From 30b79f286eb8fb088517e34aee53efa4ae2b8e33 Mon Sep 17 00:00:00 2001 From: haozhu233 Date: Wed, 27 Mar 2024 02:07:07 +0000 Subject: [PATCH] deploy: 8fd1661a20bf100e9c80565ceb5efe9ffdb992d4 --- _sources/quick_tour.md | 9 ++++++-- quick_tour.html | 50 ++++++++++++++++++++++-------------------- searchindex.js | 2 +- 3 files changed, 34 insertions(+), 27 deletions(-) diff --git a/_sources/quick_tour.md b/_sources/quick_tour.md index dd20f7e..8c21f24 100644 --- a/_sources/quick_tour.md +++ b/_sources/quick_tour.md @@ -23,7 +23,7 @@ Here we use the `mESC` data from the BEELINE benchmark. The `mESC` data comes fr If you want to see the inference on a larger network with 14,000+ genes and 8,000+ cells, check out the other example. -``` +```python bl_dt, bl_gt = rd.data.load_beeline( benchmark_data='mESC', benchmark_setting='1000_STRING' ) @@ -31,11 +31,16 @@ bl_dt, bl_gt = rd.data.load_beeline( Here, `load_beeline` gives you a tuple, where the first element is an anndata of the single cell experession data and the second element is an array of all the ground truth links (based on the STRING network in this case). -``` +```md +```python bl_dt ``` + +```{code-output} AnnData object with n_obs × n_vars = 421 × 1620 obs: 'cell_type', 'cell_type_index' +``` +``` ## GRN Inference diff --git a/quick_tour.html b/quick_tour.html index 199bb99..6180768 100644 --- a/quick_tour.html +++ b/quick_tour.html @@ -309,8 +309,6 @@

Contents

@@ -340,38 +338,44 @@

Data Loadingregdiffusion package comes with a set of preprocessed data, including the BEELINE benchmarks, Hammond microglia in male adult mice, and another labelled microglia subset from a mice cerebellum atlas project.

Here we use the mESC data from the BEELINE benchmark. The mESC data comes from Mouse embryonic stem cells. It has 421 cells and 1,620 genes.

If you want to see the inference on a larger network with 14,000+ genes and 8,000+ cells, check out the other example.

-
bl_dt, bl_gt = rd.data.load_beeline(
+
bl_dt, bl_gt = rd.data.load_beeline(
     benchmark_data='mESC', benchmark_setting='1000_STRING'
 )
 

 

 
Here, load_beeline gives you a tuple, where the first element is an anndata of the single cell experession data and the second element is an array of all the ground truth links (based on the STRING network in this case).

-
bl_dt
+
```python
+bl_dt
 

 

-
AnnData object with n_obs × n_vars = 421 × 1620
-obs: ‘cell_type’, ‘cell_type_index’

-
-

-
GRN Inference#

-
You are recommended to use the provided trainer to train a RegDiffusion Model. You need to provide the expression data in a numpy array to the trainer.

-
During the training process, the training loss and the average amount of change on the adjacency matrix are provided on the progress bar. The model converges when the step change n the adjacency matrix is near-zero. By default, the train method will train the model for 1,000 iterations. It should be sufficient in most cases. If you want to keep training the model afterwards, you can simply call the train methods again with the desired number of iterations.

-
rd_trainer = rd.RegDiffusionTrainer(bl_dt.X)
-rd_trainer.train()
+

+## GRN Inference
+
+You are recommended to use the provided trainer to train a RegDiffusion Model. You need to provide the expression data in a numpy array to the trainer. 
+
+During the training process, the training loss and the average amount of change on the adjacency matrix are provided on the progress bar. The model converges when the step change n the adjacency matrix is near-zero. By default, the `train` method will train the model for 1,000 iterations. It should be sufficient in most cases. If you want to keep training the model afterwards, you can simply call the `train` methods again with the desired number of iterations. 
+
+

+

+
rd_trainer = rd.RegDiffusionTrainer(bl_dt.X)
+rd_trainer.train()

+

+We run this experiment on an A100 card and the inference finishes within 8 seconds. 
+
+When ground truth links are avaiable, you can test the inference performance by setting up an evaluator. You need to provide both the ground truth links and the gene names. Note that the order of the provided gene names here should be the same as the column order in the expression table (and the inferred adjacency matrix). 
+
 

 

-
We run this experiment on an A100 card and the inference finishes within 8 seconds.

-
When ground truth links are avaiable, you can test the inference performance by setting up an evaluator. You need to provide both the ground truth links and the gene names. Note that the order of the provided gene names here should be the same as the column order in the expression table (and the inferred adjacency matrix).

-
evaluator = rd.evaluator.GRNEvaluator(bl_gt, bl_dt.var_names)
-inferred_adj = rd_trainer.get_adj()
-evaluator.evaluate(inferred_adj)
+
evaluator = rd.evaluator.GRNEvaluator(bl_gt, bl_dt.var_names)
+inferred_adj = rd_trainer.get_adj()
+evaluator.evaluate(inferred_adj)

+

+## GRN object
+
+In order to facilitate the downstream analyses on GRN, we defined an `GRN` object in the `regdiffusion` package. You need to provide the gene names in the same order as in your expression table.
 

 

 

-

-
GRN object#

-
In order to facilitate the downstream analyses on GRN, we defined an GRN object in the regdiffusion package. You need to provide the gene names in the same order as in your expression table.

-

 
 
 
@@ -421,8 +425,6 @@ 
GRN object
 
  • Requirements
    • 
 
  • Data Loading
    • 
-
  • GRN Inference
    • 
-
  • GRN object
    • 
 
   
    
 
diff --git a/searchindex.js b/searchindex.js
index d99f8f4..49d0eb5 100644
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+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file