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توضیحات

SCSilicon: a Python package that simulate single-cell DNA sequencing data.

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	SCSilicon-1.1.4
نام	SCSilicon
نسخه کتابخانه	1.1.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Xikang Feng
ایمیل نویسنده	fxk@nwpu.edu.cn
آدرس صفحه اصلی	https://github.com/xikanfeng2/SCSilicon
آدرس اینترنتی	https://pypi.org/project/SCSilicon/
مجوز	-

# SCSilicon SCSilicon is a tool for synthetic single-cell DNA sequencing data generation. ![overview.png](overview.png) ## 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](https://github.com/qasimyu/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](https://github.com/qasimyu/scssim). ## 2. Installation ### Installation with pip To install with pip, run the following from a terminal: ```Bash pip install scsilicon ``` ### Installation from Github To clone the repository and install manually, run the following from a terminal: ```Bash git clone https://github.com/xikanfeng2/SCSilicon.git cd SCSilicon python setup.py install ``` ## 3. Quick start The following code runs SCSilicon. ```Python 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. ```Python 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: ```Python 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: ```Python 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: ```Python 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. ```Python 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. ```Python snp_simulator.sim_samples() ``` If you want to simulate `multiple` samples once, you can use the `cell_no` parameter to contorl this. ```Python 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. ```Python 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. ```Python cnv_simulator.sim_samples(params) ``` The `seg_no` parameter can be used to control the segments in each chromosome. ```Python 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. ```Python 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. ```Python 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. ```Python 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: ![cnv heatmap](cnv-heatmap.png) ## 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. ```Python 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. ```Python 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 todo ## Help If you have any questions or require assistance using SCSilicon, please contact us with fxk@nwpu.edu.cn.

نیازمندی

مقدار	نام
>=1.16.1	numpy
>=0.23.4	pandas
>=0.4.0	tasklogger
>=3.2	wget
>=0.11.1	seaborn
>=3.0.2	matplotlib

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl SCSilicon-1.1.4:

pip install SCSilicon-1.1.4.whl

نصب پکیج tar.gz SCSilicon-1.1.4:

pip install SCSilicon-1.1.4.tar.gz