Agecrack-ng is a tool for searching and extracting age-related features from data.
# prepare environment
brew install mmseqs2
# clone
git clone [email protected]:moozeq/agecrack-ng.git
cd agecrack-ng
# setup venv
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# run
./agecrack-ng.py -husage: agecrack-ng.py [-h] [--mode {predictor,ontology,ontology-parse,vectors,prepare,mmseqs-estimation}] [--model {rf,encv,en}] [--filters FILTERS [FILTERS ...]]
[--filter-class FILTER_CLASS] [--exclude EXCLUDE [EXCLUDE ...]] [--extract-threshold EXTRACT_THRESHOLD] [--anage ANAGE] [--ncbi NCBI] [--skip]
[--count-proteins] [--ontology-plots-freqs] [--reload] [--models-reuse] [--models-params MODELS_PARAMS] [--models-rand MODELS_RAND]
[--models-stratify] [--models-bins MODELS_BINS] [--models-plots-show] [--models-plots-unprocess] [--models-plots-annotate]
[--models-plots-annotate-threshold MODELS_PLOTS_ANNOTATE_THRESHOLD] [--models-plots-clusters-count MODELS_PLOTS_CLUSTERS_COUNT]
[--mmseq-params MMSEQ_PARAMS] [--mmseq-threshold MMSEQ_THRESHOLD] [--mmseq-vectors-mode {count,bool}] [--mmseq-force] [--plot-anage-hists] [-v]
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Tool for searching and extracting age-related features from data.
optional arguments:
-h, --help show this help message and exit
--mode {predictor,ontology,ontology-parse,vectors,prepare,mmseqs-estimation}
Select mode for running the program:
- "predictor" gives single best predictor for longevity based on predefined parameters
- "ontology" runs analysis for clusters ontology and correlation with longevity
- "ontology-parse" parses files obtained in ontology analysis
- "vectors" produces additional visualization of species genes vectors
- "prepare" download all data, cluster them and prepare for further analysis
- "mmseqs-estimation" produces additional plots for mmseqs params estimation
(default: predictor)
--model {rf,encv,en} ML model (default: rf)
--filters FILTERS [FILTERS ...]
Filters used for extracting proteins sequences, examples in "filters.json" file, when list with empty string is provided (`['']`) - no filtering is applied (default: [''])
--filter-class FILTER_CLASS
Filter species from specific phylo class (e.g. "Mammalia") (default: None)
--exclude EXCLUDE [EXCLUDE ...]
List of excluded species from analysis (default: [])
--extract-threshold EXTRACT_THRESHOLD
Filter out species with count of genes below threshold (default: 1)
--anage ANAGE AnAge database file (default: data/anage_data.txt)
--ncbi NCBI NCBI eukaryotes database file (default: data/eukaryotes.txt)
--skip Skip downloading and extracting part, use it to speed up when trying different models, (option is omitted when running mmseq) (default: False)
--count-proteins Count proteins for proper log messages (can impact performance greatly) (default: False)
--ontology-plots-freqs
Show and save plot for clusters frequencies at first 10 places instead importance (default: False)
--reload Reload produced files, set when changing thresholds (default: False)
--models-reuse Reuse ML models from files if exist (default: False)
--models-params MODELS_PARAMS
Specify params for model as json dict, if not specified the ones from "grid_params.json" will be used (default: None)
--models-rand MODELS_RAND
Random state for splitting data for training and testing (default: 17)
--models-stratify Try to stratify dataset using bins (default: False)
--models-bins MODELS_BINS
How many bins for stratifying data, if not specified - number of species divided by 2 (default: 0)
--models-plots-show Show plots for each model (default: False)
--models-plots-unprocess
Unprocess data for models predicted plots (default: False)
--models-plots-annotate
Annotate points on models plots with species names (default: False)
--models-plots-annotate-threshold MODELS_PLOTS_ANNOTATE_THRESHOLD
Difference between predicted and known lifespan that should be annotated (default: 0.5)
--models-plots-clusters-count MODELS_PLOTS_CLUSTERS_COUNT
Up to how many most important clusters should be shown on an ontology plot (default: 30)
--mmseq-params MMSEQ_PARAMS
Specify params for mmseqs as json dict, params = "min_seq_id", "c", "cov_mode" (default: {"min_seq_id": 0.8, "c": 0.8, "cov_mode": 0})
--mmseq-threshold MMSEQ_THRESHOLD
Clusters under strength of this threshold will be filter out (default: 0)
--mmseq-vectors-mode {count,bool}
Vectors mode for species, use "bool" to obtain boolean vectors instead of integer vectors with sequences counts (default: count)
--mmseq-force Force re-running mmseq (default: False)
--plot-anage-hists Plot AnAge database histograms (default: False)
-v, --verbose Increase verbosity (default: 0)
# best vertebrates predictor
./agecrack-ng.py -vv --mode predictor --model rf --filters repair --mmseq-vectors-mode bool --models-rand 1 --mmseq-params '{"min_seq_id": 0.8, "c": 0.2, "cov_mode": 2}' --models-params '{"n_estimators": 300, "max_depth": 18}'
# plots on unprocessed data
./agecrack-ng.py -vv --mode predictor --model rf --filters repair --mmseq-vectors-mode bool --models-rand 1 --mmseq-params '{"min_seq_id": 0.8, "c": 0.2, "cov_mode": 2}' --models-params '{"n_estimators": 300, "max_depth": 18}' --models-plots-unprocess
# gather multiple models and analyse them all
./agecrack-ng.py -vv --mode ontology --model rf --filters repair --mmseq-vectors-mode bool --models-rand 1 --mmseq-params '{"min_seq_id": 0.8, "c": 0.2, "cov_mode": 2}'
# best mammalia predictor
./agecrack-ng.py -vv --mode predictor --model rf --filter-class Mammalia --exclude 'Homo sapiens' --extract-threshold 1000 --models-plots-annotate --models-rand 17 --mmseq-params '{"min_seq_id": 0.8, "c": 0.8, "cov_mode": 0}' --models-params '{"n_estimators": 30, "max_depth": 7}'
# plots on unprocessed data
./agecrack-ng.py -vv --mode predictor --model rf --filter-class Mammalia --exclude 'Homo sapiens' --extract-threshold 1000 --models-plots-annotate --models-plots-annotate --models-plots-annotate-threshold 15 --models-rand 17 --mmseq-params '{"min_seq_id": 0.8, "c": 0.8, "cov_mode": 0}' --models-params '{"n_estimators": 30, "max_depth": 7}'
# gather multiple models and analyse them all
./agecrack-ng.py -vv --mode ontology --model rf --filter-class Mammalia --exclude 'Homo sapiens' --extract-threshold 1000 --models-plots-annotate --models-rand 17 --mmseq-params '{"min_seq_id": 0.8, "c": 0.8, "cov_mode": 0}'
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