.. _usage:

Basic Usage
===========

This section shows how to use **GeneCover** to compute gene-gene correlation and select marker genes.

Required Imports
----------------

.. code-block:: python

    from genecover import *
    import numpy as np

Main Inputs
-----------

- **data**: A NumPy array of shape `(N, d)` storing gene expression measurements,
  where `N` is the number of cells/spots and `d` is the number of genes.
  
  Alternatively, `data` can be a list of such arrays (e.g., one per donor or batch), as long as they all have the same number of genes.

- **w**: A 1D array of length `d` representing the cost (weight) of selecting each gene.  
  A simple choice is:

  .. code-block:: python

      w = np.ones(data.shape[1])

- **solver:** A string indicating which solver to use for gene selection. Options include:
    - `"Gurobi"`: the Gurobi solver for integer programming.
    - `"SCIP"`: the SCIP solver for integer programming.
    - `"Greedy"`: the greedy heuristic for gene selection.

Gene-Gene Correlation
---------------------

Compute the gene-gene correlation matrix:

.. code-block:: python

    corr_mat = gene_gene_correlation(data)

Gene Selection (Single Run)
---------------------------

Obtain 100 marker genes using the standard Gurobi optimization:

.. code-block:: python

    markers = GeneCover(
        num_marker=100,
        corr_mat=corr_mat,
        w=w,
        solver=solver)


Iterative Gene Selection
------------------------

Select 300 markers in three iterative steps (e.g., 100 markers per round):

.. code-block:: python

    iterative_markers = Iterative_GeneCover(
        incremental_sizes=[100, 100, 100],
        corr_mat=corr_mat,
        w=w,
        solver=solver)