10x Genomics Single Cell ATAC

Check this GitHub page to see how 10x Chromium Single Cell ATAC libraries are generated experimentally. This is a droplet-based method, where transposed nuclei are captured inside droplets. At the same time, gel beads with barcoded primer containing Nextera Read 1 sequence are also captured inside the droplet. A few cycles of linear PCR (single primer PCR) are performed inside the droplet. Since the transposed nuclei have the Nextera Read 1 sequence in the open chromatin, the DNA fragment will be labelled with the barcodes during the linear PCR stage. 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 and a sample index is added during the library amplification stage.

For Your Own Experiments

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/PN-1000084) taken from the commercial kit from 10x Genomics, to them and they will demultiplex for you. Tell them it is 10x scATAC library and they will know what to do.

If you sequence by yourself, you need to run bcl2fastq by yourself with a SampleSheet.csv in a very 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,,,,,,SI-GA-A1_1,AAACGGCG,,,,
Sample01,,,,,,SI-GA-A1_2,CCTACCAT,,,,
Sample01,,,,,,SI-GA-A1_3,GGCGTTTC,,,,
Sample01,,,,,,SI-GA-A1_4,TTGTAAGA,,,,
Sample02,,,,,,SI-GA-B1_1,AGGCTACC,,,,
Sample02,,,,,,SI-GA-B1_2,CTAGCTGT,,,,
Sample02,,,,,,SI-GA-B1_3,GCCAACAA,,,,
Sample02,,,,,,SI-GA-B1_4,TATTGGTG,,,,

You can see each sample actually has four different index sequences. This is because each well from the plate PN-1000212/PN-1000084 actually contain four different indices for base balancing.

Important

Make sure you understand how sequencing is done for this assay by checking this GitHub page. There are a total of four reads in this order:

Order	Read	Cycle	Description
1	Read 1	> 50	This normally yields R1_001.fastq.gz, Genomic insert
2	Index 1 (i7)	8 or 10	This normally yields I1_001.fastq.gz, Sample index
3	Index 2 (i5)	16	This normally yields I2_001.fastq.gz, Cell barcodes
4	Read 2	> 50	This normally yields R2_001.fastq.gz, Genomic insert

Look at the order of the sequencing, 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 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_S1_I1_001.fastq.gz
Sample01_S1_R1_001.fastq.gz
Sample01_S1_R2_001.fastq.gz
Sample01_S1_R3_001.fastq.gz

# files for Sample02

Sample02_S2_I1_001.fastq.gz
Sample02_S2_R1_001.fastq.gz
Sample02_S2_R2_001.fastq.gz
Sample02_S2_R3_001.fastq.gz

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 mean I2 in our normal usage. The R3 files here actually mean R2 in our normal usage. Anyway, DO NOT get confused.

Public Data

We will use the example data set from the 10x website, using the 500 Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor (Next GEM v1.1) data.

mkdir -p 10xscATAC/pbmc500
wget -P 10xscATAC/pbmc500 -c https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_500_nextgem/atac_pbmc_500_nextgem_fastqs.tar
tar xf 10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs.tar -C 10xscATAC/pbmc500/

These are the files after extraction:

scg_prep_test/10xscATAC/
└── pbmc500
    ├── atac_pbmc_500_nextgem_fastqs
    │   ├── atac_pbmc_500_nextgem_S1_L001_I1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R2_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R3_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_I1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_R1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_R2_001.fastq.gz
    │   └── atac_pbmc_500_nextgem_S1_L002_R3_001.fastq.gz
    └── atac_pbmc_500_nextgem_fastqs.tar

Like said before, we could safely ignore all the I1 reads.

Prepare Whitelist

The barcodes on the gel beads of the 10x Genomics platform are well defined. We need the information for the Single Cell ATAC kit. If you have cellranger-atac in your computer, you will find a file called 737K-cratac-v1.txt.gz in the lib/python/atac/barcodes directory. If you don’t have cellranger-atac, I have prepared the file for you:

# download the whitelist

wget -P 10xscATAC/pbmc500/ https://teichlab.github.io/scg_lib_structs/data/10X-Genomics/737K-cratac-v1.txt.gz
gunzip 10xscATAC/pbmc500/737K-cratac-v1.txt.gz

# reverse complement the whitelist

cat 10xscATAC/pbmc500/737K-cratac-v1.txt | \
    rev | tr 'ACGT' 'TGCA' > \
    10xscATAC/pbmc500/737K-cratac-v1_rc.txt

Explain Whitelist

You may wonder what is the reverse complementary step about. The cell barcodes in the 737K-cratac-v1.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 737K-cratac-v1.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 Fragments

Now we are ready to map the reads to the genome using chromap:

mkdir -p 10xscATAC/chromap_outs

# map and generate the fragment file

chromap -t 4 --preset atac \
        -x hg38/chromap_index/genome.index \
        -r hg38/hg38.analysisSet.fa \
        -1 10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L001_R1_001.fastq.gz,10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L002_R1_001.fastq.gz \
        -2 10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L001_R3_001.fastq.gz,10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L002_R3_001.fastq.gz \
        -b 10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L001_R2_001.fastq.gz,10xscATAC/pbmc500/atac_pbmc_500_nextgem_fastqs/atac_pbmc_500_nextgem_S1_L002_R2_001.fastq.gz \
        --barcode-whitelist 10xscATAC/pbmc500/737K-cratac-v1.txt \
        -o 10xscATAC/chromap_outs/fragments.tsv

# compress and index the fragment file

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

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.

Explain chromap

If you understand the 10x Genomics Single Cell ATAC experimental procedures described in this GitHub Page, the command above should be straightforward to understand.

-t 4

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

-x hg38/chromap_index/genome.index

The chromap index file. We are dealing with human samples in this case.

-r hg38/hg38.analysisSet.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 10xscATAC/pbmc500/737K-cratac-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-atac. 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 10xscATAC/chromap_outs/fragments.tsv

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

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 hg38/hg38.analysisSet.fa | \
    sort -k1,1 > hg38/hg38.chrom.sizes

This is the first 5 lines of hg38/hg38.chrom.sizes:

chr1    248956422
chr10   133797422
chr11   135086622
chr11_KI270721v1_random 100316
chr12   133275309

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 10xscATAC/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 hg38/hg38.chrom.sizes stdout | \
    sort -k1,1 -k2,2n | \
    gzip > 10xscATAC/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 hg38.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 10xscATAC/chromap_outs/reads.bed.gz \
               -g hs -f BED -q 0.01 \
               --nomodel --shift -100 --extsize 200 \
               --keep-dup all \
               -B --SPMR \
               --outdir 10xscATAC/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 hg38.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 10xscATAC/chromap_outs/aggregate_peaks.narrowPeak | \
    sort -k1,1 -k2,2n > 10xscATAC/chromap_outs/aggregate_peaks_sorted.bed

# prepare the overlap

bedtools intersect \
    -a 10xscATAC/chromap_outs/aggregate_peaks_sorted.bed \
    -b 10xscATAC/chromap_outs/reads.bed.gz \
    -wo -sorted -g hg38/hg38.chrom.sizes | \
    sort -k8,8 | \
    bedtools groupby -g 8 -c 4 -o freqdesc | \
    gzip > 10xscATAC/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 hg38/hg38.chrom.sizes options make the program use very little memory. Here is an example (top 5 lines) of the output of this part:

chr1	181365	181579	aggregate_peak_1	chr1	181336	181386	GCACGCACAGGTGGTA	.	+	21
chr1	181365	181579	aggregate_peak_1	chr1	181352	181402	ACTACCCGTCCTCAGG	.	-	37
chr1	181365	181579	aggregate_peak_1	chr1	181375	181425	ACTACCCGTCCTCAGG	.	+	50
chr1	181365	181579	aggregate_peak_1	chr1	181418	181468	GCACGCACAGGTGGTA	.	-	50
chr1	181365	181579	aggregate_peak_1	chr1	181423	181473	TTATGTCCATTACTTC	.	+	50

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 the first 5 records from peak_read_ov.tsv.gz:

AAACGAAAGAATCAAC	aggregate_peak_32073:1
AAACGAAAGACACGGT	aggregate_peak_18915:2,aggregate_peak_41671:2,aggregate_peak_54745:2
AAACGAAAGACACTTC	aggregate_peak_14784:2,aggregate_peak_16349:2,aggregate_peak_37921:2
AAACGAAAGACCATAA	aggregate_peak_51252:2
AAACGAAAGACGACTG	aggregate_peak_32356:2

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

• The first line means that in cell AAACGAAAGAATCAAC, the peak aggregate_peak_32073 has 1 count. All the rest peaks not mentioned here have 0 counts in this cell.

• The second line means that in cell AAACGAAAGACACGGT, the peak aggregate_peak_18915 has 2 counts, the peak aggregate_peak_41671 has 2 counts and the peak aggregate_peak_54745 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 10xscATAC/chromap_outs/raw_peak_bc_matrix

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

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

After that, you should have the matrix.mtx in the 10xscATAC/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 10xscATAC/chromap_outs/raw_peak_bc_matrix/features.tsv

# filter cells using starsolo

STAR --runMode soloCellFiltering \
     10xscATAC/chromap_outs/raw_peak_bc_matrix \
     10xscATAC/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 10xscATAC/chromap_outs/filtered_peak_bc_matrix/peaks.bed

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

scg_prep_test/10xscATAC/
├── 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
└── pbmc500
    ├── 737K-cratac-v1_rc.txt
    ├── 737K-cratac-v1.txt
    ├── atac_pbmc_500_nextgem_fastqs
    │   ├── atac_pbmc_500_nextgem_S1_L001_I1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R2_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L001_R3_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_I1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_R1_001.fastq.gz
    │   ├── atac_pbmc_500_nextgem_S1_L002_R2_001.fastq.gz
    │   └── atac_pbmc_500_nextgem_S1_L002_R3_001.fastq.gz
    └── atac_pbmc_500_nextgem_fastqs.tar

5 directories, 29 files

Note

How does this workflow compared to cellranger-atac?

It is certainly much much faster, but what about the results? We can compare them with the cellranger-atac generated matrix from the example page. Just to do a very brief comparison:

Comparison	chromap + macs2	cellranger-atac	overlap
Peak number	71,000	65,908	61,042
Cell number	643	484	482