PDBE-6103 (#7)

* x-axis lowered, vlines extended

* Python version bump and version bump

* Residue range parsable into dendro fname

It is now possible to parse in a residue range (possibly UniProt) into the application. This now affects the file name of the dendrogram, if rendered. If the residue IDs are not specified, behaviour reverts to normal

* Trial with 3.10.10

* Bump Python version to 3.10.14

* File name typo in dendrogram

* Updated docs and removed old examples

.github/workflows/main.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.10.12]
+        python-version: [3.10.14]
 
     steps:
     - uses: actions/checkout@v2
@@ -56,7 +56,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
         matrix:
-            python-version: [3.10.12]
+            python-version: [3.10.14]
     needs: build
 
     steps:

README.md

@@ -13,7 +13,7 @@ For intructions on importing `protein-cluster-conformers` into your own Python c
 `protein-cluster-conformers` requires >=Python3.10 to run. Initialise virtual environment and install dependencies with:
 
 ```shell
-$ cd contact_map_difference
+$ cd protein-cluster-conformers]
 $ python3.10 -m venv cluster_venv
 $ source cluster_venv/bin/activate
 $ python -m pip install -r requirements.txt
@@ -31,6 +31,7 @@ $ python find_conformers.py [-h] [-v] -u UNIPROT -m MMCIF [MMCIF ...]
                           	[-g PATH_DENDROGRAM [PATH_DENDROGRAM ...]]
                           	[-w PATH_SWARM [PATH_SWARM ...]] [-o PATH_HISTOGRAM]
                           	[-a PATH_ALPHA_FOLD]
+                            [-0 FIRST_RESIDUE_POSITION] [-1 LAST_RESIDUE_POSITION]
 ```
 
 The following parameters can be parsed:
@@ -58,6 +59,10 @@ optional arguments:
                         Path to save histograms of distance difference maps
   -a PATH_ALPHA_FOLD, --path_alpha_fold PATH_ALPHA_FOLD
                         Path to save AlphaFold Database structure
+  -0 FIRST_RESIDUE_POSITION, --first_residue_position FIRST_RESIDUE_POSITION
+                        First residue position in (UniProt) sequence
+  -1 LAST_RESIDUE_POSITION, --last_residue_position LAST_RESIDUE_POSITION
+                        Last residue position in (UniProt) sequence
 
 ```
 
@@ -148,7 +153,7 @@ The resulting plots are saved in PNG format (to save render time). E.g:
 **Example**: O34926
 
 ```shell
-$ python run_find_clusters.py -u "O34926" \
+$ python find_clusters.py -u "O34926" \
 	-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
@@ -163,22 +168,24 @@ $ python run_find_clusters.py -u "O34926" \
 From the clustering results, a dendrogram can be rendered to show the relationships between all clustered chains. To save a dendrogram of the hierarchical clustering results, run:
 
 ```shell
-$ python run_find_clusters.py -u "A12345" \
+$ python find_clusters.py -u "A12345" \
     -m /path/to/structure_1.cif [chains] \
     -m ... \
     -g /path/to/save/dendrogram/ [png svg]
 ```
 
 where either a `png` or `svg` file type is saved. E.g.
 
-<img src="./benchmark_data/figures/P14902_agglomerative_dendrogram.png" alt="Dendrogram of clustered UniProt:P14902 chains, via UPGMA agglomerative clustering" height="350"/>
+<img src="./benchmark_data/figures/O34926_1_405_agglomerative_dendrogram.png" alt="Dendrogram of clustered UniProt:P14902 chains, via UPGMA agglomerative clustering" width="400"/>
+
+<img src="./benchmark_data/figures/P15291_122_398_agglomerative_dendrogram.png" alt="Dendrogram of clustered UniProt:P14902 chains, via UPGMA agglomerative clustering" width="400"/>
 
 <br>
 
 **Example**: O34926
 
 ```shell
-$ python run_find_clusters.py -u "O34926" \
+$ python find_clusters.py -u "O34926" \
 	-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
@@ -193,7 +200,7 @@ $ python run_find_clusters.py -u "O34926" \
 The scores generated between pairwise structure comparisons can be plotted as a swarm plot by parsing the `-w` flag:
 
 ```shell
-$ python run_find_clusters.py -u "A12345" \
+$ python find_clusters.py -u "A12345" \
     -m /path/to/structure_1.cif [chains] \
     -m ... \
     -w /path/to/save/swarm_plot/ [png svg]
@@ -269,7 +276,7 @@ $ ./run_O34926.sh
 **Example #2:** P15291
 
 ``` shell
-python3 run_find_conformers.py -u "P15291" \
+python3 find_conformers.py -u "P15291" \
     -m benchmark_data/examples/P15291/P15291_updated_mmcif/2fy7_updated.cif A \
     -m benchmark_data/examples/P15291/P15291_updated_mmcif/2fya_updated.cif A \
     -m benchmark_data/examples/P15291/P15291_updated_mmcif/2fyb_updated.cif A \

__version__.py

@@ -1 +1 @@
-__version__ = "1.1.0"
+__version__ = "1.2.0"

cluster_conformers/cluster_chains.py

@@ -105,7 +105,8 @@ def plot_dendrogram(
     unp: str, axis, linkage_matrix: ndarray = None, cutoff: float = None, **kwargs
 ) -> "tuple(Figure, Axes)":
     """
-    Create linkage matrix from SKLearn model and plot the dendrogram of nodes.
+    Create linkage matrix from SKLearn model and plot the dendrogram of nodes. Applies
+    this to parsed Matplotlib axis, inplace.
 
     :param unp: UniProt accession
     :type unp: str
@@ -123,26 +124,48 @@ def plot_dendrogram(
     """
 
     # Plot the corresponding dendrogram
-    dn = dendrogram(linkage_matrix, ax=axis, **kwargs)
-    del dn
+    dendrogram_plot = dendrogram(linkage_matrix, ax=axis, **kwargs)
+
+    hline_props = {
+        "colors": ["black"],
+        "linestyles": ["dashed"],
+        "linewidths": 1,
+        "alpha": 0.5,
+    }
 
     # Add horizontal line where cutoff is placed
     if cutoff:
-        max_parent = max(linkage_matrix[:, 2])
+        max_parent = linkage_matrix[:, 2].max()
         axis_xlimits = axis.get_xlim()
         axis.hlines(
             y=max_parent * cutoff,
             xmin=axis_xlimits[0],
             xmax=axis_xlimits[1],
-            colors=["black"],
-            linestyles=["dashed"],
-            linewidths=1,
-            alpha=0.5,
+            **hline_props,
         )
 
-    axis.set_title(f"Agglomerative clustering dendrogram: {unp}", fontweight="bold")
+    axis.set_title(f"Agglomerative clustering results: {unp}", fontweight="bold")
     axis.set_ylabel("Score (\u212B)")
 
+    # Set the y-axis limits
+    rock_bottom = -max_parent * 0.025
+    axis.set_ylim(rock_bottom, max_parent * 1.05)
+
+    # Add vlines for each leaf below the x-axis
+    axis.vlines(
+        x=axis.get_xticks(),
+        ymin=rock_bottom,
+        ymax=0,
+        colors=dendrogram_plot["leaves_color_list"],
+        linestyles=["solid"],
+        linewidths=1,
+        # alpha=0.5,
+    )
+
+    axis.hlines(y=0, xmin=axis_xlimits[0], xmax=axis_xlimits[1], **hline_props)
+
+    del dendrogram_plot
+
 
 def plot_swarmplot(y_data: Iterable, unp: str) -> "tuple(Figure, Axes)":
     """Creates a strip plot of non-overlapping data points for a given list of data. The

cluster_conformers/cluster_monomers.py

@@ -672,6 +672,7 @@ def render_dendrogram(
     path_save: PosixPath = None,
     png: bool = False,
     svg: bool = False,
+    unp_range: "tuple[int, int]" = None,
 ) -> None:
     """
     Plot hierachical dendrogram from clustering results. Must have a linkage matrix and
@@ -684,6 +685,7 @@ def render_dendrogram(
     :type png: bool, optional
     :param svg: Save dendrogram image in SVG format, defaults to False
     :type svg: bool, optional
+    :param unp_range: Range of UniProt residues used for clustering
     """
 
     # Set matplotlib global formatting
@@ -718,14 +720,24 @@ def render_dendrogram(
             leaf_rotation=90,
         )  # p=3
 
+        # UniProt residue range specified, make modifications (optional)
+        if unp_range:
+            ax.set_title(
+                f"Agglomerative clustering results: {unp} ({unp_range[0]}-{unp_range[1]})",
+                fontweight="bold",
+            )
+            fname = f"{unp}_{unp_range[0]}_{unp_range[1]}_agglomerative_dendrogram"
+        else:
+            fname = f"{unp}_agglomerative_dendrogram"
+
+        # Save file
         io_utils.save_figure(
             path_save,
-            save_fname=f"{unp}_agglomerative_dendrogram",
+            save_fname=fname,
             png=png,
             svg=svg,
         )
 
-        # plt.clf()
         plt.close(fig=fig)
 
     else:

cluster_conformers/utils/appearance_utils.py

@@ -11,5 +11,5 @@ def init_plot_appearance():
     """
 
     # Figure parameters and formatting
-    plt.style.use("seaborn-v0_8-colorblind")
+    # plt.style.use("seaborn-v0_8-colorblind")
     plt.style.use("fast")

examples/run_O34926.sh

@@ -7,7 +7,7 @@ rm benchmark_data/examples/O34926/O34926_cluster_results/*
 
 # mprof run --python
 
-python find_conformers.py -u "O34926" \
+python3 find_conformers.py -u "O34926" \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
@@ -18,6 +18,8 @@ python find_conformers.py -u "O34926" \
     -i 3nc6 \
     -f \
     -g benchmark_data/examples/O34926/O34926_cluster_results/ png svg \
+    -0 1 \
+    -1 405
     # -v \
     # -a benchmark_data/examples/O34926/O34926_alpha_fold_mmcifs