Genotyper for somatic SNV and indel discovery in PTA-amplified single cells.
SCAN2 should not be used for genotyping germline mutations, as it excludes any mutation with any read support in matched bulk samples.
IMPORTANT: SCAN2 should only be applied to diploid autosomes (i.e., human autosomes 1-22). Sex chromosomes are currently not supported, but in principle SCAN2 is applicable to female X chromosomes.
Note
The instructions below guide the user through a minimal demo of the SCAN2 tool with the primary goal of checking the installation. For a more detailed walkthrough including cross-sample panel building and mutation signature-based rescue, please see the wiki.
SCAN2 currently will not call any mutation candidate with any supporting reads in the matched bulk sample. At standard WGS sequencing depths of 30x, this excludes nearly all germline variants, the majority of early post-zygotic mutations and, if the matched bulk is from a closely related tissue, many lower frequency clonal somatic mutations as well. We therefore often refer to SCAN2 calls as "non-clonal," although this is only approximately the case since clonal mutations with low frequency may also have 0 reads in the matched bulk.
It is not necessarily a good idea to allow mutations with >0 reads in bulk since it may allow some germline heterozygous mutations or relatively rare artifacts (of sequencing, alignment, etc.) to be miscalled as somatic mutations. This is particularly true in single cells with very low (i.e., <100) somatic mutation burden.
This restriction may be removed in the future after further testing.
SCAN2 is freely available for non-commercial use.
SCAN2 is distributed as a conda package. Installation requires the conda package management tool and a Linux-flavored OS.
Operating systems tested
- GNU/Linux, kernel version 3.10.0, CentOS 7.
IMPORTANT Ensure that your chosen install prefix has sufficient disk space: SCAN2 requires ~15G of disk space to analyze GRCh37 samples and an additional ~10G is required for each extra genome.
$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ bash Miniconda3-latest-Linux-x86_64.sh
# Accept the license by typing "yes"
# Choose to run conda init (enter yes a second time during script)
Log-out and back in to source .bashrc and put conda on $PATH
Create a conda environment for SCAN2 and install necessary packages
conda create -n scan2 -c conda-forge -c bioconda -c jluquette -c dranew -c soil scan2
# Once the environment is created, it must be activated every time
# you wish to use SCAN2.
conda activate scan2
SigProfilerMatrixGenerator is used to classify indels into the 83-class indel mutation signature format ID83. This classification requires a reference genome to determine the sequence context around each indel. The following command shows how to install a SigProfilerMatrixGenerator reference genome for GRCh37; GRCh38 is also available. See https://github.com/AlexandrovLab/SigProfilerMatrixGenerator for details on other genome assemblies and custom assemblies.
N.B. this installation step is required even if indels are not analyzed.
REMEMBER the scan2 conda environment must be activated (see above) when running these commands!
$ python
from SigProfilerMatrixGenerator import install as genInstall
genInstall.install('GRCh37', rsync=False, bash=True)
quit()
SCAN2 has been tested on the NCBI human reference build 37 and hg38.
The SCAN2 demo requires GRCh37.
Download the human reference genome if needed. The reference genome should match the genome used for read alignment.
wget ftp:https://[email protected]/bundle/b37/human_g1k_v37_decoy.fasta.gz
wget ftp:https://[email protected]/bundle/b37/human_g1k_v37_decoy.fasta.fai.gz
wget ftp:https://[email protected]/bundle/b37/human_g1k_v37_decoy.dict.gz
gunzip *.gz
dbSNP build 147 (common variants only) was used in the publication. It is critical to use the database of common variants; the full dbSNP often intersects with somatic mutations and will cause reduced sensitivity. Instructions for generating the required tribble index are provided at the bottom of this page.
wget https://ftp.ncbi.nih.gov/snp/pre_build152/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz
gunzip common_all_20180423.vcf.gz
# Create Tribble index (see bottom of this page)
This VCF can be filtered to match build 147 (used in Luquette et al. 2022). Each line contains a dbSNPBuildID=XXX tag; simply filter for XXX <= 147.
wget https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.tgz
wget https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3_chrX.tgz
Unzip everything and move the chrX SHAPEIT files into the main SHAPEIT directory.
tar xzvf 1000GP_Phase3.tgz
tar xzvf 1000GP_Phase3_chrX.tgz
mv genetic_map_chrX_* 1000GP_Phase3_chrX* 1000GP_Phase3
To run the SCAN2 demo, you will need the GRCh37 genome, not GRCh38.
NOTE: GRCh38 alignments use the 'chr' prefix for chromosome names (i.e., chr1, chr2, ..., rather than 1, 2, ...).
conda install -c conda-forge -c bioconda bioconductor-bsgenome.hsapiens.ucsc.hg38
$ python
from SigProfilerMatrixGenerator import install as genInstall
genInstall.install('GRCh38', rsync=False, bash=True)
quit()
Download the human reference genome if needed. The reference genome should match the genome used for read alignment.
wget https://storage.googleapis.com/genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.fasta
wget https://storage.googleapis.com/genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.fasta.fai
wget https://storage.googleapis.com/genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.dict
Instructions for generating the required tribble index are provided at the bottom of this page.
wget https://ftp.ncbi.nih.gov/snp/pre_build152/organisms/human_9606_b151_GRCh38p7/VCF/common_all_20180418.vcf.gz
gunzip common_all_20180418.vcf.gz
# IMPORTANT: add a 'chr' prefix to sites. The dbSNP VCF currently
# does not contain ##contig=<ID=chrXXX... header lines, so we only need
# to update sites. This may change in the future.
sed -e 's/\(^[^#]\)/chr\1/' common_all_20180418.vcf > common_all_20180418.chrprefix.vcf
# Create Tribble index (see bottom of this page)
The user must create a reference panel manually for Eagle phasing. SCAN2 provides
a script (scan2_download_eagle_refpanel.sh) to do this, which essentially
implements the strategy recommended by Eagle2's authors (see
https://alkesgroup.broadinstitute.org/Eagle/#x1-320005.3.2). The script is located
in the same directory as the scan2 binary (which scan2), but is
runnable by simply typing the script's name when the scan2 conda environment is
activated.
mkdir eagle_1000g_panel
cd eagle_1000g_panel
for chr in {1..22} X; do
scan2_download_eagle_refpanel.sh path/to/Homo_sapiens_assembly38.fasta $chr
done
Download Eagle's genmap file:
wget https://storage.googleapis.com/broad-alkesgroup-public/Eagle/downloads/tables/genetic_map_hg38_withX.txt.gz
Finally, you must supply the following arguments to scan2 config to use Eagle instead of SHAPEIT:
--phaser eagle--eagle-refpanel path/to/eagle_1000g_panel- this should be the directory created by the commands above.--eagle-genmap /path/to/genetic_map_hg38_withX.txt.gz
These files are aligned to GRCh37, so one must downloaded the GRCh37 external data files as directed above to run the demo.
We provide two MDA-amplified (not PTA-amplified!) single cell BAMs and one matched bulk from Dong et al. (Nature Methods 2017) for the demo. These are chosen (rather than our PTA data) because they are publicly available on SRA while our PTA cells are only available only through protected access at dbGaP.
wget https://compbio.med.harvard.edu/scan-snv/hunamp.chr22.bam
wget https://compbio.med.harvard.edu/scan-snv/hunamp.chr22.bam.bai
wget https://compbio.med.harvard.edu/scan-snv/il-11.chr22.bam
wget https://compbio.med.harvard.edu/scan-snv/il-11.chr22.bam.bai
wget https://compbio.med.harvard.edu/scan-snv/il-12.chr22.bam
wget https://compbio.med.harvard.edu/scan-snv/il-12.chr22.bam.bai
Replace instances of /path/to/... with the paths downloaded above. This demo runs in ~15 minutes on a 20 core machine by restricting analysis to a 2 MB segment of chr22.
# Creates a directory for the analysis
scan2 -d demo init
cd demo
# Configure analysis parameters. Can be run multiple times to change
# parameters.
scan2 config \
--verbose \
--ref /path/to/human_g1k_v37_decoy.fasta \
--dbsnp /path/to/dbsnp_138_b37.vcf \
--shapeit-refpanel /path/to/1000GP_Phase3 \
--regions 22:30000000-30999999,22:31000000-31999999 \
--bulk-bam /path/to/hunamp.chr22.bam \
--sc-bam /path/to/il-11.chr22.bam
--sc-bam /path/to/il-12.chr22.bam
# Attempts to validate the chosen parameters and input files. Not
# exhaustive, but helpful to prevent errors.
scan2 validate
# Run the analysis. Set the number of cores you wish you use; for
# more advanced parallelization on a cluster or the cloud, see the
# sections below.
scan2 run --joblimit <N cores>
See scan2 -h for more details on configuration options and runtime arguments.
After SCAN2 completes, single sample results are stored in
Rdata format demo/call_mutations/[single_cell_sample_name]/scan2_object.rda.
Run R and load the RData file. IMPORTANT! Use the R installation in the SCAN2 conda environment for the SCAN2 R library.
$ R
R> library(scan2)
# The two single cell samples are 'h24' (il-11) and 'h25' (il-12)
R> load('demo/call_mutations/h25/scan2_object.rda')
We now provide an S4 class called 'SCAN2' that handles both the genotyping logic and allows convenient user interaction. Each single cell in a SCAN2 run will have a 'results' object saved in the corresponding scan2_object.rda file.
The S4 class provides a summary of several features of the data.
R> results
# SCAN2
# Region:
GRanges object with 22 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] 1 1-249250621 *
[2] 2 1-243199373 *
[3] 3 1-198022430 *
[4] 4 1-191154276 *
[5] 5 1-180915260 *
... ... ... ...
[18] 18 1-78077248 *
[19] 19 1-59128983 *
[20] 20 1-63025520 *
[21] 21 1-48129895 *
[22] 22 1-51304566 *
-------
seqinfo: 22 sequences from GRCh37.p13 genome
# Genome: hs37d5
# Single cell ID: h25
# Bulk ID: hunamp
# GATK: 2517 raw sites
# AB model training hSNPs: 753 phasing: 0|1=411, 1|0=342
# VAF correlation between neighboring hSNPs:
# <10 bp 1 <100 bp 0.983 <1000 bp 0.897 <10 kbp 0.806 <100 kbp 0.203
# 36 resampled hSNPs
# 7 resampled hIndels
# AB model parameters:
# average (over chromosomes): a=-2.374, b=669.314, c=1.125, d=10193.908
# Allele balance:
# mean (0 is neutral): -0.073
# uncertainty (Q25, median, Q75): 0.372 0.558 1.162
# mean at training hSNPs: 0.058
# Mutation models: computed
# CIGAR data: single cell: 2517 sites, bulk: 2517 sites
# Static filters:
# snv: 12 retained 1873 removed 0 NA
# indel: 7 retained 625 removed 0 NA
# FDR prior data:
# snv: 25 candidates 657 germline hets 4 max burden
# indel: 13 candidates 108 germline hets 0 max burden
# Depth profile:
# snv: genome <: min. bulk DP 1.3%, min. sc DP 8.6%, either 9.4%
# indel: genome <: min. bulk DP 1.3%, min. sc DP 15.4%, either 15.9%
# Somatic mutation calls: mode=new, target.fdr=0.010
# snv: 0 called 11 resampled training calls
# indel: 0 called 1 resampled training calls
# ALL indel calls have been suppressed (insufficient number of single cells in cross-sample filter
# Somatic mutation burden:
# snv: NA somatic, NA% sens, NA callable Gbp, NA muts/haploid Gbp, NA muts per genome
# indel: NA somatic, NA% sens, NA callable Gbp, NA muts/haploid Gbp, NA muts per genome
# indel mutation burden estimates ARE NOT VALID (cross-sample panel insufficiency)!
# Mutation rescue by signature: (not computed)
This SCAN2 object contains only VAF-based somatic mutation calls. For
signature-based rescue, see scan2 rescue -h. Further, this SCAN2 object
will not contain indel calls because no cross sample filter was provided
(see scan2 config --help, --cross-sample-panel).
There should be no called somatic SNVs in this demo dataset.
R> snvs <- results@gatk[pass == TRUE & muttype == 'snv']
R> snvs
Empty data.table (0 rows and 73 cols): chr,pos,dbsnp,refnt,altnt,mq...
In a practical setting, parallelization will be required. SCAN2 leverages Snakemake to offer parallelization via any of the following:
- A single machine with multiple cores. To do this, increase the
--joblimitparameter but do not invoke cluster or cloud arguments. A total memory limit can also be set via--memlimit, which should be supplied in megabytes. - Clusters with distributed resource management software (e.g., SLURM,
LSF, GridEngine). If your resource management software supports
DRMAA (https://www.drmaa.org), the authors recommend using the
--drmaa. However, additional libraries may be necessary to interface with the DRMAA wrapper.- E.g., in the SCAN2 publication, a SLURM cluster was accessed via
the slurm-drmaa 1.1.1 package (https://github.com/natefoo/slurm-drmaa). This
package was maintained by local cluster system admins.
An example command line for running SCAN2 with DRMAA follows. Note the
--memargument, which uses Snakemake's{resources.mem}placeholder to allow each individual job in the SCAN2 pipeline to specify the amount of memory needed and the{threads}placeholder.scan2 run --joblimit 100 --drmaa ' -p YOUR_QUEUE_NAME -A YOUR_ACCOUNT -t 10:00 -c {threads} --mem={resources.mem}' - If your scheduler is not DRMAA-compatible (or if the appropriate
DRMAA interface is unavailable), Snakemake's
--clusteroption offers similar functionality to--drmaa, but with fewer features.
- E.g., in the SCAN2 publication, a SLURM cluster was accessed via
the slurm-drmaa 1.1.1 package (https://github.com/natefoo/slurm-drmaa). This
package was maintained by local cluster system admins.
An example command line for running SCAN2 with DRMAA follows. Note the
- Cloud environments.
See Snakemake's documentation for more details on cluster and cloud execution: https://snakemake.readthedocs.io/en/stable/.
dbSNP VCFs must be indexed by Tribble (not tabix) for GATK. The dbSNP found in the GATK's resource bundle is already indexed. If you wish to use a different dbSNP version (as in the above section on downloading external data dependencies), use IGVtools as detailed below:
# IMPORTANT! IMPORTANT! Do not install igvtools into your SCAN2 conda
# environment. Create a new environment for igvtools! At the time of this writing,
# igvtools depends on an incompatible version of the JDK and will break the
# GATK installation.
conda deactivate # exit the SCAN2 environment
conda create -n igvtools -c bioconda igvtools # create a new environment with only igvtools installed
conda activate igvtools
igvtools index /path/to/your/dbsnp.vcf
conda deactivate # exit the igvtools environment
conda activate scan2 # reactivate the SCAN2 environment