<tool id="naivestates" name="naivestates" version="@VERSION@.3" 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>