From 457a5f5711d2f594742e6d6793683286bff7a393 Mon Sep 17 00:00:00 2001 From: David Merrell Date: Sat, 9 May 2020 18:53:13 -0500 Subject: [PATCH] README clarifications --- README.md | 36 +++++++++++++++++++++--------------- 1 file changed, 21 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 176a2fd..cf6aefc 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ git clone git@github.com:gitter-lab/ssps.git * Find additional installation instructions here: https://julialang.org/downloads/platform/. * Use `Pkg` -- Julia's package manager -- to install the project's julia dependencies: ``` - $ cd graph-ppl/julia-project + $ cd ssps/julia-project $ julia --project=. _ _ _ _(_)_ | Documentation: https://docs.julialang.org @@ -49,15 +49,6 @@ git clone git@github.com:gitter-lab/ssps.git julia> exit() ``` -# Running SSPS - -Follow these steps to run SSPS on your dataset. You will need -* a CSV file (tab separated) containing your time series data -* a CSV file (comma separated) containing your prior edge confidences. - -1. `cd` to the `run_ssps` directory -2. Configure the parameters in `ssps_config.yaml` as appropriate -3. run Snakemake: `$ snakemake`. # Reproducing the analyses @@ -104,7 +95,7 @@ Hence, the analyses entail some extra setup: 3. Check whether **MATLAB** is installed. * If you don't have MATLAB, then you won't be able to run the [exact DBN inference method of Hill et al., 2012](https://academic.oup.com/bioinformatics/article/28/21/2804/235527). - * You'll + * You'll need to comment out the `hill` method wherever it appears in `analysis_config.yaml`. After completing this additional setup, we are ready to **run the analyses**. 1. Make any necessary modifications to the configuration file: `analysis_config.yaml`. @@ -113,17 +104,32 @@ After completing this additional setup, we are ready to **run the analyses**. * If you're running the analyses on your local host, simply move to the directory containing `Snakefile` and call `snakemake`. ``` - (my_environment) $ cd graph-ppl + (my_environment) $ cd ssps (my_environment) $ snakemake ``` + * Since Julia is a dynamically compiled language, some time will be devoted to compilation when you run SSPS for the first time. You may see some warnings in `stdout` -- this is normal. * If you're running the analyses on a cluster, call snakemake with the same **Snakemake profile** you found [here](https://github.com/Snakemake-Profiles/doc): ``` - (my_environment) $ cd graph-ppl - (my_environment) $ snakemake --profile + (my_environment) $ cd ssps + (my_environment) $ snakemake --profile YOUR_PROFILE_NAME ``` (You will probably need to edit the job submission parameters in the profile's `config.yaml` file.) -3. Relax. It will probably take a few thousand cpu-hours to run all of the analyses. +4. Relax. It will probably take a few thousand cpu-hours to run all of the analyses. + + +# Running SSPS on your data + +Follow these steps to run SSPS on your dataset. You will need +* a CSV file (tab separated) containing your time series data +* a CSV file (comma separated) containing your prior edge confidences. +* Optional: a JSON file containing a list of variable names (i.e., node names). + +1. Install the **python3.7 dependencies** if you haven't already. Find detailed instructions above. +2. `cd` to the `run_ssps` directory +3. Configure the parameters in `ssps_config.yaml` as appropriate +4. run Snakemake: `$ snakemake`. + # Licenses