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:
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:
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