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Description:

gpseer 0.3.3

GPSeer
Simple software for inferring missing data in sparsely measured genotype-phenotype maps

Basic Usage
Install gpseer using pip:
pip install gpseer

To use as a command line, call gpseer on an input .csv file containing genotype-phenotype data.
The API Demo.ipynb
demonstrates how to use GPSeer in a Jupyter notebook.

Documentation
Tutorial

Downloading the example
To get started, use GPSeer's fetch-example command to download an example from its Github repo.
Download the gpseer example and explore the example input data:
# fetch data from Github page.
> gpseer fetch-example

[GPSeer] Downloading files to /examples...
[GPSeer] └──>: 100%|██████████████████| 3/3 [00:00<00:00, 9.16it/s]
[GPSeer] └──> Done!

# Change into the example directory and checkout the files that were downloaded
> cd examples/
> ls

API Demo.ipynb
example-full.csv
example-test.csv
example-train.csv
Generate Dataset.ipynb
genotypes.txt
pfcrt-raw-data.csv

Predicting missing data using ML model.
Estimate the maximum likelihood additive model on the training set and predict all missing genotypes. The predictions will be written to a file named "example-train_predictions.csv".
> gpseer estimate-ml example-train.csv

[GPSeer] Reading data from example-train.csv...
[GPSeer] └──> Done reading data.
[GPSeer] Constructing a model...
[GPSeer] └──> Done constructing model.
[GPSeer] Fitting data...
[GPSeer] └──> Done fitting data.
[GPSeer] Predicting missing data...
[GPSeer] └──> Done predicting.
[GPSeer] Calculating fit statistics...
[GPSeer]

Fit statistics:
---------------

parameter value
0 num_genotypes 128
1 num_unique_mutations 8
2 explained_variation 0.985186
3 num_parameters 9
4 num_obs_to_converge 2.82714
5 threshold None
6 spline_order None
7 spline_smoothness None
8 epistasis_order 1

[GPSeer]

Convergence:
------------

mutation num_obs num_obs_above fold_target converged
0 F0K 64 64 22.637735 True
1 S1Y 69 69 24.406308 True
2 Q2T 63 63 22.284020 True
3 R3V 70 70 24.760023 True
4 N4D 62 62 21.930306 True
5 A5C 69 69 24.406308 True
6 C6D 65 65 22.991450 True
7 C7A 64 64 22.637735 True

[GPSeer] └──> Done.
[GPSeer] Writing phenotypes to example-train_predictions.csv...
[GPSeer] └──> Done writing predictions!
[GPSeer] Writing plots...
[GPSeer] Writing example-train_correlation-plot.pdf...
[GPSeer] Writing example-train_phenotype-histograms.pdf...
[GPSeer] └──> Done plotting!
[GPSeer] GPSeer finished!

Compute the predictive power of the model by cross-validation
Estimate how well your model is predicting data using the "cross-validate"
subcommand. Try the example below where we generate 100 subsets from the data
and compute your prediction scores.
> gpseer cross-fit example-test.csv

[GPSeer] Reading data from example-train.csv...
[GPSeer] └──> Done reading data.
[GPSeer] Fitting all data data...
[GPSeer] └──> Done fitting data.
[GPSeer] Sampling the data...
[GPSeer] └──>: 100%|████████████████████| 100/100 [00:03<00:00, 25.90it/s]
[GPSeer] └──> Done sampling data.
[GPSeer] Plotting example-train_cross-validation-plot.pdf...
[GPSeer] └──> Done writing data.
[GPSeer] Writing scores to example-train_cross-validation-scores.csv...
[GPSeer] └──> Done writing data