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GRIMER version 1.1.0 (#5)
* bump v1.1.0, fix bugs, new manuals * fix header int to str * docs * export for tables, more docs * control as metadata field-value, export all cols obstable * readme * readme * docs on root * multiple fields metadata prevalance decontam * images manual * first user manual version * Update README.md * docs, add export to help button * revised docs, new logo and params
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‎README.md

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# GRIMER
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![GRIMER](grimer/img/logo.png)
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GRIMER performs analysis of microbiome data and generates a portable and interactive dashboard integrating annotation, taxonomy and metadata with focus on contamination detection. More information about the method can be found in the [pre-print](https://doi.org/10.1101/2021.06.22.449360)
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## Examples
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Online examples of reports generated with GRIMER: https://pirovc.github.io/grimer-reports/
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## Installation
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Via conda
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```bash
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conda install -c bioconda -c conda-forge grimer
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```
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or locally installing only dependencies via conda:
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```bash
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git clone https://github.com/pirovc/grimer.git
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cd grimer
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conda env create -f env.yaml # or mamba env create -f env.yaml
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conda activate grimer # or source activate grimer
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python setup.py install --record files.txt # Uninstall: xargs rm -rf < files.txt
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grimer -h
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```
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## Usage
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### Tab-separated input table
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```bash
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grimer -i input_table.tsv
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```
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### BIOM file
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```bash
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grimer -i myfile.biom
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```
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### Tab-separated input table with taxonomic annotated observations (e.g. sk__Bacteria;k__;p__Actinobacteria;c__Actinobacteria...)
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```bash
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grimer -i input_table.tsv -f ";"
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```
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### Tab-separated input table with metadata
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```bash
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grimer -i input_table.tsv -m metadata.tsv
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```
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GRIMER performs analysis of microbiome studies and generates a portable and interactive dashboard integrating annotation, taxonomy and metadata with focus on contamination detection.
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### With taxonomy integration (ncbi)
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```bash
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grimer -i input_table.tsv -m metadata.tsv -t ncbi #optional -b taxdump.tar.gz
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```
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- [Installation, user manual](https://pirovc.github.io/grimer/)
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- [Live examples](https://pirovc.github.io/grimer/examples/)
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- [Pre-print](https://doi.org/10.1101/2021.06.22.449360)
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### With configuration file to setup external tools, references and annotations
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```bash
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grimer -i input_table.tsv -m metadata.tsv -t ncbi -c config/default.yaml -d -g
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```
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### Analyzing any MGnify public study
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```bash
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./grimer-mgnify.py -i MGYS00006024 -o output_folder/
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```
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## Parameters
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grimer
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optional arguments:
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-h, --help show this help message and exit
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-v, --version show program's version number and exit
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required arguments:
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-i INPUT_FILE, --input-file INPUT_FILE
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Main input table with counts (Observation table, Count table, Contingency Tables, ...) or .biom file. By default rows contain observations and columns contain
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samples (use --tranpose if your file is reversed). First column and first row are used as headers.
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main arguments:
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-m METADATA_FILE, --metadata-file METADATA_FILE
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Input metadata file in simple tabular format with samples in rows and metadata fields in columns. QIIME 2 metadata format is also accepted, with an extra row to
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define categorical and numerical fields. If not provided and --input-file is a .biom files, will attempt to get metadata from it.
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-t {ncbi,gtdb,silva,greengenes,ott}, --taxonomy {ncbi,gtdb,silva,greengenes,ott}
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Define taxonomy to convert entry and annotate samples. Will automatically download and parse or files can be provided with --tax-files.
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-b [TAX_FILES ...], --tax-files [TAX_FILES ...]
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Optional specific taxonomy files to use.
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-r [RANKS ...], --ranks [RANKS ...]
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Taxonomic ranks to generate visualizations. Use 'default' to use entries from the table directly. Default: default
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-c CONFIG, --config CONFIG
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Configuration file with definitions of references, controls and external tools.
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output arguments:
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-g, --mgnify Plot MGnify chart
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-d, --decontam Run and plot DECONTAM
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-l TITLE, --title TITLE
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Title to display on the header of the report.
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-p [{overview,samples,heatmap,correlation} ...], --output-plots [{overview,samples,heatmap,correlation} ...]
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Plots to generate. Default: overview,samples,heatmap,correlation
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-o OUTPUT_HTML, --output-html OUTPUT_HTML
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File to output report. Default: output.html
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--full-offline Embed javascript library in the output file. File will be around 1.5MB bigger but also work without internet connection. That way your report will live forever.
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general data options:
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-f LEVEL_SEPARATOR, --level-separator LEVEL_SEPARATOR
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If provided, consider --input-table to be a hierarchical multi-level table where the observations headers are separated by the indicated separator characther
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(usually ';' or '|')
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-y VALUES, --values VALUES
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Force 'count' or 'normalized' data parsing. Empty to auto-detect.
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-w, --cumm-levels Activate if input table has already cummulative values among levels.
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-s, --transpose Transpose --input-table (if samples are listed on columns and observations on rows)
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-u [UNASSIGNED_HEADER ...], --unassigned-header [UNASSIGNED_HEADER ...]
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Define one or more header names containing unsassinged/unclassified counts.
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--obs-replace [OBS_REPLACE ...]
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Replace values on table observations labels/headers (support regex). Example: '_' ' ' will replace underscore with spaces, '^.+__' '' will remove the matching
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regex.
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--sample-replace [SAMPLE_REPLACE ...]
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Replace values on table sample labels/headers (support regex). Example: '_' ' ' will replace underscore with spaces, '^.+__' '' will remove the matching regex.
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-z REPLACE_ZEROS, --replace-zeros REPLACE_ZEROS
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INT (add 'smallest count'/INT to every raw count), FLOAT (add FLOAT to every raw count). Default: 1000
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--min-frequency MIN_FREQUENCY
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Define minimum number/percentage of samples containing an observation to keep the observation [values between 0-1 for percentage, >1 specific number].
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--max-frequency MAX_FREQUENCY
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Define maximum number/percentage of samples containing an observation to keep the observation [values between 0-1 for percentage, >1 specific number].
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--min-count MIN_COUNT
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Define minimum number/percentage of counts to keep an observation [values between 0-1 for percentage, >1 specific number].
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--max-count MAX_COUNT
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Define maximum number/percentage of counts to keep an observation [values between 0-1 for percentage, >1 specific number].
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Samples options:
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-j TOP_OBS_BARS, --top-obs-bars TOP_OBS_BARS
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Top abundant observations to show in the bars.
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Heatmap and clustering options:
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-a TRANSFORMATION, --transformation TRANSFORMATION
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none (counts), norm (percentage), log (log10), clr (centre log ratio). Default: log
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-e METADATA_COLS, --metadata-cols METADATA_COLS
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How many metadata cols to show on the heatmap. Higher values makes plot slower to navigate.
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--optimal-ordering Activate optimal_ordering on linkage, takes longer for large number of samples.
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--show-zeros Do not skip zeros on heatmap. File will be bigger and iteraction with heatmap slower.
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--linkage-methods [{single,complete,average,centroid,median,ward,weighted} ...]
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--linkage-metrics [{braycurtis,canberra,chebyshev,cityblock,correlation,cosine,dice,euclidean,hamming,jaccard,jensenshannon,kulsinski,mahalanobis,minkowski,rogerstanimoto,russellrao,seuclidean,sokalmichener,sokalsneath,sqeuclidean,wminkowski,yule} ...]
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--skip-dendrogram Disable dendogram. Will create smaller files.
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Correlation options:
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-x TOP_OBS_CORR, --top-obs-corr TOP_OBS_CORR
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Top abundant observations to build the correlationn matrix, based on the avg. percentage counts/sample. 0 for all
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![recording-(2)5](https://user-images.githubusercontent.com/4673375/211857099-c9492232-c5f8-444e-aa68-70d6db8c82b4.gif)
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## Powered by
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[<img src="https://static.bokeh.org/branding/logos/bokeh-logo.png" height="60">](https://bokeh.org)
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[<img src="https://pandas.pydata.org/static/img/pandas.svg" height="40">](https://pandas.org)
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[<img src="https://raw.githubusercontent.com/scipy/scipy/master/doc/source/_static/logo.svg" height="40">](https://scipy.org)

‎config/default.yaml

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"Contaminants": "files/contaminants.yml"
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"Human-related": "files/human-related.yml"
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#controls:
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# "Positve Controls": "path/file1.tsv"
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# "Negative Controls": "path/file1.tsv"
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# controls:
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# "Negative Controls": "path/file1.tsv"
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# "Positve Controls":
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# "Metadata_Field":
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# - "Metadata_Value1"
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# - "Metadata_Value2"
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external:
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mgnify: "files/mgnify5989.tsv"
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# - "path/file1.txt"
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# - "path/file2.txt"
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# prevalence_metadata:
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# "Field1": "ValueA"
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# "Field2": "ValueB"
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# "Field1":
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# - "ValueA"
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# - "ValueB"
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# "Field2":
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# - "ValueC"
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‎docs/config.md

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# Configuration file
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GRIMER uses a configuration file to set reference sources of annotation (e.g. contaminants), controls and external tools (decontam, mgnify). The configuration can be provided with the argument `-c/--config` and it should be in the [YAML](https://yaml.org/){ target="_blank" } format.
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A basic example of a configuration file:
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```yaml
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references:
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"Contaminants": "files/contaminants.yml"
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"Human-related": "files/human-related.yml"
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controls:
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"Negative Controls": "path/file1.tsv"
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"Positve Controls":
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"Metadata_Field":
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- "Metadata_Value1"
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- "Metadata_Value2"
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external:
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mgnify: "files/mgnify5989.tsv"
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decontam:
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threshold: 0.1
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method: "frequency"
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```
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## references
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References can be provided as an external `.yml/.yaml` file in a specific format (see below) or as a text file with one taxonomic identifier or taxonomic name per line.
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```yaml
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"General Description":
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"Specific description":
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url: "www.website.com?id={}"
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ids: [1,2,3]
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```
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A real example of saliva organisms extracted from BacDive (NCBI taxonomic ids):
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```yaml
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"Human-related bacterial isolates from BacDive":
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"Saliva":
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url: "https://bacdive.dsmz.de/search?search=taxid:{}"
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ids: [152331, 113107, 157688, 979627, 45634, 60133, 157687, 1624, 1583331, 1632, 249188]
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```
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Common contaminants compiled from the literature and human-related possible sources of contamination are available in the [GRIMER repository](https://github.com/pirovc/grimer/tree/main/files){ target="_blank" }. For more information, please refer to the [pre-print](https://doi.org/10.1101/2021.06.22.449360){ target="_blank" }. If the target study overlaps with some of those annotation (e.g. study of human skin), related entries can be easily removed from the provided files to not generate redundant annotations.
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## controls
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Several control groups can be provided to annotate samples. They can be provided as a file with one sample identifier per line:
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```yaml
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controls:
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"Controls": "controls.txt"
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```
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or directly from the metadata (`-m/--metadata-file`) as a field and value(s) information:
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```yaml
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controls:
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"Other Controls":
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"sample_type": # field
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- "blank" # value
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- "control" # value
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```
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Both methods can be combined into one configuration file.
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## external
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Set the configuration and functionality of external tools executed by GRIMER.
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### mgnify
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GRIMER uses a parsed MGnify database to annotate observations and link them to the respective MGnify repository, reporting most common biome occurrences. Instructions on how to re-generate the parsed database from MGnify can be found [here](https://github.com/pirovc/grimer/tree/main/files#mgnify){ target="_blank" }.
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A [pre-parsed database](https://raw.githubusercontent.com/pirovc/grimer/main/files/mgnify5989.tsv){ target="_blank" } is available in the GRIMER repository (generated on 2022-03-09). To use it, please set the file in the configuration as follows and activate it with the `-g/--mgnify` when running GRIMER.
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```yaml
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external:
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mgnify: "files/mgnify5989.tsv"
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```
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### decontam
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GRIMER can run [DECONTAM](https://benjjneb.github.io/decontam/){ target="_blank" } with `-d/--decontam`, but some configuration is necessary. It is possible to set the threshold (P* hyperparameter) and the method (frequency, prevalence, combined).
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For the frequency/combined method, DNA frequencies for each sample have to be provided either in a `.tsv` file (sample identifier <tab> frequency) or as a metadata field. If none is provided, the sum of all counts in the input table is used for the frequency calculation.
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For the prevalence/combined method, file(s) with a list of sample identifiers or a metadata field/value can be provided. If none is provided, all samples defined in the "controls" are considered for the prevalence calculation.
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Below an example of how to set-up the configuration file for DECONTAM:
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```yaml
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external:
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decontam:
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threshold: 0.1 # P* hyperparameter threshold, values between 0 and 1
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method: "frequency" # Options: frequency, prevalence, combined
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frequency_file: "path/file1.txt"
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# frequency_metadata: "Field1"
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# prevalence_file:
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# - "path/file1.txt"
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# - "path/file2.txt"
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prevalence_metadata:
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"Field1":
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- "ValueA"
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- "ValueB"
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"Field2":
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- "ValueC"
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```
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## Using the configuration file
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Example [UgandaMaternalV3V4.16s_DADA2.taxon_abundance.biom](https://microbiomedb.org/common/downloads/release-31/c66d2dc8473138e3a737ef2ad0b25f1e6e9c0f22/UgandaMaternalV3V4.16s_DADA2.taxon_abundance.biom){ target="_blank" } file from [microbiomedb.org](https://microbiomedb.org){ target="_blank" }
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config.yml (external .yml files are available in the [GRIMER repository](https://github.com/pirovc/grimer/tree/main/files){ target="_blank" })
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```yml
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references:
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"Contaminants": "files/contaminants.yml"
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"Human-related": "files/human-related.yml"
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external:
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mgnify: "files/mgnify5989.tsv"
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decontam:
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threshold: 0.1 # [0-1] P* hyperparameter
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method: "frequency" # frequency, prevalence, combined
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```
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Running GRIMER with DECONTAM and MGnify integration
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```bash
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grimer --input-file UgandaMaternalV3V4.16s_DADA2.taxon_abundance.biom \
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--config config.yml \
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--decontam --mgnify \
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--taxonomy ncbi \
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--ranks superkingdom phylum class order family genus species
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```

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