[![Example](https://img.shields.io/badge/Usage-example-green)](https://github.com/shenwanxiang/bidd-aggmap/tree/master/paper/example)

[![Documentation Status](https://readthedocs.org/projects/bidd-aggmap/badge/?version=latest)](https://bidd-aggmap.readthedocs.io/en/latest/?badge=latest)

[![Downloads](https://pepy.tech/badge/aggmap)](https://pepy.tech/project/aggmap)

[![PyPI version](https://badge.fury.io/py/aggmap.svg)](https://badge.fury.io/py/aggmap)

[![Paper](https://img.shields.io/badge/paper-Nucleic_Acids_Research-blue)](https://academic.oup.com/nar/article/50/8/e45/6517966?login=false)

<img src="./docs/images/logo.png" align="left" height="170" width="130" >

# Jigsaw-like AggMap

## A Robust and Explainable Omics Deep Learning Tool

----

### Installation (Only on Linux system) 

install aggmap by:

```bash

# create an aggmap env

conda create -n aggmap python=3.8

conda activate aggmap

pip install --upgrade pip

pip install aggmap==1.2.1

```

----

### Usage

```python

import pandas as pd

from sklearn.datasets import load_breast_cancer

from aggmap import AggMap, AggMapNet

# Data loading

data = load_breast_cancer()

dfx = pd.DataFrame(data.data, columns=data.feature_names)

dfy = pd.get_dummies(pd.Series(data.target))

# AggMap object definition, fitting, and saving 

mp = AggMap(dfx, metric = 'correlation')

mp.fit(cluster_channels=5, emb_method = 'umap', verbose=0)

mp.save('agg.mp')

# AggMap visulizations: Hierarchical tree, embeddng scatter and grid

mp.plot_tree()

mp.plot_scatter(enabled_data_labels=True, radius=5)

mp.plot_grid(enabled_data_labels=True)

# Transoformation of 1d vectors to 3D Fmaps (-1, w, h, c) by AggMap

X = mp.batch_transform(dfx.values, n_jobs=4, scale_method = 'minmax')

y = dfy.values

# AggMapNet training, validation, early stopping, and saving

clf = AggMapNet.MultiClassEstimator(epochs=50, gpuid=0)

clf.fit(X, y, X_valid=None, y_valid=None)

clf.save_model('agg.model')

# Model explaination by simply-explainer: global, local

simp_explainer = AggMapNet.simply_explainer(clf, mp)

global_simp_importance = simp_explainer.global_explain(clf.X_, clf.y_)

local_simp_importance = simp_explainer.local_explain(clf.X_[[0]], clf.y_[[0]])

# Model explaination by shapley-explainer: global, local

shap_explainer = AggMapNet.shapley_explainer(clf, mp)

global_shap_importance = shap_explainer.global_explain(clf.X_)

local_shap_importance = shap_explainer.local_explain(clf.X_[[0]])

```

### How It Works?

- AggMap flowchart of feature mapping and agglomeration into ordered (spatially correlated) multi-channel feature maps (Fmaps)

![how-it-works](https://raw.githubusercontent.com/shenwanxiang/bidd-aggmap/master/docs/images/hiw.jpg)

**a**, AggMap flowchart of feature mapping and aggregation into ordered (spatially-correlated) channel-split feature maps (Fmaps).**b**, CNN-based AggMapNet architecture for Fmaps learning. **c**, proof-of-concept illustration of AggMap restructuring of unordered data (randomized MNIST) into clustered channel-split Fmaps (reconstructed MNIST) for CNN-based learning and important feature analysis. **d**, typical biomedical applications of AggMap in restructuring omics data into channel-split Fmaps for multi-channel CNN-based diagnosis and biomarker discovery (explanation `saliency-map` of important features).

----

### Proof-of-Concepts of reconstruction ability on MNIST Dataset

<video width="320" height="240" controls>

  <source src="https://www.shenwx.com/files/Video_MNIST.mp4" type="video/mp4">

</video>

- It can reconstruct to the original image from completely randomly permuted (disrupted) MNIST data:

![reconstruction](https://raw.githubusercontent.com/shenwanxiang/bidd-aggmap/master/docs/images/reconstruction.png)

`Org1`: the original grayscale images (channel = 1), `OrgRP1`: the randomized images of Org1 (channel = 1), `RPAgg1, 5`: the reconstructed images of `OrgPR1` by AggMap feature restructuring (channel = 1, 5 respectively, each color represents features of one channel). `RPAgg5-tkb`: the original images with the pixels divided into 5 groups according to the 5-channels of `RPAgg5` and colored in the same way as `RPAgg5`.

----

### The effect of the number of channels on model performance 

- Multi-channel Fmaps can boost the model performance notably:

![channel_effect](https://raw.githubusercontent.com/shenwanxiang/bidd-aggmap/master/docs/images/channel_effect.png)

The performance of AggMapNet using different number of channels on the `TCGA-T (a)` and `COV-D (b)`. For `TCGA-T`, ten-fold cross validation average performance, for `COV-D`, a fivefold cross validation was performed and repeat 5 rounds using different random seeds (total 25 training times), their average performances of the validation set were reported.

----

### Example for Restructured Fmaps

- The example on WDBC dataset: click [here](https://github.com/shenwanxiang/bidd-aggmap/blob/master/paper/example/00_breast_cancer/00_WDBC_example_flow.ipynb) to find out more!

![Fmap](https://raw.githubusercontent.com/shenwanxiang/bidd-aggmap/master/docs/images/WDBC.png)

----

### Citation

Shen, Wan Xiang, et al. "AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks." Nucleic Acids Research 50.8 (2022): e45-e45.

----