10x Multiome ATAC library and they will know what to do.

If you sequence by yourself, you need to run bcl2fastq with a SampleSheet.csv in specific way. Here is an example of SampleSheet.csv of a NextSeq run with two different samples using the indexing primers from the A1 and B1 wells, respectively:

[Header],,,,,,,,,,,
IEMFileVersion,5,,,,,,,,,,
Date,17/12/2019,,,,,,,,,,
Workflow,GenerateFASTQ,,,,,,,,,,
Application,NextSeq FASTQ Only,,,,,,,,,,
Instrument Type,NextSeq/MiniSeq,,,,,,,,,,
Assay,AmpliSeq Library PLUS for Illumina,,,,,,,,,,
Index Adapters,AmpliSeq CD Indexes (384),,,,,,,,,,
Chemistry,Amplicon,,,,,,,,,,
,,,,,,,,,,,
[Reads],,,,,,,,,,,
50,,,,,,,,,,,
50,,,,,,,,,,,
,,,,,,,,,,,
[Settings],,,,,,,,,,,
,,,,,,,,,,,
[Data],,,,,,,,,,,
Sample_ID,Sample_Name,Sample_Plate,Sample_Well,Index_Plate,Index_Plate_Well,I7_Index_ID,index,I5_Index_ID,index2,Sample_Project,Description
Sample01_ATAC,,,,,,SI-NA-A1_1,AAACGGCG,,,,
Sample01_ATAC,,,,,,SI-NA-A1_2,CCTACCAT,,,,
Sample01_ATAC,,,,,,SI-NA-A1_3,GGCGTTTC,,,,
Sample01_ATAC,,,,,,SI-NA-A1_4,TTGTAAGA,,,,
Sample02_ATAC,,,,,,SI-NA-B1_1,AGGCTACC,,,,
Sample02_ATAC,,,,,,SI-NA-B1_2,CTAGCTGT,,,,
Sample02_ATAC,,,,,,SI-NA-B1_3,GCCAACAA,,,,
Sample02_ATAC,,,,,,SI-NA-B1_4,TATTGGTG,,,,

You can see each sample actually has four different index sequences. This is because each well from the plate PN-1000212 actually contain four different indices for base balancing.

Now let’s look at the order of the sequencing read configuration above, as you can see, the first (R1), the 3rd (I2) and the 4th (R2) reads are all important for us. Therefore, we would like to get all of them for each sample based on sample index, that is, the 2nd read (I1). To do this, you should run bcl2fastq in the following way:

bcl2fastq --use-bases-mask=Y50,I8,Y16,Y50 \
          --create-fastq-for-index-reads \
          --no-lane-splitting \
          --ignore-missing-positions \
          --ignore-missing-controls \
          --ignore-missing-filter \
          --ignore-missing-bcls \
          -r 4 -w 4 -p 4

You can check the bcl2fastq manual for more information, but I the important bit that needs explanation is --use-bases-mask=Y50,I8,Y16,Y50. We have four reads, and that parameter specify how we treat each read in the stated order:

1. Y50 at the first position indicates “use the cycle as a real read”, so you will get 50-nt sequences, output as R1_001.fastq.gz, because this is the 1st real read.

2. I8 at the second position indicates “use the cycle as an index read”, so you will get 8-nt sequences, output as I1_001.fastq.gz, because this is the 1st index read.

3. Y16 at the third position indicates “use the cycle as a real read”, so you will get 16-nt sequences, output as R2_001.fastq.gz, because this is the 2nd real read, though it is the 3rd read overall.

4. Y50 at the fourth position indicates “use the cycle as a real read”, so you will get 50-nt sequences, output as R3_001.fastq.gz, because this is the 3rd real read, though it is the 4th read overall.

Therefore, you will get four fastq file per sample. Using the examples above, these are the files you should get:

# files for Sample01

Sample01_ATAC_S1_I1_001.fastq.gz # 8 bp: sample index
Sample01_ATAC_S1_R1_001.fastq.gz # 50 bp: genomic insert
Sample01_ATAC_S1_R2_001.fastq.gz # 16 bp: cell barcodes
Sample01_ATAC_S1_R3_001.fastq.gz # 50 bp: genomic insert 

# files for Sample02

Sample02_ATAC_S2_I1_001.fastq.gz # 8 bp: sample index
Sample02_ATAC_S2_R1_001.fastq.gz # 50 bp: genomic insert
Sample02_ATAC_S2_R2_001.fastq.gz # 16 bp: cell barcodes
Sample02_ATAC_S2_R3_001.fastq.gz # 50 bp: genomic insert

We can safely ignore the I1 files, but the naming here is really different from our normal usage. The R1 files are good. The R2 files here actually means I2 in our normal usage. The R3 files here actually means R2 in our normal usage. Anyway, DO NOT get confused. You are ready to go from here.

Public Data

We will use the example data set from the 10x website, using the Fresh Embryonic E18 Mouse Brain (5k) data.

mkdir 10xMultiome
wget -P 10xMultiome -c https://s3-us-west-2.amazonaws.com/10x.files/samples/cell-arc/2.0.0/e18_mouse_brain_fresh_5k/e18_mouse_brain_fresh_5k_fastqs.tar
tar xf 10xMultiome/e18_mouse_brain_fresh_5k_fastqs.tar -C 10xMultiome/

These are the files after extraction:

scg_prep_test/10xMultiome/
├── e18_mouse_brain_fresh_5k
│   ├── atac
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_I1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R3_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_I1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz
│   │   └── e18_mouse_brain_fresh_5k_S1_L002_R3_001.fastq.gz
│   └── gex
│       ├── e18_mouse_brain_fresh_5k_S1_L001_I1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_I2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_I1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_I2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz
│       └── e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz
└── e18_mouse_brain_fresh_5k_fastqs.tar

Like said before, we could safely ignore all the I1 reads. The data was also split based on lanes. For gene expression data, the I2 reads can also be ignored.

Prepare Whitelist

The barcodes on the gel beads of the 10x Genomics platform are well defined. We need the information for the 10x Chromium Single Cell Multiome ATAC + Gene Expression kit. If you have cellranger-arc in your computer, you will find two files, both of which are called 737K-arc-v1.txt.gz. One is in the lib/python/cellranger/barcodes directory, which is the gene expression whitelist. The other is in the lib/python/atac/barcodes directory, which is the ATAC whitelist. If you don’t have cellranger-atac, I have prepared and renamed the file to be a more informative for you:

# download the GEX expression whitelist

wget -P 10xMultiome/ https://teichlab.github.io/scg_lib_structs/data/10X-Genomics/gex_737K-arc-v1.txt.gz
gunzip 10xMultiome/gex_737K-arc-v1.txt.gz

# download the atac whitelist

wget -P 10xMultiome/ https://teichlab.github.io/scg_lib_structs/data/10X-Genomics/atac_737K-arc-v1.txt.gz
gunzip 10xMultiome/atac_737K-arc-v1.txt.gz

# reverse complement the atac whitelist

cat 10xMultiome/atac_737K-arc-v1.txt | \
    rev | tr 'ACGT' 'TGCA' > \
    10xMultiome/atac_737K-arc-v1_rc.txt

Explain Whitelist

The lengths of gex_737K-arc-v1.txt and atac_737K-arc-v1.txt are the same, both of which contain 736,320 barcodes. They are the barcodes in the capture oligos on the beads, one for mRNA and the other for ATAC. You can pair them to make a barcode translation file during the downstream analysis. The 1st barcode in the gex_737K-arc-v1.txt and the 1st barcode from the atac_737K-arc-v1.txt are from the same gel bead; The 2nd barcode in the gex_737K-arc-v1.txt and the 2nd barcode from the atac_737K-arc-v1.txt are from the same gel bead; … The 736,320th barcode in the gex_737K-arc-v1.txt and the 736,320th barcode from the atac_737K-arc-v1.txt are from the same gel bead. Using this information, you are able to know what GEX reads and ATAC reads are from the same cell.

You may wonder what is the reverse complementary step about. The cell barcodes in the atac_737K-arc-v1_rc.txt are the sequences on the gel beads. These 16 bp cell barcodes are in the i5 index location, that is, between Illumina P5 and the Nextera Read 1 sequence. It means they will be sequenced as Index 2 (I2). How i5 or Index 2 is sequenced depends on the machine. Previously, MiSeq, HiSeq 2000, HiSeq 2500, MiniSeq (Rapid) and NovaSeq 6000 (v1.0) use the bottom strand as the template, so the index reads will be the same as the barcodes in the 737K-cratac-v1.txt. However, more recent machines and chemistries, like iSeq 100, MiniSeq (Standard), NextSeq, HiSeq X, HiSeq 3000, HiSeq 4000 and NovaSeq 600 (v1.5), use the top strand as the template, so the index reads will be reverse complementary to the barcodes in the atac_737K-arc-v1_rc.txt. Therefore, we need to create a reverse complementary file as the whitelist for some data.

Tip

Whenever you are dealing with reads that come from index 2, you should:

1. Check the sequencing machine used to generate the data

2. Make sure you are familiar with different sequencing modes from different Illumina machines by looking at this page.

3. Extract some sequences from index 2, compare them to the whitelist or the reverse complementary to the whitelist.

From FastQ To Count Matrices

Now we are ready to map the reads to the genome using starsolo for the RNA library and chromap for the ATAC library:

mkdir -p 10xMultiome/star_outs
mkdir -p 10xMultiome/chromap_outs

# process the GEX library using starsolo

STAR --runThreadN 4 \
     --genomeDir mm10/star_index \
     --readFilesCommand zcat \
     --outFileNamePrefix 10xMultiome/star_outs/ \
     --readFilesIn 10xMultiome/e18_mouse_brain_fresh_5k/gex/e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz,10xMultiome/e18_mouse_brain_fresh_5k/gex/e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz 10xMultiome/e18_mouse_brain_fresh_5k/gex/e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz,10xMultiome/e18_mouse_brain_fresh_5k/gex/e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz \
     --soloType CB_UMI_Simple \
     --soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 12 \
     --soloCBwhitelist 10xMultiome/gex_737K-arc-v1.txt \
     --soloCellFilter EmptyDrops_CR \
     --soloStrand Forward \
     --outSAMattributes CB UB \
     --outSAMtype BAM SortedByCoordinate

# process the ATAC library using chromap

## map and generate the fragment file

chromap -t 4 --preset atac \
        -x mm10/chromap_index/genome.index \
        -r mm10/mm10.fa \
        -1 10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz,10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz \
        -2 10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L001_R3_001.fastq.gz,10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L002_R3_001.fastq.gz \
        -b 10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz,10xMultiome/e18_mouse_brain_fresh_5k/atac/e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz \
        --barcode-whitelist 10xMultiome/atac_737K-arc-v1.txt \
        -o 10xMultiome/chromap_outs/fragments.tsv

## compress and index the fragment file

bgzip 10xMultiome/chromap_outs/fragments.tsv
tabix -s 1 -b 2 -e 3 -p bed 10xMultiome/chromap_outs/fragments.tsv.gz

After this stage, we are done with the GEX library. The count matrix and other useful information can be found in the star_outs directory. For the ATAC library, two new files fragments.tsv.gz and fragments.tsv.gz.tbi are generated. They will be useful and sometimes required for other programs to perform downstream analysis. There are still some extra work.

Explain star and chromap

If you understand the 10x Chromium Single Cell Multiome ATAC + Gene Expression experimental procedures described in this GitHub Page, the commands above should be straightforward to understand.

Explain star

--runThreadN 4

Use 4 cores for the preprocessing. Change accordingly if using more or less cores.

--genomeDir mm10/star_index

Pointing to the directory of the star index. The public data we are analysing is from mouse brains.

--readFilesCommand zcat

Since the fastq files are in .gz format, we need the zcat command to extract them on the fly.

--outFileNamePrefix 10xMultiome/star_outs/

We want to keep everything organised. This directs all output files inside the 10xMultiome/star_outs directory.

--readFilesIn

If you check the manual, we should put two files here. The first file is the reads that come from cDNA, and the second the file should contain cell barcode and UMI. In 10x Chromium Single Cell Multiome ATAC + Gene Expression, cDNA reads come from Read 2, and the cell barcode and UMI come from Read 1. Check the 10x Chromium Single Cell Multiome ATAC + Gene Expression GitHub Page if you are not sure. Multiple input files are supported and they can be listed in a comma-separated manner. In that case, they must be in the same order.

--soloType CB_UMI_Simple

Most of the time, you should use this option, and specify the configuration of cell barcodes and UMI in the command line (see immediately below). Sometimes, it is actually easier to prepare the cell barcode and UMI file upfront so that we could use this parameter.

--soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 12

The name of the parameter is pretty much self-explanatory. If using --soloType CB_UMI_Simple, we can specify where the cell barcode and UMI start and how long they are in the reads from the first file passed to --readFilesIn. Note the position is 1-based (the first base of the read is 1, NOT 0).

--soloCBwhitelist 10xMultiome/gex_737K-arc-v1.txt

The plain text file containing all possible valid cell barcodes, one per line. 10x Chromium Single Cell Multiome ATAC + Gene Expression is a commercial platform. The whitelist is taken from their commercial software cellranger-arc.

--soloCellFilter EmptyDrops_CR

Experiments are never perfect. Even for droplets that do not contain any cell, you may still get some reads. In general, the number of reads from those droplets should be much smaller, often orders of magnitude smaller, than those droplets with cells. In order to identify true cells from the background, you can apply different algorithms. Check the star manual for more information. We use EmptyDrops_CR which is the most frequently used parameter.

--soloStrand Forward

The choice of this parameter depends on where the cDNA reads come from, i.e. the reads from the first file passed to --readFilesIn. You need to check the experimental protocol. If the cDNA reads are from the same strand as the mRNA (the coding strand), this parameter will be Forward (this is the default). If they are from the opposite strand as the mRNA, which is often called the first strand, this parameter will be Reverse. In the case of 10x Chromium Single Cell Multiome ATAC + Gene Expression, the cDNA reads are from the Read 2 file. During the experiment, the mRNA molecules are captured by barcoded oligo-dT primer containing UMI and the Illumina Read 1 sequence. Therefore, Read 1 consists of cell barcodes and UMI comes from the first strand, complementary to the coding strand. Read 2 comes from the coding strand. Therefore, use Forward for 10x Chromium Single Cell Multiome ATAC + Gene Expression data. This Forward parameter is the default, because many protocols generate data like this, but I still specified it here to make it clear. Check the 10x Chromium Single Cell Multiome ATAC + Gene Expression GitHub Page if you are not sure.

--outSAMattributes CB UB

We want the cell barcode and UMI sequences in the CB and UB attributes of the output, respectively. The information will be very helpful for downstream analysis.

--outSAMtype BAM SortedByCoordinate

We want sorted BAM for easy handling by other programs.

Explain chromap

-t 4

Use 4 cores for the preprocessing. Change accordingly if using more or less cores.

-x mm10/chromap_index/genome.index

The chromap index file. The public data We are analysing is from mouse brains.

-r mm10/mm10.fa

Reference genome sequence in fasta format. This is basically the file which you used to create the chromap index file.

-1, -2 and -b

They are Read 1 (genomic), Read 2 (genomic) and cell barcode read, respectively. For ATAC-seq, the sequencing is usually done in pair-end mode. Therefore, you normally have two genomic reads for each genomic fragment: Read 1 and Read 2. For the reason described previously, R1 is the genomic Read 1 and should be passed to -1; R3 is actually the genomic Read 2 and should be passed to -2; R2 is the cell barcode read and should be passed to -b. Multiple input files are supported and they can be listed in a comma-separated manner. In that case, they must be in the same order.

--barcode-whitelist 10xMultiome/atac_737K-arc-v1.txt

The plain text file containing all possible valid cell barcodes, one per line. 10x Genomics Single Cell ATAC is a commercial platform. The whitelist is taken from their commercial software cellranger-arc. In this example data, sequencing is done using NovaSeq 6000 (v1.0). Therefore, we use the original whitelist. In other cases, you might want to use the reverse complementary version of the whitelist.

-o 10xMultiome/chromap_outs/fragments.tsv

Direct the mapped fragments to a file. The format is described in the 10x Genomics website.

From ATAC Fragments To Reads

The fragment file is the following format:

Column number	Meaning
1	fragment chromosome
2	fragment start
3	fragment end
4	cell barcode
5	Number of read pairs of this fragment

It is very useful, but we often need the peak-by-cell matrix for the downstream analysis. Therefore, we need to perform a peak calling process to identify open chromatin regions. We need to convert the fragment into reads. For each fragment, we will have two reads, a forward read and a reverse read. The read length is not important, but we could generate a 50-bp read pair for each fragment.

First, we need to create a genome size file, which is a tab delimited file with only two columns. The first column is the chromosome name and the second column is the length of the chromosome in bp:

# we also sort the output by chromosome name
# which will be useful later

faSize -detailed mm10/mm10.fa | \
    sort -k1,1 > mm10/mm10.chrom.sizes

This is the first 5 lines of mm10/mm10.chrom.sizes:

chr1	195471971
chr10	130694993
chr11	122082543
chr12	120129022
chr13	120421639

Now let’s generate the reads from fragments:

# we use bedClip to remove reads outside the chromosome boundary
# we also remove reads mapped to the mitochondrial genome (chrM)

zcat 10xMultiome/chromap_outs/fragments.tsv.gz | \
    awk 'BEGIN{OFS="\t"}{print $1, $2, $2+50, $4, ".", "+" "\n" $1, $3-50, $3, $4, ".", "-"}' | \
    sed '/chrM/d' | \
    bedClip stdin mm10/mm10.chrom.sizes stdout | \
    sort -k1,1 -k2,2n | \
    gzip > 10xMultiome/chromap_outs/reads.bed.gz

Note we also sort the output reads by sort -k1,1 -k2,2n. In this way, the order of chromosomes in the reads.bed.gz is the same as that in mm10.chrom.sizes, which makes downstream processes easier. The output reads.bed.gz are the reads in bed format, with the 4th column holding the cell barcodes.

Peak Calling By MACS2

Now we can use the newly generated read file for the peak calling using MACS2:

macs2 callpeak -t 10xMultiome/chromap_outs/reads.bed.gz \
               -g mm -f BED -q 0.01 \
               --nomodel --shift -100 --extsize 200 \
               --keep-dup all \
               -B --SPMR \
               --outdir 10xMultiome/chromap_outs \
               -n aggregate

Explain MACS2

The reasons of choosing those specific parameters are a bit more complicated. I have dedicated a post for this a while ago. Please have a look at this post if you are still confused. The following output files are particularly useful:

File	Description
aggregate_peaks.narrowPeak	Open chromatin peak locations in the narrowPeak format
aggregate_peaks.xls	More information about peaks
aggregate_treat_pileup.bdg	Signal tracks. Can be used to generate the bigWig file for visualisation

Getting The Peak-By-Cell Count Matrix

Now that we have the peak and reads files, we can compute the number of reads in each peak for each cell. Then we could get the peak-by-cell count matrix. There are different ways of doing this. The following is the method I use.

Find Reads In Peaks Per Cell

First, we use the aggregate_peaks.narrowPeak file. We only need the first 4 columns (chromosome, start, end, peak ID). You can also remove the peaks that overlap the black list regions. The black list is not available for every species and every build, so I’m not doing it here. We also need to sort the peak to make sure the order of the chromosomes in the peak file is the same as that in the mm10.chrom.sizes and reads.bed.gz files. Then we could find the overlap by bedtools. We need to do this in a specific way to get the number of reads in each peak from each cell:

# format and sort peaks

cut -f 1-4 10xMultiome/chromap_outs/aggregate_peaks.narrowPeak | \
    sort -k1,1 -k2,2n > 10xMultiome/chromap_outs/aggregate_peaks_sorted.bed

# prepare the overlap

bedtools intersect \
    -a 10xMultiome/chromap_outs/aggregate_peaks_sorted.bed \
    -b 10xMultiome/chromap_outs/reads.bed.gz \
    -wo -sorted -g mm10/mm10.chrom.sizes | \
    sort -k8,8 | \
    bedtools groupby -g 8 -c 4 -o freqdesc | \
    gzip > 10xMultiome/chromap_outs/peak_read_ov.tsv.gz

Explain Finding Reads In Peaks Per Cell

We start with the command before the first pipe, that is, the intersection part. If you read the manual of the bedtools intersect, it should be straightforward to understand. The -wo option will output the records in both -a file and -b file. Since the reads.bed.gz file has the cell barcode information at the 4th column, we would get an output with both peak and cell information for the overlap. The -sorted -g mm10/mm10.chrom.sizes options make the program use very little memory. Here is an example (top 5 lines) of the output of this part:

chr1	3060888	3061088	aggregate_peak_75	chr1	3060705	3060905	GGATTAGGTTAATCCA	.	+	17
chr1	3060888	3061088	aggregate_peak_75	chr1	3060728	3060928	GAATGACTCGGTCCAT	.	+	40
chr1	3060888	3061088	aggregate_peak_75	chr1	3060759	3060959	ATTCCGGGTACCGTTG	.	+	71
chr1	3060888	3061088	aggregate_peak_75	chr1	3060777	3060977	GCAATACAGTTACTAC	.	+	89
chr1	3060888	3061088	aggregate_peak_75	chr1	3060786	3060986	AATCATGAGTCATCAT	.	+	98

We see that the 8th column holds the cell barcode and we want to group them using bedtools groupby. Therefore, we need to sort by this column, that is the sort -k8,8. When we group by the 8th column, we are interested in how many times each peak appear per group, so we could gather the information of the peak ID (4th column). That is the -g 8 -c 4 -o freqdesc. The -o freqdesc option returns a value:frequency pair in descending order. Here are some records from peak_read_ov.tsv.gz:

AAACAAGCAAAGAAGC	aggregate_peak_117806:2,aggregate_peak_35843:2,aggregate_peak_39908:2,aggregate_peak_42315:2
AAACAAGCAAAGGAAC	aggregate_peak_68626:2
AAACAAGCAAAGGCCT	aggregate_peak_116364:2,aggregate_peak_17597:2,aggregate_peak_48133:2

In a way, that is sort of a count matrix in an awkward format. For example:

• The first line means that in cell AAACAAGCAAAGAAGC, the peak aggregate_peak_117806 has 2 counts, the peak aggregate_peak_35843 has 2 counts, the peak aggregate_peak_39908 has 2 counts and the peak aggregate_peak_42315 has 2 counts. All the rest peaks not mentioned here have 0 counts in this cell.

• The second line means that in cell AAACAAGCAAAGGAAC, the peak aggregate_peak_68626 has 2 counts. All the rest peaks not mentioned here have 0 counts in this cell.

Output The Peak-By-Cell Matrix

At this stage, we pretty much have all the things needed. Those two files aggregate_peaks_sorted.bed and peak_read_ov.tsv.gz contain all information for a peak-by-cell count matrix. We just need a final touch to make the output in a standard format: a market exchange format (MEX). Since most downstream software takes the input from the 10x Genomics Single Cell ATAC results, we are going to generate the MEX and the associated files similar to the output from 10x Genomics.

Here, I’m using a python script for this purpose. You don’t have to do this. Choose whatever works for you. The point here is to just generate similar files as the peak-barcode matrix described from the 10x Genomics website.

First, let’s make a directory to hold the output files and generate the peaks.bed and barcodes.tsv files, which are easy to do:

# create dirctory
mkdir -p 10xMultiome/chromap_outs/raw_peak_bc_matrix

# The 10x Genomics peaks.bed is a 3-column bed file, so we do
cut -f 1-3 10xMultiome/chromap_outs/aggregate_peaks_sorted.bed > \
    10xMultiome/chromap_outs/raw_peak_bc_matrix/peaks.bed

# The barcode is basically the first column of the file peak_read_ov.tsv.gz
zcat 10xMultiome/chromap_outs/peak_read_ov.tsv.gz | \
    cut -f 1 > \
    10xMultiome/chromap_outs/raw_peak_bc_matrix/barcodes.tsv

The slightly more difficult file to generate is matrix.mtx. This is the python script generate_csc_mtx.py for this purpose:

# import helper packages
# most entries in the count matrix is 0, so we are going to use a sparse matrix
# since we need to keep updating the sparse matrix, we use lil_matrix from scipy
import sys
import gzip
from scipy.io import mmwrite
from scipy.sparse import lil_matrix

# the unique peak ID is a good renference
# generate a dictionary with peak_id : index_in_the_file
# sys.argv[1] is the 4-column bed file aggregate_peaks_sorted.bed
peak_idx = {}
with open(sys.argv[1]) as fh:
    for i, line in enumerate(fh):
        _, _, _, peak_name = line.strip().split('\t')
        peak_idx[peak_name] = i

# determine and create the dimension of the output matrix
# that is, to calculate the number of peaks and the number of barcodes
# sys.argv[2] is barcodes.tsv
num_peaks = len(peak_idx.keys())
num_cells = len(open(sys.argv[2]).readlines())
mtx = lil_matrix((num_peaks, num_cells), dtype = int)

# read the information from peak_read_ov.tsv.gz
# update the counts into the mtx object
# sys.argv[3] is peak_read_ov.tsv.gz
with gzip.open(sys.argv[3], 'rt') as fh:
    for i, line in enumerate(fh):
        col_idx = i # each column is a cell barcode
        count_info = line.strip().split('\t')[1]
        items = count_info.split(',')
        for pn_count in items:
            pn, count = pn_count.split(':')
            row_idx = peak_idx[pn] # each row is a peak
            mtx[row_idx, col_idx] = int(count)

# get a CSC sparse matrix, which is the same as the 10x Genomics matrix.mtx
mtx = mtx.tocsc()

# sys.argv[4] is the path to the output directory
mmwrite(sys.argv[4] + '/matrix.mtx', mtx, field='integer')

Run that script in the terminal:

python generate_csc_mtx.py \
    10xMultiome/chromap_outs/aggregate_peaks_sorted.bed \
    10xMultiome/chromap_outs/raw_peak_bc_matrix/barcodes.tsv \
    10xMultiome/chromap_outs/peak_read_ov.tsv.gz \
    10xMultiome/chromap_outs/raw_peak_bc_matrix

After that, you should have the matrix.mtx in the 10xMultiome/chromap_outs/raw_peak_bc_matrix directory.

Cell Calling (Filter Cell Barcodes)

Experiments are never perfect. Even for droplets that do not contain any cell, you may still get some reads. In general, the number of reads from those droplets should be much smaller, often orders of magnitude smaller, than those droplets with cells. In order to identify true cells from the background, we could use starolo. It is used for scRNA-seq in general, but it does have a cell calling function that takes a directory containing raw mtx and associated files, and return the filtered ones. Since starsolo looks for the following three files in the input directory: matrix.mtx, features.tsv and barcodes.tsv. Those are the output from the 10x Genomics scRNA-seq workflow. In this case, we can use peaks.bed as our features.tsv:

# trick starsolo to use peaks.bed as features.tsv by creating symlink

ln -s peaks.bed 10xMultiome/chromap_outs/raw_peak_bc_matrix/features.tsv

# filter cells using starsolo

STAR --runMode soloCellFiltering \
     10xMultiome/chromap_outs/raw_peak_bc_matrix \
     10xMultiome/chromap_outs/filtered_peak_bc_matrix/ \
     --soloCellFilter EmptyDrops_CR

# rename the new feature.tsv to peaks.bed or just create symlink
ln -s features.tsv 10xMultiome/chromap_outs/filtered_peak_bc_matrix/peaks.bed

If everything goes well, your directory should look the same as the following:

scg_prep_test/10xMultiome/
├── atac_737K-arc-v1_rc.txt
├── atac_737K-arc-v1.txt
├── chromap_outs
│   ├── aggregate_control_lambda.bdg
│   ├── aggregate_peaks.narrowPeak
│   ├── aggregate_peaks_sorted.bed
│   ├── aggregate_peaks.xls
│   ├── aggregate_summits.bed
│   ├── aggregate_treat_pileup.bdg
│   ├── filtered_peak_bc_matrix
│   │   ├── barcodes.tsv
│   │   ├── features.tsv
│   │   ├── matrix.mtx
│   │   └── peaks.bed -> features.tsv
│   ├── fragments.tsv.gz
│   ├── fragments.tsv.gz.tbi
│   ├── peak_read_ov.tsv.gz
│   ├── raw_peak_bc_matrix
│   │   ├── barcodes.tsv
│   │   ├── features.tsv -> peaks.bed
│   │   ├── matrix.mtx
│   │   └── peaks.bed
│   └── reads.bed.gz
├── e18_mouse_brain_fresh_5k
│   ├── atac
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_I1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L001_R3_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_I1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz
│   │   ├── e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz
│   │   └── e18_mouse_brain_fresh_5k_S1_L002_R3_001.fastq.gz
│   └── gex
│       ├── e18_mouse_brain_fresh_5k_S1_L001_I1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_I2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_R1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L001_R2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_I1_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_I2_001.fastq.gz
│       ├── e18_mouse_brain_fresh_5k_S1_L002_R1_001.fastq.gz
│       └── e18_mouse_brain_fresh_5k_S1_L002_R2_001.fastq.gz
├── e18_mouse_brain_fresh_5k_fastqs.tar
├── gex_737K-arc-v1.txt
└── star_outs
    ├── Aligned.sortedByCoord.out.bam
    ├── Log.final.out
    ├── Log.out
    ├── Log.progress.out
    ├── SJ.out.tab
    └── Solo.out
        ├── Barcodes.stats
        └── Gene
            ├── Features.stats
            ├── filtered
            │   ├── barcodes.tsv
            │   ├── features.tsv
            │   └── matrix.mtx
            ├── raw
            │   ├── barcodes.tsv
            │   ├── features.tsv
            │   └── matrix.mtx
            ├── Summary.csv
            └── UMIperCellSorted.txt

11 directories, 55 files