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"planemo upload for repository https://github.com/ohsu-comp-bio/naivestates commit 392f57d212a7499bf1d3e421112a32a56635bc67-dirty"
author | perssond |
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date | Fri, 12 Mar 2021 00:20:13 +0000 |
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children | a62b0c62270e |
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<tool id="naivestates" name="naivestates" version="@VERSION@.2" profile="17.09"> <description> Inference of cell states using Naive Bayes</description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements"/> @VERSION_CMD@ <command detect_errors="exit_code"><![CDATA[ @CMD_BEGIN@ -i '$counts' #if $markers -m $markers #end if --mct $mct -p $plots #if $id --id $id #end if --log $log #if $sfx --sfx $sfx #end if #if $umap --umap #end if -o . && mv *-states.csv states.csv; #if $plots != "off" mv plots/*-probs.${plots} plots/probs.${plots}; mv plots/*-summary.${plots} plots/summary.${plots}; mv plots/*-allfits.${plots} plots/allfits.${plots}; #end if ]]></command> <inputs> <param name="counts" type="data" format="csv" label="Quantified Cell Matrix"/> <param name="markers" type="data" format="txt" optional="true" label="Markers to model"/> <param name="mct" type="data" format="csv" label="Marker-State Association Map"/> <param name="plots" type="select" label="Generate plots showing the fit"> <option selected="true" value="png">png</option> <option value="pdf">pdf</option> <option value="off">off</option> </param> <param name="id" type="text" value="" label="Column name containing cell IDs"/> <param name="log" type="select" label="Log Transform" help="Whether to apply a log transform"> <option selected="true" value="auto">auto</option> <option value="yes">yes</option> <option value="no">no</option> </param> <param name="sfx" type="text" value="_cellMask" optional="true" label="Common suffix" help="Common suffix on marker columns (e.g., _cellMask)"/> <param name="umap" type="boolean" checked="true" label="Generate UMAP plots"/> </inputs> <outputs> <data format="csv" name="states" from_work_dir="states.csv" label="${tool.name} on ${on_string}: States CSV"/> <data format="png" name="probs-png" from_work_dir="plots/probs.png" label="${tool.name} on ${on_string}: Probabilities"> <filter>plots == 'png'</filter> </data> <data format="png" name="summary-png" from_work_dir="plots/summary.png" label="${tool.name} on ${on_string}: Summary"> <filter>plots == 'png'</filter> </data> <data format="png" name="allfits-png" from_work_dir="plots/allfits.png" label="${tool.name} on ${on_string}: AllFits"> <filter>plots == 'png'</filter> </data> <data format="pdf" name="probs-pdf" from_work_dir="plots/probs.pdf" label="${tool.name} on ${on_string}: Probabilities"> <filter>plots == 'pdf'</filter> </data> <data format="pdf" name="summary-pdf" from_work_dir="plots/summary.pdf" label="${tool.name} on ${on_string}: Summary"> <filter>plots == 'pdf'</filter> </data> <data format="pdf" name="allfits-pdf" from_work_dir="plots/allfits.pdf" label="${tool.name} on ${on_string}: AllFits"> <filter>plots == 'pdf'</filter> </data> </outputs> <help><![CDATA[ naivestates - Inference of cell states using Naive Bayes This work is supported by the NIH Grant 1U54CA225088: Systems Pharmacology of Therapeutic and Adverse Responses to Immune Checkpoint and Small Molecule Drugs and by the NCI grant 1U2CCA233262: Pre-cancer atlases of cutaneous and hematologic origin (PATCH Center). Introduction naivestates is a label-free, cluster-free tool for inferring cell types from quantified marker expression data, based on known marker <-> cell type associations. The tool is designed to be run as a Docker container, but can also be installed in a Conda environment or as an R package. naivestates expects as input information about marker expression on a per-cell basis, provided in .csv format. One of the columns must contain cell IDs. An example input file may look as follows: CellID,KERATIN,FOXP3,SMA 1,64.18060200668896,193.00334448160535,303.5016722408027 2,54.850202429149796,151.19433198380565,176.3846153846154 3,63.94712643678161,210.43218390804597,483.9448275862069 4,142.01320132013203,227.85808580858085,420.76897689768975 5,56.66379310344828,197.01896551724138,343.7810344827586 6,69.97454545454545,187.59636363636363,267.9709090909091 7,67.57754010695187,185.63368983957218,351.7914438502674 8,64.012,190.02,349.348 9,56.9622641509434,159.79245283018867,236.43867924528303 ... Installation Download the container image Pull the latest version with docker pull labsyspharm/naivestates Alternatively, you can pull a specific version, which is recommended to ensure reproducibility of your analyses. For example, v1.2.0 can be pulled with docker pull labsyspharm/naivestates:1.2.0 Examine the tool usage instructions docker run --rm labsyspharm/naivestates:1.2.0 /app/main.R -h replacing 1.2.0 with the version you are working with. Omit :1.2.0 entirely if you pulled the latest version above. The flag --rm tells Docker to delete the container instance after it finishes displaying the help message. Basic usage At minimum, the tool requires an input file and the list of marker names: docker run --rm -v /path/to/data/folder:/data labsyspharm/naivestates:1.2.0 \ /app/main.R -i /data/myfile.csv -m aSMA,CD45,panCK where we can make a distinction between Docker-level arguments: --rm once again cleans up the container instance after it finishes running the code -v /path/to/data/folder:/data maps the local folder containing your data to /data inside the container :1.2.0 specifies the container version that we pulled above and tool-level arguments: -i /data/myfile.csv specifies which data file to process -m aSMA,CD45,panCK specifies the markers of interest (NOTE: comma-delimited, no spaces) If there is a large number of markers, place their names in a standalone file markers.txt with one marker per line. Ensure that the file lives in /path/to/data/folder/ and modify the Docker call to use the new file: docker run --rm -v /path/to/data/folder:/data labsyspharm/naivestates:1.2.0 \ /app/main.R -i /data/myfile.csv -m /data/markers.txt Additional parameters The following parameters are optional, but may be useful in certain scenarios: --plots <off|pdf|png> - (default: off) Produces QC plots of individual marker fits and summary UMAP plots in .png or .pdf format. --id - (default: CellID) Name of the column that contains cell IDs --log <yes|no|auto> - (default: auto) When a log10 transformation should be applied prior to fitting the data. The tool will do this automatically if it detects large values. Use --log no to force the use of original, non-transformed values instead. -o - (default: /data) Alternative output directory. (Note that any file written to a directory that wasn't mapped with docker -v will not persist when the container is destroyed.) --mct - The tool has a basic marker -> cell type (mct) mapping in typemap.csv. More sophisticated mct mappings can be defined by creating a custom-map.csv file with two columns: Marker and State. Ensure that custom-map.csv is in /path/to/data/folder and point the tool at it with --mct (e.g., /app/main.R -i /data/myfile.csv --mct /data/custom-map.csv -m aSMA,CD45,panCK) Alternative execution environments Running in a Conda environment If you are working in a computational environment that doesn't support Docker, the repository provides a Conda-based alternative. Ensure that conda is installed on your system, then 1) clone this repository, 2) instantiate the conda environment and 3) install the tool. git clone https://github.com/labsyspharm/naivestates.git cd naivestates conda env create -f conda.yml conda activate naivestates R -s -e "devtools::install_github('labsyspharm/naivestates')" The tool can now be used as above by running main.R: ./main.R -h ./main.R -i /path/to/datafile.csv -m aSMA,CD45,panCK Running as an R package The tool can also be installed as an R package directly from GitHub: if( !require(devtools) ) install.packages("devtools") devtools::install_github( "labsyspharm/naivestates" ) Example usage: library( tidyverse ) library( naivestates ) # Load the original data X <- read_csv( "datafile.csv" ) # Fit models to channels aSMA, CD45 and panCK # Specify that cell IDs are in column CellID GMM <- GMMfit( X, CellID, aSMA, CD45, panCK ) # Plot a fit to one of the markers plotFit( GMM, "CD45" ) # Write out the results to results.csv GMMreshape(GMM) %>% write_csv( "results.csv" ) OHSU Wrapper Repo: https://github.com/ohsu-comp-bio/naivestates ]]></help> <expand macro="citations" /> </tool>