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This section will help you set up the necessary software and packages required to run SigProfilerAssignment.
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@[toc](Sections)
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## Prerequisites ##
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-[Python][1] version >= 3.4.0
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-[Python][1] version >= 3.9
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- Downloaded reference genomes using [SigProfilerMatrixGenerator][2] (only if mutation calling files are used as input)
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- Other dependencies and necessary packages are downloaded during the installation
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## Download Reference Genome ##
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In case you want to use SigProfilerAssignment with mutation calling files as input, you first need to download the appropriate reference genome. Current reference genomes supported include GRCh37, GRCh38, mm9, mm10, and rn6. To install the reference genome/s, you need to use [SigProfilerMatrixGenerator][2].
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## Installation ##
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First, install the python package using pip:
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```
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$ pip install SigProfilerMatrixGenerator
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```
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Install your desired reference genome from the command line/terminal as follows:
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```
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$ python
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>> from SigProfilerMatrixGenerator import install as genInstall
SigProfilerAssignment can be executed on any Windows/MacOS/Unix system. First follow the [SigProfilerMatrixGenerator][2] guide for installing `Python` and `pip`. Next, follow the download instructions for the latest stable release or the current GitHub version.
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## Mac/Unix ##
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First, follow the [SigProfilerMatrixGenerator][3] Mac/Unix guide for installing `Python` and `pip`. Next, follow the download instructions for the latest stable release or the current GitHub version.
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### Installation with `pip` ###
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### Mac/Unix Stable Release ###
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Install `SigProfilerAssignment` using `pip`:
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Install last `SigProfilerAssignment` PyPI version using `pip`:
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```
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$ pip install SigProfilerAssignment
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```
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### Mac/Unix GitHub Release ###
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First, download the [zip file][4] or clone the GitHub repository by:
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### Install specific GitHub Release ###
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First, download the [zip file][3] or clone the GitHub repository by:
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$ cd SigProfilerAssignment
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$ pip install .
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```
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After SigProfilerAssignment successfully installs, the environment is set up and ready to use.
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## Windows ##
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First, follow the [SigProfilerMatrixGenerator][3] Windows guide for installing `Python` and `pip`. Next, follow the download instructions for the latest stable release or the current GitHub version.
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## Download Reference Genome ##
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In case you want to use SigProfilerAssignment with mutation calling files as input, you first need to download the appropriate reference genome. Current reference genomes supported include GRCh37, GRCh38, mm9, mm10, rn6 and rn7. To install the reference genome/s, you need to use [SigProfilerMatrixGenerator][2].
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The last PyPI [SigProfilerMatrixGenerator][2] version is installed with SigProfilerAssignment by default. You can also install a specific version following the instructions in [SigProfilerMatrixGenerator Wiki][2].
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### Windows Stable Release ###
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Install `SigProfilerAssignment` using `pip`:
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```
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$ pip install SigProfilerAssignment
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```
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Once [SigProfilerMatrixGenerator][2], install your desired reference genome from the command line/terminal as follows.
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### Installation from command line ###
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### Windows GitHub Release ###
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First, download the [zip file][4] or clone the GitHub repository by:
Next, enter the downloaded directory and install the package by unzipping the contents of SigProfilerAssignment-master or the zip file of a corresponding branch:
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```
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$ cd SigProfilerAssignment
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$ pip install .
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```python
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$ python
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>>from SigProfilerMatrixGenerator import install as genInstall
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This section provides an example for users to quickly get started with using the SigProfilerAssignment tool. The following example will use somatic mutational data from breast cancer samples from [Nik-Zainal et al. 2012 Cell][1], and will showcase how to use SigProfilerAssignment with different types of files containing the input somatic mutations, including variant calling files (VCFs) and mutational matrices.
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@[toc](Sections)
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----------
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## Prerequisites ##
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- Downloaded **GRCh37** reference genome using SigProfilerMatrixGenerator
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## Downloading Input Example Data ##
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## Downloading input example data ##
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This example uses somatic mutational data from a breast cancer genome. Download the example dataset `BRCA.zip` at the following location or use the command line:
Once `BRCA.zip` has been downloaded, unzip the file. The unzipped `BRCA` folder contains `BRCA.txt` and another folder `BRCA_vcf`. The file `BRCA.txt` is a mutational matrix defined using SBS-96 classification (created by [SigProfilerMatrixGenerator][3]) and `BRCA_vcf` contains the corresponding VCF file associated to the sample.
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## Running SigProfilerAssignment (VCF) ##
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You will be assigning reference mutational signatures from [COSMIC][4] v3.3 to the breast cancer sample in the subfolder `BRCA_vcf` used as input for this example.
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## Running SigProfilerAssignment from VCF ##
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You will be assigning reference mutational signatures from [COSMIC][4] v3.5 to the breast cancer sample in the subfolder `BRCA_vcf` used as input for this example.
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First, start a Python interactive shell and import the SigProfilerAssignment library.
After SigProfilerAssignment has finished running, an output directory name `output_vcf` will be created. This directory will contain the output files and is located in the directory where the Python instance was started. To learn more about the output produced by SigProfilerAssignment, please refer to the [Using the Tool - Output][5] section.
You will be assigning reference mutational signatures from [COSMIC][6] v3.3 to the mutational matrix defined using the SBS-96 classification named `BRCA.txt` input for this example.
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You will be assigning reference mutational signatures from [COSMIC][4] v3.5 to the mutational matrix defined using the SBS-96 classification named `BRCA.txt` input for this example.
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First, start a Python interactive shell and import the SigProfilerAssignment library.
After SigProfilerAssignment has finished running, an output directory name `output_mm` will be created. This directory will contain the output files and is located in the directory where the Python instance was started. To lear more about the output produced by SigProfilerAssignment, please refer to the [Using the Tool - Output][5] section.
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You can also run SigProfilerAssignment `cosmic_fit` function from command line:
After SigProfilerAssignment has finished running, an output directory name `output_mm` will be created. This directory will contain the output files and is located in the directory where the Python instance was started. To learn more about the output produced by SigProfilerAssignment, please refer to the [Using the Tool - Output][5] section.
You will be assigning reference mutational signatures from [COSMIC][8] v3.3 to the multi-sample segmentation file obtained from one of the copy number calling tools named `all.breast.ascat.summary.sample.tsv` input for this example.
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You will be assigning reference mutational signatures from [COSMIC][4] v3.5 to the multi-sample segmentation file obtained from one of the copy number calling tools named `all.breast.ascat.summary.sample.tsv` input for this example.
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First, start a Python interactive shell and import the SigProfilerAssignment library.
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After SigProfilerAssignment has finished running, an output directory name `example_sf` will be created. This directory will contain the output files and is located in the directory where the Python instance was started. To lear more about the output produced by SigProfilerAssignment, please refer to the [Using the Tool - Output][5] section.
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You can also run SigProfilerAssignment `cosmic_fit` function from command line:
After SigProfilerAssignment has finished running, an output directory name `example_sf` will be created. This directory will contain the output files and is located in the directory where the Python instance was started. To learn more about the output produced by SigProfilerAssignment, please refer to the [Using the Tool - Output][5] section.
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## Additional Information ##
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In the above examples, the other non specified parameters are passed in with their default values. All of the function arguments and their types are explained in detail in the [Using the Tool - Input section][7]. To learn more about the files that were produced, you can refer to [Using the Tool - Output][5].
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In the above examples, the other non specified parameters are passed in with their default values. All of the function arguments and their types are explained in detail in the [Using the Tool - Input section][6]. To learn more about the files that were produced, you can refer to [Using the Tool - Output][5].
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