grumps 1.0.1

Genomic distance based Rapid Uncovering of Microbial Population Structures

GRUMPS

Genomic distance based Rapid Uncovering of Microbial Population Structures.

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

conda env create -n grumps_env kabram::grumps

Note: using mamba as a replacement for conda can spead up the environment creation process.

Installing into existing environment

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:

conda install kabram::py-grumps

To install the R component:

conda install kabram::r-grumps

Installing from other sources

pip

pip install grumps

Note: the pip version does not have the R dependecies needed for r-grumps.

r-devtools

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:

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:

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.

grumps -h

• Produce summary of input dataset. Quickly obtain multiple statistical summaries as well as a histogram for the input dataset

grumps -m summary [filepath_to_dataset]

• Clean input dataset using regular cleaning mode. Clean the input dataset using K-means clustering.

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.

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.

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.

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.

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.

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

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.

distmat_converter -c yes [filepath_to_fastANI_output.tab]