SCSilicon

SCSilicon is a tool for synthetic single-cell DNA sequencing data generation.

1. Pre-requirements

• python3
• numpy>=1.16.1
• pandas>=0.23.4,<0.24
• tasklogger>=0.4.0
• wget>=3.2
• seaborn>=0.11.1
• matplotlib>=3.0.2
• SCSsim

All python packages will be automatically installed when you install SCSilicon if these packages are not included in your python library.

To install SCSsim, please refer to the README of SCSsim.

2. Installation

Installation with pip

To install with pip, run the following from a terminal:

pip install scsilicon

Installation from Github

To clone the repository and install manually, run the following from a terminal:

git clone https://github.com/xikanfeng2/SCSilicon.git
cd SCSilicon
python setup.py install

3. Quick start

The following code runs SCSilicon.

import scsilicon as scs

# create SCSiliconParams object
params = scs.SCSiliconParams()

#download all necessary reference files. Just run once in the first time and remove this line after the first run.
scs.download_ref_data(params)

# simulate snp samples
snp_simulator = scs.SNPSimulator()
snp_simulator.sim_samples(params)

# simulate snv samples
snv_simulator = scs.SNVSimulator()
snv_simulator.sim_samples(params)

# simulate indel samples
indel_simulator = scs.IndelSimulator()
indel_simulator.sim_samples(params)

# simulate cnv samples
cnv_simulator = scs.CNVSimulator()
cnv_simulator.sim_samples(params)

4. SCSiliconParams object

All the general parameters for the SCSilicon simulation are stored in a SCSiliconParams object. Let’s create a new one.

params = scs.SCSiliconParams()

4.1 All parameters in SCSiliconParams object

• out_dir: string, optional, default: './'.
  The output directory path

• ref: string, optional, default: hg19.
  The reference genome version: hg19 or hg38

• chrom: string, optional, default: chr22.
  The chromosome number for reads generation: all or a specific chromosome

• layout: string, optional, default: 'SE'.
  The reads laryout: PE or SE (PD for paired-end and SE for single-end)

• coverage: int, optional, default: 5.
  The sequencing coverage

• isize: int, optional, default: 260.
  The mean insert size for paired-end sequencing

• threads: int, optional, default: 1.
  The number of threads to use for reads generation

• verbose: int or boolean, optional, default: 1.
  If True or > 0, print log messages

4.2 Getting and setting

If we want to look at the value of parameters, we can extract it using the get_params function:

params.get_params()

# console log: {'out_dir': './', 'ref': 'hg19', 'chrom': 'chr20', 'layout': 'PE', 'coverage': 5, 'isize': 260, 'threads': 10}

Alternatively, to give a parameter a new value we can use the set_params function:

paramss.set_params(ref='hg38', chrom='chr22')

# console log: {'out_dir': './', 'ref': 'hg38', 'chrom': 'chr22', 'layout': 'PE', 'coverage': 5, 'isize': 260, 'threads': 10}

We can also set parameters directly when we create new SCSiliconParams object:

params = scs.SCSiliconParams(ref='hg38', chrom='chr22')

5. Simulating reads for SNPs using SNPSimulator object

Once we have a set of parameters we are happy with we can use SNPSimulator to simulate samples with SNPs in it.

snp_simulator = scs.SNPSimulator()
snp_simulator.sim_samples(params)

5.1 All parameters in SNPSimulator object

• cell_no: int, optional, default: 1.
  The cell number for this simulation

• snp_no : int, optional, default: 1000
  The SNP number of each sample

For each sample, SNPSimulator will randomly select a total number of SNPs from dbSNP file and snp_no parameter can be used to control this total number.

5.2 Getting and setting

Similar to SCSiliconParams, SNPSimulator uses the functions get_params and set_params to get or set parameters.

5.3 Generating FASTAQ sample

SNPSimulator object uses the function sim_samples to generate FASTQ files for each sample.

snp_simulator.sim_samples()

If you want to simulate multiple samples once, you can use the cell_no parameter to contorl this.

snp_simulator.set_params(cell_no=10)

# or set the parameter when creating the object
snp_simulator = scs.SNPSimulator(cell_no=10)

# generating reads
snp_simulator.sim_samples(params)

Above code will simulate 10 samples with FASTQ format once.

5.4 Output files of sim_samples function

The sim_samples function will generate two output files for each sample in your output directory.

• sample{1}-snps.txt: the SNPs included in this sample. This file can be reagrded as the groud truth for SNP detection software.
• sample{1}.fq: the reads data of this sample with FASTQ format.

{1} is the sample no., like sample1-snps.txt, sample2-snps.txt.

6. Simulating reads for CNVs using CNVimulator object

We can use CNVimulator to simulate samples with CNVs.

cnv_simulator = scs.CNVSimulator()
cnv_simulator.sim_samples(params)

6.1 All parameters in CNVimulator object

• cell_no: int, optional, default: 1.
  The cell number for this simulation

• bin_len: int, optional, default: 500000.
  The fixed bin length

• seg_no: int, optional, default: 10.
  The segment number for each chromosome

• cluster_no: int, optional, default: 1.
  The cell cluster number for multiple sample simulation

• normal_frac: float, optional, default: 0.4.
  The fraction of normal cells

• noise_frac: float, optional, default: 0.1.
  The noise fraction for cnv matrix

6.2 Getting and setting

Similar to SCSiliconParams, CNVimulator uses the functions get_params and set_params to get or set parameters.

6.3 Generating FASTAQ sample

CNVimulator object also uses the function sim_samples to generate FASTQ files for each sample.

cnv_simulator.sim_samples(params)

The seg_no parameter can be used to control the segments in each chromosome.

cnv_simulator.set_params(seg_no=8)

# or set the parameter when creating the object
cnv_simulator = scs.SNPSimulator(seg_no=8)

# generating reads
cnv_simulator.sim_samples(params)

Above code will split each chromosome to 8 segments and this is useful for segmentation experiments of single cell CNV detection tools.

If you want to simulate multiple samples once, you can use the cell_no parameter to contorl this.

cnv_simulator.set_params(cell_no=10)

# or set the parameter when creating the object
cnv_simulator = scs.SNPSimulator(cell_no=10)

# generating reads
cnv_simulator.sim_samples(params)

Above code will simulate 10 samples with FASTQ format once.

For multiple-sample simulation, you can use the cluster_no parameter to seperate these samples to several clusters.

cnv_simulator.set_params(cluster_no=5)

# or set the parameter when creating the object
cnv_simulator = scs.SNPSimulator(cluster_no=10)

# generating reads
cnv_simulator.sim_samples(params)

6.4 Output files of sim_samples function

The sim_samples function will generate two output files for each sample in your output directory.

• cnv.csv: the CNV matrix with cells as rows and bins as columns. This file can be reagrded as the groud truth for CNV detection software.
• segments.csv: the segments information for each chromosome. This file can be reagrded as the groud truth for segmentation experiments.
• clusters.csv: the clusters information for each sample. This file can be reagrded as the groud truth for cell cluster experiments.
• sample{1}.fq: the reads data of this sample with FASTQ format.

{1} is the sample no., like sample1.fq, sample2.fq.

6.5 Visualizing the CNV matrix

CNVimulator object has the funciton visualize_cnv_matrix to draw the heatmap graph for the cnv matrix.

cnv_simulator.visualize_cnv_matrix(out_prefix)

This function will save the heatmap with pdf format to the file named as out_prefix.pdf. One example of cnv heatmap graph is shown below:

7. Simulating reads for SNVs using SNVSimulator object

Once we have a set of parameters we are happy with we can use SNVSimulator to simulate samples with SNVs in it.

snv_simulator = scs.SNVSimulator()
snv_simulator.sim_samples(params)

7.1 All parameters in SNVSimulator object

• cell_no: int, optional, default: 1.
  The cell number for this simulation

• snv_no: int, optional, default: 1000
  The SNV number of each sample

8. Simulating reads for Indels using IndelSimulator object

Once we have a set of parameters we are happy with we can use IndelSimulator to simulate samples with Indels in it.

indel_simulator = scs.IndelSimulator()
indel_simulator.sim_samples(params)

8.1 All parameters in IndelSimulator object

• cell_no: int, optional, default: 1.
  The cell number for this simulation

• in_no: int, optional, default: 1000
  The insertion number of each sample

• del_no: int, optional, default: 1000
  The deletion number of each sample

Cite us

Feng, X., Chen, L. SCSilicon: a tool for synthetic single-cell DNA sequencing data generation. BMC Genomics 23, 359 (2022). Full text

Help

If you have any questions or require assistance using SCSilicon, please contact us with [email protected].