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docs: tutorials for COMBINE25 #369
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581b8e9
plan for COMBINE
ntalluri 01d9a45
delete the docs line
ntalluri 87c001a
start of organizing tutorial
ntalluri aaa9098
new updates on intro
ntalluri ce8d8f8
clean up
ntalluri 24040e9
added in a couple of steps for basic
ntalluri 83d175d
more updates
ntalluri 0cdf615
more ideas
ntalluri 144fc68
more additions for the configs
ntalluri f55a101
restructure and working on beginner
ntalluri 85e91d5
moving things around
ntalluri 9acc8f2
wrote up step 1 and step 2
ntalluri 54a1be5
update config and clean up headers
ntalluri c20f634
plan for step 3
ntalluri cf434b6
updated introduction and added more to beginner
ntalluri 5111e4a
remove config/beginner.yaml
ntalluri 25bc2cf
Merge branch 'main' of github.com:ntalluri/spras into tutorial
ntalluri 2cbdbb4
finished beginner
ntalluri 0cb8e9b
refactor
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hash_length: 7 | ||
container_framework: docker | ||
unpack_singularity: false | ||
container_registry: | ||
base_url: docker.io | ||
owner: reedcompbio | ||
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# Each algorithm has an 'include' parameter. By toggling 'include' to true/false the user can change | ||
# which algorithms are run in a given experiment. | ||
# | ||
# algorithm-specific parameters are embedded in lists so that users can specify multiple. If multiple | ||
# parameters are specified then the algorithm will be run as many times as needed to cover all parameter | ||
# combinations. For instance if we have the following: | ||
# - name: "myAlg" | ||
# params: | ||
# include: true | ||
# a: [1,2] | ||
# b: [0.5,0.75] | ||
# | ||
# then myAlg will be run on (a=1,b=0.5),(a=1,b=0.75),(a=2,b=0.5), and (a=2,b=0,75). Pretty neat, but be | ||
# careful: too many parameters might make your runs take a long time. | ||
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algorithms: | ||
- name: "pathlinker" | ||
params: | ||
include: true | ||
run1: | ||
k: 1 | ||
# run2: # uncomment for step 3.2 | ||
# k: [10, 100] # uncomment for step 3.2 | ||
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# Here we specify which pathways to run and other file location information. | ||
# Assume that if a dataset label does not change, the lists of associated input files do not change | ||
datasets: | ||
- # Labels can only contain letters, numbers, or underscores | ||
label: egfr | ||
node_files: ["tps-egfr-prizes.txt"] # the input nodes | ||
edge_files: ["phosphosite-irefindex13.0-uniprot.txt"] # the interactome | ||
# Placeholder | ||
other_files: [] | ||
# Relative path from the spras directory where these files live | ||
data_dir: "input" | ||
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reconstruction_settings: | ||
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# Set where everything is saved | ||
locations: | ||
reconstruction_dir: "output/basic" | ||
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analysis: | ||
# Create one summary per pathway file and a single summary table for all pathways for each dataset | ||
summary: | ||
include: false # set to true for step 3.3 | ||
# Create Cytoscape session file with all pathway graphs for each dataset | ||
cytoscape: | ||
include: false # set to true for step 3.3 |
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Advanced Capabilities and Features | ||
====================================== | ||
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More like these are all the things we can do with this, but will not be showing | ||
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- mention parameter tuning | ||
- say that parameters are not preset and need to be tuned for each dataset | ||
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CHTC integration | ||
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Anything not included in the config file | ||
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1. Global Workflow Control | ||
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Sets options that apply to the entire workflow. | ||
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- Examples: the container framework (docker, singularity, dsub) and where to pull container images from | ||
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running spras with multiple parameter combinations with multiple algorithms on multiple Datasets | ||
- for the tutorial we are only doing one dataset |
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CHTC is local to our university. The way to say it may be Snakemake integration with cloud and high-throughput computing resources, which we've prototyped in our local cluster. If we start testing in OSG that would be different because many people are eligible for accounts.