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+[![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.
+
+----
+