grumps

Name	grumps JSON
Version	1.0.3 JSON
	download
home_page	None
Summary	Genomic distance based Rapid Uncovering of Microbial Population Structures
upload_time	2024-09-16 17:07:14
maintainer	None
docs_url	None
author	None
requires_python	>=3.7
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keywords	ani clustering data cleaning genome machine learning mash population structure
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# GRUMPS
***G***enomic distance based ***R***apid ***U***ncovering of ***M***icrobial ***P***opulation ***S***tructures.

`grumps` is designed to assist and speed up the construction of species level population structures using Mash distances as an input. ANI values can be used as well if the ANI values are converted to a decimal difference value {i.e. (100 - ANIvalue) /100}. Additional helper scripts are provided if you do not have a correctly formatted distance matrix to input.

`grumps` reads a normalized distance matrix and returns a filtered result. It can be run in `summary`, `regular`, `strict`, `sigma`, `target`,
`clique`, and `small` modes.

For more information about these modes please see the white paper:
https://doi.org/10.1101/2022.12.19.521123

## Dependencies
`grumps` utilizes the following python libraries:
* python >=3.7
* pandas
* networkx
* seaborn
* scipy
* scikit-learn

## Installation

`grumps` is installable from conda, pip or from source.

The best installation method for `grumps` is conda within a new environment.

### Installing from conda
#### Creating a new environment
```sh
conda env create -n grumps_env kabram::grumps
```
**Note:** using [mamba](https://mamba.readthedocs.io/en/latest/) as a replacement for conda can spead up the environment creation process.

#### Installing into existing environment
```sh
conda install kabram::grumps
```
`grumps` has two components in the conda package: `py-grumps` and `r-grumps`. Each component is a conda package which contains all necessary dependencies for that component.

To install the python component:
```sh
conda install kabram::py-grumps
```
To install the R component:
```sh
conda install kabram::r-grumps
```
### Installing from other sources
#### pip
```sh
pip install grumps
```
**Note:** the pip version does not have the R dependecies needed for `r-grumps`.

#### r-devtools
```sh
R -e 'devtools::install_github("kalebabram/r_grumps")'
```
**Note:** R library `devtools` is required: `install.packages('devtools')` or `conda install r-devtools` for this approach. To get the CLI entrypoint, download `cligrumps.R` from the repository. You can rename the Rscript to `r-grumps`, relocate it to a directory in your $PATH, and make it executable for equivalent behavior to the conda install of `r-grumps`.

#### Source
All neccessary files needed to build the python package of `grumps` are found in `src/grumps` in the `grumps` repository: https://github.com/kalebabram/grumps.git

All neccessary files needed to build the R package of `grumps` are found in the `r-grumps` repository: https://github.com/kalebabram/r-grumps.git

In order to get the CLI entrypoint for the R package, simply download the Rscript `cligrumps.R` to your computer. You can rename the Rscript to `r-grumps`, relocate it to a directory in your `$PATH`, and make it executable for equivalent behavior to the conda install of `r-grumps`.

## `grumps` library support
### python
`grumps` also is available as a python library to allow easy integration into existing python based workflows.

While `grumps` has many components, the following overview summarizes functions which users are intended to interact with:
```sh
grumps
├── .api
│ └── .pipeLine()
├── .core
│ ├── .grumpsObj()
│ └── .distmatConverter()
└── .modes
├── .regularMode()
├── .removerMode()
├── .targetMode()
├── .smallMode()
├── .sigmaMode()
├── .summaryMode()
└── .cliqueMode()
```
**Note:** `import grumps.api as grumps` will automatically load all the above functions which can be accessed via `grumps.<function_name>` (i.e. `grumps.grumpsObj()`)

The intended use of the python `grumps` library is as follows:
```py
import grumps.api as grumps
# if you need to convert your input file to NxN distance matrix. The location of the converted file is printed.
grumps.distmatConverter('/path/to/input/file.tab')
# load in the NxN distance matrix
data = grumps.grumpsObj('/path/to/distmat/file.csv')
# change these grumpsObj defaults or the cleaning modes/pipeline will run with defaults
data.mode = 'regular'
data.cutOff = 0.05
data.clusterMethod = 'ward'
data.makeHeatmap = 'yes'
data.figFormat = 'png'
data.targetFilePath = ''
data.removeFilePath = ''
data.sigma = 'yes'
data.medoid = 'yes'
# if you want to run grumps automatically
grumps.pipeLine(data)
# if you want to use a specific cleaning mode
data = grumps.regularMode(data)
```

## Usage Summary
### `grumps`
The following section provides a set of minimal command line commands to use `grumps`. Please use the help page, `grumps -h`, to see all command line options and what modes these options can be used with.

* **Produce help page.** Quickly check the software usage and available command line options.
```sh
grumps -h
```

* **Produce summary of input dataset.** Quickly obtain multiple statistical summaries as well as a histogram for the input dataset
```sh
grumps -m summary [filepath_to_dataset]
```

* **Clean input dataset using `regular` cleaning mode.** Clean the input dataset using K-means clustering.
```sh
grumps -m regular [filepath_to_dataset]
```

* **Clean input dataset using `strict` cleaning mode.** Clean the input dataset using K-means clustering followed by a three-sigma rule based cleaning step using the means of each genome.
```sh
grumps -m strict [filepath_to_dataset]
```

* **Clean input dataset using `clique` cleaning mode.** Clean the input dataset with a graph-based clustering approach. Useful for dividing datasets containing multiple species into a collection of uncleaned species level datasets.
```sh
grumps -m clique [filepath_to_dataset]
```

* **Clean input dataset using `sigma` cleaning mode.** Clean the input dataset using a three-sigma rule based cleaning step applied to the extreme left and right tails of value distribution for each genome. **Note:** this step is automatically performed in `regular` and `strict` cleaning modes if `-s no` not specified.
```sh
grumps -m sigma [filepath_to_dataset]
```

* **Clean input dataset using `target` cleaning mode.** Clean the input dataset using a set of target genomes. Any genome that has a value greater than the cutoff (default 0.05) to any of the provided target genomes are removed.
```sh
grumps -m target -t [filepath_to_file_with_target_ids] [filepath_to_dataset]
```

* **Clean input dataset using `remover` cleaning mode.** Remove a set of genomes from the input dataset by ID.
```sh
grumps -m remover -r [filepath_to_file_with_ids_to_remove] [filepath_to_dataset]
```

## Helper Script
`distmat_converter` reads a regularly delimited file and returns a .csv distance matrix result. By default, the output of `mash dist` can be used by `distmat_converter` to obtain a Mash distance matrix for `grumps`
```sh
distmat_converter [filepath_to_mash_output.tab]
```
If an ANI delimited file is input, please specify how `distmat_converter` should handle the ANI values with the options `-c yes` or `-i yes`. **Note:** `-c` or `-i` are conflicting options with `-c` having a higher priority. `-c yes` converts the ANI values to Mash values via (100-ANI)/1. `-i yes` simply inverts ANI values via 100-ANI.
```sh
distmat_converter -c yes [filepath_to_fastANI_output.tab]
```

Raw data

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    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.7",
    "maintainer_email": "Kaleb Abram <abram.kaleb@gmail.com>",
    "keywords": "ANI, clustering, data cleaning, genome, machine learning, mash, population structure",
    "author": null,
    "author_email": "Kaleb Abram <abram.kaleb@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/51/94/acfca8240d0b2c3c1d089d620db4760f7cbc2b13ad2937bfdec3e0de8dfe/grumps-1.0.3.tar.gz",
    "platform": null,
    "description": "# GRUMPS\n***G***enomic distance based ***R***apid ***U***ncovering of ***M***icrobial ***P***opulation ***S***tructures.\n\n`grumps` is designed to assist and speed up the construction of species level population structures using Mash distances as an input. ANI values can be used as well if the ANI values are converted to a decimal difference value {i.e. (100 - ANIvalue) /100}. Additional helper scripts are provided if you do not have a correctly formatted distance matrix to input.\n\n`grumps` reads a normalized distance matrix and returns a filtered result. It can be run in `summary`, `regular`, `strict`, `sigma`, `target`,\n`clique`, and `small` modes. \n\nFor more information about these modes please see the white paper:\nhttps://doi.org/10.1101/2022.12.19.521123\n\n## Dependencies\n`grumps` utilizes the following python libraries:\n* python >=3.7\n* pandas\n* networkx\n* seaborn\n* scipy\n* scikit-learn\n\n## Installation\n\n`grumps` is installable from conda, pip or from source. \n\nThe best installation method for `grumps` is conda within a new environment. \n\n### Installing from conda\n#### Creating a new environment\n```sh\nconda env create -n grumps_env kabram::grumps\n```\n**Note:** using [mamba](https://mamba.readthedocs.io/en/latest/) as a replacement for conda can spead up the environment creation process.\n\n#### Installing into existing environment\n```sh\nconda install kabram::grumps\n```\n`grumps` has two components in the conda package: `py-grumps` and `r-grumps`. Each component is a conda package which contains all necessary dependencies for that component. \n\nTo install the python component:\n```sh\nconda install kabram::py-grumps\n```\nTo install the R component:\n```sh\nconda install kabram::r-grumps\n```\n### Installing from other sources\n#### pip\n```sh\npip install grumps\n```\n**Note:** the pip version does not have the R dependecies needed for `r-grumps`. \n\n#### r-devtools\n```sh\nR -e 'devtools::install_github(\"kalebabram/r_grumps\")'\n```\n**Note:** R library `devtools` is required: `install.packages('devtools')` or `conda install r-devtools` for this approach. To get the CLI entrypoint, download `cligrumps.R` from the repository. You can rename the Rscript to `r-grumps`, relocate it to a directory in your $PATH, and make it executable for equivalent behavior to the conda install of `r-grumps`. \n\n#### Source\nAll neccessary files needed to build the python package of `grumps` are found in `src/grumps` in the `grumps` repository: https://github.com/kalebabram/grumps.git\n\nAll neccessary files needed to build the R package of `grumps` are found in the `r-grumps` repository: https://github.com/kalebabram/r-grumps.git\n\nIn order to get the CLI entrypoint for the R package, simply download the Rscript `cligrumps.R` to your computer. You can rename the Rscript to `r-grumps`, relocate it to a directory in your `$PATH`, and make it executable for equivalent behavior to the conda install of `r-grumps`. \n\n## `grumps` library support\n### python\n`grumps` also is available as a python library to allow easy integration into existing python based workflows.\n\nWhile `grumps` has many components, the following overview summarizes functions which users are intended to interact with:\n```sh\ngrumps\n\u251c\u2500\u2500 .api\n\u2502   \u2514\u2500\u2500 .pipeLine()\n\u251c\u2500\u2500 .core\n\u2502   \u251c\u2500\u2500 .grumpsObj()\n\u2502   \u2514\u2500\u2500 .distmatConverter()\n\u2514\u2500\u2500 .modes\n    \u251c\u2500\u2500 .regularMode()\n    \u251c\u2500\u2500 .removerMode()\n    \u251c\u2500\u2500 .targetMode()\n    \u251c\u2500\u2500 .smallMode()\n    \u251c\u2500\u2500 .sigmaMode()\n    \u251c\u2500\u2500 .summaryMode()\n    \u2514\u2500\u2500 .cliqueMode()\n```\n**Note:** `import grumps.api as grumps` will automatically load all the above functions which can be accessed via `grumps.<function_name>` (i.e. `grumps.grumpsObj()`)\n\nThe intended use of the python `grumps` library is as follows:\n```py\nimport grumps.api as grumps\n# if you need to convert your input file to NxN distance matrix. The location of the converted file is printed.\ngrumps.distmatConverter('/path/to/input/file.tab')\n# load in the NxN distance matrix\ndata = grumps.grumpsObj('/path/to/distmat/file.csv')\n# change these grumpsObj defaults or the cleaning modes/pipeline will run with defaults\ndata.mode = 'regular'\ndata.cutOff = 0.05\ndata.clusterMethod = 'ward'\ndata.makeHeatmap = 'yes'\ndata.figFormat = 'png'\ndata.targetFilePath = ''\ndata.removeFilePath = ''\ndata.sigma = 'yes'\ndata.medoid = 'yes'\n# if you want to run grumps automatically\ngrumps.pipeLine(data)\n# if you want to use a specific cleaning mode\ndata = grumps.regularMode(data)\n```\n\n## Usage Summary\n### `grumps`\nThe following section provides a set of minimal command line commands to use `grumps`. Please use the help page, `grumps -h`, to see all command line options and what modes these options can be used with.  \n\n* **Produce help page.** Quickly check the software usage and available command line options.\n```sh\ngrumps -h\n```\n\n* **Produce summary of input dataset.** Quickly obtain multiple statistical summaries as well as a histogram for the input dataset\n```sh\ngrumps -m summary [filepath_to_dataset]\n```\n\n* **Clean input dataset using `regular` cleaning mode.** Clean the input dataset using K-means clustering. \n```sh\ngrumps -m regular [filepath_to_dataset] \n```\n\n* **Clean input dataset using `strict` cleaning mode.** Clean the input dataset using K-means clustering followed by a three-sigma rule based cleaning step using the means of each genome.\n```sh\ngrumps -m strict [filepath_to_dataset]\n```\n\n* **Clean input dataset using `clique` cleaning mode.** Clean the input dataset with a graph-based clustering approach. Useful for dividing datasets containing multiple species into a collection of uncleaned species level datasets.\n```sh\ngrumps -m clique [filepath_to_dataset]\n```\n\n* **Clean input dataset using `sigma` cleaning mode.** Clean the input dataset using a three-sigma rule based cleaning step applied to the extreme left and right tails of value distribution for each genome. **Note:** this step is automatically performed in `regular` and `strict` cleaning modes if `-s no` not specified.\n```sh\ngrumps -m sigma [filepath_to_dataset]\n```\n\n* **Clean input dataset using `target` cleaning mode.** Clean the input dataset using a set of target genomes. Any genome that has a value greater than the cutoff (default 0.05) to any of the provided target genomes are removed.\n```sh\ngrumps -m target -t [filepath_to_file_with_target_ids] [filepath_to_dataset]\n```\n\n* **Clean input dataset using `remover` cleaning mode.** Remove a set of genomes from the input dataset by ID. \n```sh\ngrumps -m remover -r [filepath_to_file_with_ids_to_remove] [filepath_to_dataset]\n```\n\n## Helper Script\n`distmat_converter` reads a regularly delimited file and returns a .csv distance matrix result. By default, the output of `mash dist` can be used by `distmat_converter` to obtain a Mash distance matrix for `grumps`\n```sh\ndistmat_converter [filepath_to_mash_output.tab]\n```\nIf an ANI delimited file is input, please specify how `distmat_converter` should handle the ANI values with the options `-c yes` or `-i yes`. **Note:** `-c` or `-i` are conflicting options with `-c` having a higher priority. `-c yes` converts the ANI values to Mash values via (100-ANI)/1. `-i yes` simply inverts ANI values via 100-ANI. \n```sh\ndistmat_converter -c yes [filepath_to_fastANI_output.tab]\n```\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\"  \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.  2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. 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