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ISSAAC-seq

ISSAAC-seq#

Check this GitHub page to see how ISSAAC-seq libraries are generated experimentally. In this method, open chromatin DNA and RNA/DNA hybrid after reverse transcription were tagged by the transposase Tn5. Then single nuclei are captured either through FACS or by a droplet system with Nextera sequence on the bead. In this documentation, we are only demonstrating the droplet-based workflow with 10x Genomics Single Cell ATAC system. Soon, we will update a new version of the FACS-based workflow, which will unify both FACS- and droplet-based reagents.

For Your Own Experiments#

If you follow the protocol, you will see that ISSAAC-seq leverage the 10x Genomics Single Cell ATAC kit for the joint detection of both gene expression (RNA) and chromatin accessibility (ATAC) from the same cell. Therefore, both the RNA and the ATAC libraries have the same configuration, and they are the same as the 10x Genomics Single Cell ATAC library configuration.

If you sequence your data via your core facility or a company, you will need to provide the sample and modality index sequence, which is basically the Illumina Nextera N7xx primer, to them and ask they to sequence the library as if they are 10x Genomics Single Cell ATAC libraries. They will know what to do and demultiplex for you.

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 Illumina Nextera Indexing primers of N701, N702, N703 and N704:

[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],,,,,,,,,,,
151,,,,,,,,,,,
151,,,,,,,,,,,
,,,,,,,,,,,
[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
Sample1_ATAC,,,,,,N701,TAAGGCGA,,,,
Sample1_RNA,,,,,,N702,CGTACTAG,,,,
Sample2_ATAC,,,,,,N703,AGGCAGAA,,,,
Sample2_RNA,,,,,,N704,TCCTGAGC,,,,

You can see the i7 sequence in the N7xx primer serves as the index to discriminate both the sample (Sample1 vs Sample2) and the modality (ATAC vs RNA).

Important

Make sure you understand how sequencing is done for ISSAAC-seq by checking this GitHub page. For convenience, we always sequence the RNA library and the ATAC library in the same lane together. Very often, we also mix them with other 10x Genomics Single Cell ATAC libraries. For both ISSAAC-ATAC and ISSAAC-RNA, 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, RNA cDNA reads or ATAC genomic insert

2

Index 1 (i7)

8

This normally yields I1_001.fastq.gz, Sample and modality 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, RNA UMI or ATAC genomic insert

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 and modality index, that is, the 2nd read (I1). To do this, you should run bcl2fastq in the following way:

bcl2fastq --use-bases-mask=Y151,I8,Y16,Y151 \
          --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=Y151,I8,Y16,Y151. We have four reads, and that parameter specify how we treat each read in the stated order:

  1. Y151 at the first position indicates “use the cycle as a real read”, so you will get 151-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. Y151 at the fourth position indicates “use the cycle as a real read”, so you will get 151-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 per modality. Using the examples above, these are the files you should get:

# files for Sample1_ATAC

Sample1_ATAC_S1_I1_001.fastq.gz # 8 bp: sample and modality index
Sample1_ATAC_S1_R1_001.fastq.gz # 151 bp: genomic insert
Sample1_ATAC_S1_R2_001.fastq.gz # 16 bp: cell barcodes
Sample1_ATAC_S1_R3_001.fastq.gz # 151 bp: genomic insert 

# files for Sample1_RNA

Sample1_RNA_S2_I1_001.fastq.gz # 8 bp: sample and modality index
Sample1_RNA_S2_R1_001.fastq.gz # 151 bp: cDNA reads
Sample1_RNA_S2_R2_001.fastq.gz # 16 bp: cell barcodes
Sample1_RNA_S2_R3_001.fastq.gz # 151 bp: The first 10 bp are UMI, the rest (poly-T) are ignored

# files for Sample2_ATAC

Sample2_ATAC_S3_I1_001.fastq.gz # 8 bp: sample and modality index
Sample2_ATAC_S3_R1_001.fastq.gz # 151 bp: genomic insert
Sample2_ATAC_S3_R2_001.fastq.gz # 16 bp: cell barcodes
Sample2_ATAC_S3_R3_001.fastq.gz # 151 bp: genomic insert

# files for Sample2_RNA

Sample2_RNA_S4_I1_001.fastq.gz # 8 bp: sample and modality index
Sample2_RNA_S4_R1_001.fastq.gz # 151 bp: cDNA reads
Sample2_RNA_S4_R2_001.fastq.gz # 16 bp: cell barcodes
Sample2_RNA_S4_R3_001.fastq.gz # 151 bp: The first 10 bp are UMI, the rest (poly-T) are ignored

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. You are ready to go from here.

Public Data#

The data is from the following paper from our group:

Note

Xu W, Yang W, Zhang Y, Chen Y, Zhang Q, Wang X, Song K, Jin W, Chen X (2022) ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. bioRxiv 2022.01.16.476488. https://doi.org/10.1101/2022.01.16.476488

where we developed the ISSAAC-seq method for the first time. The data is deposited to ArrayExpress under the accession code E-MTAB-11264. There are quite a few samples there, but we will just use the mCortex_Droplet_rep1 sample.

mkdir -p ISSAAC-seq/data
wget -P ISSAAC-seq/data -c \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847057/mCortex_rep1_Droplet_ATAC_S1_L001_I2_001.fastq.gz \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847057/mCortex_rep1_Droplet_ATAC_S1_L001_R1_001.fastq.gz \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847057/mCortex_rep1_Droplet_ATAC_S1_L001_R2_001.fastq.gz \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847058/mCortex_rep1_Droplet_RNA_S1_L001_I2_001.fastq.gz \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847058/mCortex_rep1_Droplet_RNA_S1_L001_R1_001.fastq.gz \
    ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR984/ERR9847058/mCortex_rep1_Droplet_RNA_S1_L001_R2_001.fastq.gz

These are the files after download:

scg_prep_test/ISSAAC-seq/data/
├── mCortex_rep1_Droplet_ATAC_S1_L001_I2_001.fastq.gz
├── mCortex_rep1_Droplet_ATAC_S1_L001_R1_001.fastq.gz
├── mCortex_rep1_Droplet_ATAC_S1_L001_R2_001.fastq.gz
├── mCortex_rep1_Droplet_RNA_S1_L001_I2_001.fastq.gz
├── mCortex_rep1_Droplet_RNA_S1_L001_R1_001.fastq.gz
└── mCortex_rep1_Droplet_RNA_S1_L001_R2_001.fastq.gz

0 directories, 6 files

As you can see, we did not upload the I1 file, because it is not important. In addition, instead of using the R1, R2 and R3 names from the bcl2fastq, we renamed the files during the submission. The current naming is consistent with our normal usage: R1, I2 and R2, where I2 is the cell barcodes.

For the RNA data, we need an extra step. To use starsolo, we need to stitch the cell barcode (I2) and the UMI (the first 10bp of R2) into a single fastq:

paste <(zcat ISSAAC-seq/data/mCortex_rep1_Droplet_RNA_S1_L001_I2_001.fastq.gz) \
      <(zcat ISSAAC-seq/data/mCortex_rep1_Droplet_RNA_S1_L001_R2_001.fastq.gz) | \
      awk -F '\t' '{ if(NR%4==1||NR%4==3) {print $1} else {print $1 substr($2,1,10)} }' | \
      gzip > ISSAAC-seq/data/mCortex_rep1_Droplet_RNA_S1_L001_CB_UMI.fastq.gz

That yields a new fastq file mCortex_rep1_Droplet_RNA_S1_L001_CB_UMI.fastq.gz with 26bp in length. The first 16 bp are cell barcodes and the last 10 bp are UMI. Now you ready to go.

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 ATAC kit, because that is the kit used in the mCortex data set. Both the RNA library and the ATAC library are using this whitelist. 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 ISSAAC-seq/data https://teichlab.github.io/scg_lib_structs/data/10X-Genomics/737K-cratac-v1.txt.gz
gunzip ISSAAC-seq/data/737K-cratac-v1.txt.gz

# reverse complement the whitelist

cat ISSAAC-seq/data/737K-cratac-v1.txt | \
    rev | tr 'ACGT' 'TGCA' > \
    ISSAAC-seq/data/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 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 ISSAAC-seq/star_outs
mkdir -p ISSAAC-seq/chromap_outs

# process the RNA library using starsolo

STAR --runThreadN 4 \
     --genomeDir mm10/star_index \
     --readFilesCommand zcat \
     --outFileNamePrefix ISSAAC-seq/star_outs/ \
     --readFilesIn ISSAAC-seq/data/mCortex_rep1_Droplet_RNA_S1_L001_R1_001.fastq.gz ISSAAC-seq/data/mCortex_rep1_Droplet_RNA_S1_L001_CB_UMI.fastq.gz \
     --soloType CB_UMI_Simple \
     --soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 10 \
     --soloCBwhitelist ISSAAC-seq/data/737K-cratac-v1_rc.txt \
     --clip3pNbases 116 \
     --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 ISSAAC-seq/data/mCortex_rep1_Droplet_ATAC_S1_L001_R1_001.fastq.gz \
        -2 ISSAAC-seq/data/mCortex_rep1_Droplet_ATAC_S1_L001_R2_001.fastq.gz \
        -b ISSAAC-seq/data/mCortex_rep1_Droplet_ATAC_S1_L001_I2_001.fastq.gz \
        --barcode-whitelist ISSAAC-seq/data/737K-cratac-v1_rc.txt \
        -o ISSAAC-seq/chromap_outs/fragments.tsv

## compress and index the fragment file

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

After this stage, we are done with the RNA 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 ISSAAC-seq 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 the cerebral cortex of an adult mouse.

--readFilesCommand zcat

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

--outFileNamePrefix ISSAAC-seq/star_outs/

We want to keep everything organised. This directs all output files inside the ISSAAC-seq/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 ISSAAC-seq, cDNA reads come from Read 1, and the cell barcode and UMI come from CB_UMI.fastq.gz file we just prepared before . Check the ISSAAC-seq 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 10

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 ISSAAC-seq/data/737K-cratac-v1_rc.txt

The plain text file containing all possible valid cell barcodes, one per line. For this data set, the ISSAAC-seq droplet workflow used the 10x Chromium Single Cell ATAC kit as the single cell capture platform. This is a commercial platform. The whitelist is taken from their commercial software cellranger-atac. This experiment was sequenced on a NovaSeq (v1.5), so we should use the reverse complementary version of the original whitelist. In other cases, you might want to use the original one.

--clip3pNbases 116

We sequenced this library together with libraries from other people that requires 151 bp pair end sequencing. Therefore, we have 151 bp in the cDNA reads (R1_001.fastq.gz) which is unnecessarily long. The 3’ of the read may contain adaptor sequences, so we just used the first 35 bp of Read 1 for the mapping, which is sufficient. This option remove 116 bp from the 3’ end. Change this parameter or drop this option accordingly for your own data.

--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 ISSAAC-seq, the cDNA reads are from the Read 1 file. During the experiment, the transposed cDNA molecules in the RNA/DNA hybrid are captured by barcoded primer containing Illumina Nextera Read 1 sequence. The reads come from the coding strand. Therefore, use Forward for ISSAAC-seq 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 ISSAAC-seq 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 the cerebral cortex of an adult mouse.

-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; R2 is the genomic Read 2 and should be passed to -2; I2 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 ISSAAC-seq/data/737K-cratac-v1_rc.txt

The plain text file containing all possible valid cell barcodes, one per line. For this data set, the ISSAAC-seq droplet workflow used the 10x Chromium Single Cell ATAC kit as the single cell capture platform. This is a commercial platform. The whitelist is taken from their commercial software cellranger-atac. This experiment was sequenced on a NovaSeq (v1.5), so we should use the reverse complementary version of the original whitelist. In other cases, you might want to use the original one.

-o ISSAAC-seq/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 ISSAAC-seq/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 > ISSAAC-seq/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 ISSAAC-seq/chromap_outs/reads.bed.gz \
               -g mm -f BED -q 0.01 \
               --nomodel --shift -100 --extsize 200 \
               --keep-dup all \
               -B --SPMR \
               --outdir ISSAAC-seq/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 ISSAAC-seq/chromap_outs/aggregate_peaks.narrowPeak | \
    sort -k1,1 -k2,2n > ISSAAC-seq/chromap_outs/aggregate_peaks_sorted.bed

# prepare the overlap

bedtools intersect \
    -a ISSAAC-seq/chromap_outs/aggregate_peaks_sorted.bed \
    -b ISSAAC-seq/chromap_outs/reads.bed.gz \
    -wo -sorted -g mm10/mm10.chrom.sizes | \
    sort -k8,8 | \
    bedtools groupby -g 8 -c 4 -o freqdesc | \
    gzip > ISSAAC-seq/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	3012629	3012836	aggregate_peak_54	chr1	3012465	3012665	GATGCATTGAATCGAA	.	+	36
chr1	3012629	3012836	aggregate_peak_54	chr1	3012465	3012665	GTTCTGGCTTGACTCA	.	+	36
chr1	3012629	3012836	aggregate_peak_54	chr1	3012540	3012740	CCTCCCTTGTTCGTTT	.	+	111
chr1	3012629	3012836	aggregate_peak_54	chr1	3012562	3012762	CAGGAGCCTCCTAGGC	.	-	133
chr1	3012629	3012836	aggregate_peak_54	chr1	3012577	3012777	AGGTAGCGACGTACAT	.	+	148

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:

AAACAACGAAAACTGA	aggregate_peak_109919:2,aggregate_peak_200603:2
AAACAACGAAAAGCTA	aggregate_peak_57301:2
AAACAACGAAAAGGTT	aggregate_peak_41947:1

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

  • The first line means that in cell AAACAACGAAAACTGA, the peak aggregate_peak_109919 has 2 counts and the peak aggregate_peak_200603 has 2 counts. All the rest peaks not mentioned here have 0 counts in this cell.

  • The second line means that in cell AAACAACGAAAAGCTA, the peak aggregate_peak_57301 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 ISSAAC-seq/chromap_outs/raw_peak_bc_matrix

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

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

After that, you should have the matrix.mtx in the ISSAAC-seq/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 ISSAAC-seq/chromap_outs/raw_peak_bc_matrix/features.tsv

# filter cells using starsolo

STAR --runMode soloCellFiltering \
     ISSAAC-seq/chromap_outs/raw_peak_bc_matrix \
     ISSAAC-seq/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 ISSAAC-seq/chromap_outs/filtered_peak_bc_matrix/peaks.bed

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

scg_prep_test/ISSAAC-seq/
├── 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
├── data
│   ├── 737K-cratac-v1_rc.txt
│   ├── 737K-cratac-v1.txt
│   ├── mCortex_rep1_Droplet_ATAC_S1_L001_I2_001.fastq.gz
│   ├── mCortex_rep1_Droplet_ATAC_S1_L001_R1_001.fastq.gz
│   ├── mCortex_rep1_Droplet_ATAC_S1_L001_R2_001.fastq.gz
│   ├── mCortex_rep1_Droplet_RNA_S1_L001_CB_UMI.fastq.gz
│   ├── mCortex_rep1_Droplet_RNA_S1_L001_I2_001.fastq.gz
│   ├── mCortex_rep1_Droplet_RNA_S1_L001_R1_001.fastq.gz
│   └── mCortex_rep1_Droplet_RNA_S1_L001_R2_001.fastq.gz
└── 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

9 directories, 42 files
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