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docs/index.rst

@@ -2,25 +2,62 @@ RegDiffusion
 =============
 
 RegDiffusion is a very fast regulatory network inference algorithm based on 
-probabilistic diffusion model. It works well on genes and is capable to predict 
-biologically verifiable links from single cell RNA-seq data.
+probabilistic diffusion model. It works well on genes and is capable to rapidly
+predict biologically verifiable links from large single cell RNA-seq data with 
+10,000+ genes.
 
+Installation
+------------
+
+``regdiffusion`` is available on pypi:
+
+    pip install regdiffusion
+
+Quick Tour
+----------
+This package `regdiffusion` provides the official implementation of the
+RegDiffusion algorithm and a set of easy-to-use companion tools to evaluate,
+analyze, and visualize the inferred network. We also provide access tools to 
+GRN benchmarks and preprocessed single cell datasets for evaluation. 
+
+We tried to keep the top level interface straightforward. Right now, it only 
+consists of 4 components: the `RegDiffusionTrainer` class, the `GRN` class, the 
+`GRNEvaluator` class, and the `data` module. 
+
+- `RegDiffusionTrainer`: You can use it to train a `RegDiffusion` model by 
+  providing log transformed expression data in a `numpy` array. The training
+  process could be either started or continued using the `.train()` method. You 
+  can export the inferred `GRN` using the `.get_grn()` method.
+- `GRN`: The `GRN` class provides a container to save the inferred adjacency
+  matrix and the corresponding gene names. You can save the `GRN` object to 
+  a local `HDF5` file using the `.to_hdf5()` method and reload the saved file 
+  using the `read_hdf5()` function. It also comes with functionalities to 
+  export or visualize local regions. 
+- `GRNEvaluator`: The ground truth of regulatory relationship often exist as 
+  list of edges but the values to be evaluated are often in adjacency matrix. 
+  The `GRNEvaluator` class is designed to fill the gap. Right now it supports
+  common metrics such as AUROC, AUPR, AUPR Ratio, EP, and EPR. 
+- `data` module: Right now, the `data` module includes quick access to BEELINE 
+  benchmarks and our preprocessed single cell datasets on mouse microglia. 
+
+Inference Speed
+---------------
 Inference on networks with 15,000 genes takes under 5 minutes with modern 
 accelerated computing. In contrast, previous VAE based models would take more 
 than 4 hours on the same device. Even if you don't have access to those fancy 
 GPU cards, RegDiffusion still works. Inference on the same large network takes 
 roughly 3 hours on a mid-range 12-core CPU. It does run slower but it's still 
 faster than previous algorithm on GPU thanks to the improvement on algorithm. 
 
-Installation
-------------
+Citation
+--------
+Please consider cite our work if you found it useful for your work:
 
-``regdiffusion`` is available on pypi:
 
-    pip install regdiffusion
 
 .. toctree::
    :caption: Get Started:
+   :hidden:
 
    quick_tour