Issue_33 and 31 fix

A fix related to #33 and #31

README.md

@@ -11,15 +11,24 @@ Palantir is an algorithm to align cells along differentiation trajectories. Pala
         $> pip install palantir
 
 2. Palantir depends on a number of `python3` packages available on pypi and these dependencies are listed in `setup.py`
-All the dependencies will be automatically installed using the above commands
+
+    All the dependencies will be automatically installed using the above commands
 
 3. To uninstall:
 
 		$> pip uninstall palantir
 
 4. If you would like to determine gene expression trends, please install <a href="https://cran.r-project.org"> R <a> programming language and the R package <a href="https://cran.r-project.org/web/packages/gam/">GAM </a>. You will also need to install the rpy2 module using 
 
+		$> pip install .['PLOT_GENE_TRENDS']
+		    OR,
 		$> pip install rpy2
+
+    In case of compiler error during installation of `rpy2`, try to link your compiler in `env`. Example:
+
+        $> env CC=/usr/local/Cellar/gcc/xxx/bin/gcc-x pip install .['PLOT_GENE_TRENDS']
+
+    where `x` should be replaced with the version numbers
 		
 5. Palantir can also be used with [**Scanpy**](https://github.com/theislab/scanpy). It is fully integrated into Scanpy, and can be found under Scanpy's external modules ([link](https://scanpy.readthedocs.io/en/latest/api/scanpy.external.html#external-api))
 
@@ -70,6 +79,10 @@ ____
 Release Notes
 -------------
 
+### Version 0.2.6
+
+ * A fix to [issue#33](https://github.com/dpeerlab/Palantir/issues/33) and [issue#31](https://github.com/dpeerlab/Palantir/issues/31)
+
 ### Version 0.2.5
 
  * A fix related to [issue#28](https://github.com/dpeerlab/Palantir/issues/28). When identifying terminal states, duplicate values were generated instead of unique ones.

setup.py

@@ -40,9 +40,11 @@
         "matplotlib>=2.2.2",
         "seaborn>=0.8.1",
         "tzlocal",
-        "rpy2>=3.0.2",
         "scanpy",
     ],
+    extras_require={
+        'PLOT_GENE_TRENDS': ["rpy2>=3.0.2"]
+    },
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",

src/palantir/utils.py

@@ -22,11 +22,13 @@ def run_pca(data, n_components=300):
     return pca_projections, pca.explained_variance_ratio_
 
 
-def run_diffusion_maps(data_df, n_components=10, knn=30, n_jobs=-1, alpha=0):
+def run_diffusion_maps(data_df, n_components=10, knn=30, alpha=0):
     """Run Diffusion maps using the adaptive anisotropic kernel
 
-    :param data_df: PCA projections of the data or adjancency matrix
+    :param data_df: PCA projections of the data or adjacency matrix
     :param n_components: Number of diffusion components
+    :param knn: Number of nearest neighbors for graph construction
+    :param alpha: Normalization parameter for the diffusion operator
     :return: Diffusion components, corresponding eigen values and the diffusion operator
     """
 
@@ -36,7 +38,11 @@ def run_diffusion_maps(data_df, n_components=10, knn=30, n_jobs=-1, alpha=0):
         print("Determing nearest neighbor graph...")
         temp = sc.AnnData(data_df.values)
         sc.pp.neighbors(temp, n_pcs=0, n_neighbors=knn)
-        kNN = temp.uns["neighbors"]["distances"]
+        # maintaining backwards compatibility to Scanpy `sc.pp.neighbors`
+        try:
+            kNN = temp.uns["neighbors"]["distances"]
+        except KeyError:
+            kNN = temp.obsp['distances']
 
         # Adaptive k
         adaptive_k = int(np.floor(knn / 3))

src/palantir/version.py

@@ -1,3 +1,3 @@
-__version__ = "0.2.5"
+__version__ = "0.2.6"
 __author__ = "Manu Setty"
 __author_email__ = "manu.talanki@gmail.com"