v1.3.2pr

README.md

@@ -201,10 +201,29 @@ clustered = metagene.clustering(
 # Heatmap with optimized distances between genes will be drawn
 clustered.draw().savefig("path/to/clustered_hm.pdf")
 ```
-Output for _Brachypodium distachyon_
+Output for _Brachypodium distachyon_ - CHG
 
 <p align="middle">
-    <img width="300" src="https://user-images.githubusercontent.com/43905117/280022981-d6d4ffff-7f3d-4e33-8e42-05b98ca28161.png">
+    <img width="300" src="https://user-images.githubusercontent.com/43905117/282321746-baf97da1-6a35-4a17-9772-5a2f4d67e6a4.png">
+</p>
+
+### Genes dynamicTreeCut
+
+To shrink clustered heat-map and capture main patterns genes can be split into modules using 
+[dynamicTreeCut algorithm](https://github.com/kylessmith/dynamicTreeCut/tree/master) by Peter Langfelder and Bin Zhang.
+Then genes can be plotted as heat-map as previous example:
+
+```python
+# Parameters are the same as for cutreeHybrid (see dynamicTreeCut)
+modules = clustered.modules(deepSplit = 1)
+
+modules.draw().savefig("path/to/modules_hm.pdf")
+```
+
+Output for _Brachypodium distachyon_ - CHG
+
+<p align="middle">
+    <img width="300" src="https://user-images.githubusercontent.com/43905117/282321739-df4486f6-9b1f-467e-87ea-cfd441f80e0a.png">
 </p>
 
 ### Smoothing the line plot

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "bismarkplot"
-version = "1.3.2a"
+version = "1.3.2pr"
 authors = [
   { name="shitohana", email="kyudytskiy@gmail.com" },
 ]

src/bismarkplot/BismarkPlot.py

@@ -472,6 +472,8 @@ def __init__(self, bismark_df: pl.DataFrame, count_threshold=5, dist_method="euc
 
         self.gene_labels = unpivot.with_columns(pl.col("label").cast(pl.Utf8))["label"].to_numpy()
         self.matrix = unpivot[list(map(str, range(self.total_windows)))].to_numpy()
+
+        self.gene_labels = self.gene_labels[~np.isnan(self.matrix).any(axis=1)]
         self.matrix = self.matrix[~np.isnan(self.matrix).any(axis=1), :]
 
         # dist matrix
@@ -597,7 +599,7 @@ def draw(
             show_size=False
     ) -> Figure:
         """
-        Method for visualiztion of moduled genes. Every row of heat-map represents an average methylation
+        Method for visualization of moduled genes. Every row of heat-map represents an average methylation
         profile of genes of the module.
 
         :param fig_axes: tuple(Fig, Axes) to plot
@@ -1622,8 +1624,10 @@ def draw(
         else:
             subplots_y = 1
 
-        if len(self.samples) > 1:
+        if len(self.samples) > 1 and subplots_y > 1:
             subplots_x = (len(self.samples) + len(self.samples) % 2) // subplots_y
+        elif len(self.samples) > 1:
+            subplots_x = len(self.samples)
         else:
             subplots_x = 1