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structure_index

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Structure Index code repository

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# Structure Index

Welcome! This repository hosts the implementation for the Structure Index (SI), a graph-based topological metric able to quantify the amount of structure present at the distribution of a given feature over a point cloud in an arbitrary D-dimensional space. See the [publication](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011768) for specific details and follow [this notebook](https://colab.research.google.com/github/PridaLab/structure_index/blob/main/demos/structure_index_demo.ipynb) for a step by step demo.

## The method

Identifying the structure (or lack thereof) of the distribution of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., auditory coding) or in anatomical image segmentation.

The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood. It can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. The following image illustrates the concepts behind this method:

![sI_github_F1](https://user-images.githubusercontent.com/48024498/203568627-fd912bb2-fc94-4c1f-bfe3-85247dc1cde5.png)

**A**, Feature gradient distribution in a 2D-ellipsoid data cloud. Each point in the data cloud is assigned to a group associated with a feature bin value (bin-group). **B**, **C**, Next, the overlapping matrix between bin-groups is computed. **D**, The overlapping matrix represents a connection graph between bin-groups, where structure (overlapping, clustering, etc..) can be quantified using the SI from 0 (random, equivalent to full overlapping) to 1 (maximal separation, equivalent to zero overlapping between bins). **E**, The case of a randomly distributed feature in a 2D data cloud.
 
## How to use it

[This notebook](https://colab.research.google.com/github/PridaLab/structure_index/blob/main/demos/structure_index_demo.ipynb) illustrates one example of how to use the Structure Index to quantify structure. Follow and execute each block to generate syntethic data and to compute the structure present on it. Moreover it includes a section to use the built-in function of the repo 'draw_graph' to visualize the resulting weighted directed graph.

In sort, the way to compute the structure index is as follows:
```
SI, bin_label, overlap_mat, shuf_SI = compute_structure_index(data, label)
```

### Parameters
        data: numpy 2d array of shape [n_samples,n_dimensions]
            Array containing the signal

label: numpy 2d array of shape [n_samples,n_features]
            Array containing the labels of the data. It can either be a 
            column vector (scalar feature) or a 2D array (vectorial feature)

### Optional parameters
        n_bins: integer (default: 10)
            number of bin-groups the label will be divided into (they will 
            become nodes on the graph). For vectorial features, if one wants 
            different number of bins for each entry then specify n_bins as a 
            list (i.e. [10,20,5]). Note that it will be ignored if 
            'discrete_label' is set to True.

dims: list of integers or None (default: None)
            list of integers containing the dimensions of data along which the 
            structure index will be computed. Provide None to compute it along 
            all dimensions of data.
        
        distance_metric: str (default: 'euclidean')
            Type of distance used to compute the closest n_neighbors. See 
            'distance_options' for currently supported distances.

n_neighbors: int (default: 15)
            Number of neighbors used to compute the overlapping between 
            bin-groups. This parameter controls the tradeoff between local and 
            global structure.

discrete_label: boolean (default: False)
            If the label is discrete, then one bin-group will be created for 
            each discrete value it takes. Note that if set to True, 'n_bins' 
            parameter will be ignored.
        
        num_shuffles: int (default: 100)
            Number of shuffles to be computed. Note it must fall within the 
            interval [0, np.inf).

verbose: boolean (default: False)
            Boolean controling whether or not to print internal process..
            
### Returns:
        SI: float
            structure index

bin_label: tuple
            Tuple containing:
                [0] Array indicating the bin-group to which each data point has 
                    been assigned.
                [1] Array indicating feature limits of each bin-group. Size is
                [number_bin_groups, n_features, 3] where the last dimension 
                contains [bin_st, bin_center, bin_en]

overlap_mat: numpy 2d array of shape [n_bins, n_bins]
            Array containing the overlapping between each pair of bin-groups.

shuf_SI: numpy 1d array of shape [num_shuffles,]
            Array containing the structure index computed for each shuffling 
            iteration.

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