Quick Start

This is a very quick introduction to gpseer that demonstrates the basic modes of the software. A complete tutorial–including how to interpret the outputs of the calculation–is given on the tutorial page.

The API Demo.ipynb demonstrates how to use GPSeer in a Jupyter notebook.

Downloading examples

Download the gpseer examples:

> gpseer fetch-example

[GPSeer] Downloading files to /examples...
[GPSeer] └──>: 100%|██████████████████| 3/3 [00:00<00:00,  9.16it/s]
[GPSeer] └──> Done!
> 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 unmeasured phenotypes of from a collection of measured phenotypes

Estimate the maximum likelihood additive model on the training set and predict all missing genotypes.

> gpseer estimate-ml example-train.csv

[GPSeer] Reading data from example-test.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  0.984436
3   num_obs_to_converge   2.89004
4             threshold      None
5          spline_order      None
6     spline_smoothness      None
7       epistasis_order         1


[GPSeer]

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

  mutation  num_obs  fold_target  converged
0      K0F       64    22.145002       True
1      S1Y       59    20.414924       True
2      T2Q       63    21.798986       True
3      R3V       58    20.068908       True
4      D4N       62    21.452971       True
5      C5A       69    23.875080       True
6      D6C       65    22.491018       True
7      A7C       64    22.145002       True


[GPSeer] └──> Done.
[GPSeer] Writing phenotypes to example-test_predictions.csv...
[GPSeer] └──> Done writing predictions!
[GPSeer] Writing plots...
[GPSeer] └──> Done plotting!
[GPSeer] GPSeer finished!

Your predictions will be in example-train_predictions.csv. Several other .csv files and graphs will be generated. For a full description of this output, see the Input/Output page.

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 10 subsets from the data and compute our prediction scores.

> gpseer goodness-of-fit example-train.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, 26.76it/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.
[GPSeer] GPSeer finished!

A cross-validation plot called example_train_cross-validation-plot.pdf will be created, along with a csv file. For a full description of this output, see the Input/Output page.