Bump to version 1.0!

README.md

@@ -149,10 +149,20 @@ Note that the gradient descent portion of the t-SNE visualization step can take
 
 Scripts for performing additional analyses of the data are also available in the `bin/` directory.
 
-#### Scanorama implementation
+## Scanorama implementation
 
 For those interested in the algorithm implementation, `scanorama/scanorama.py` is the main file that handles the mutual nearest neighbors-based matching, batch correction, and panorama assembly.
 
+## Troubleshooting
+
+- Make sure the input matrices are cells-by-genes, not the transpose.
+
+- For the example scripts, be sure to run `bin/process.py` first, although this is not necessary if you are using Scanorama through the API.
+
+- For large data set integration under memory constraints (e.g., if you run into a `MemoryError`), try lowering the `batch_size` parameter. And stay tuned for more improvements!
+
+- Scanorama versions 0.2 through 0.6.1 had default parameters that resulted in non-optimal batch correction results (integration was unaffected). Upgrade to the latest version for a fix to this issue.
+
 ## Questions
 
 For questions about the pipeline and code, contact brianhie@mit.edu. We will do our best to provide support, address any issues, and keep improving this software. And do not hesitate to submit a pull request and contribute!

setup.py

@@ -2,10 +2,10 @@
 
 setup(
     name='scanorama',
-    version='0.7.1',
+    version='1.0',
     description='Panoramic stitching of heterogeneous single cell transcriptomic data',
     url='https://github.com/brianhie/scanorama',
-    download_url='https://github.com/brianhie/scanorama/archive/v0.7.1.tar.gz',
+    download_url='https://github.com/brianhie/scanorama/archive/v1.0.tar.gz',
     packages=find_packages(exclude=['bin', 'conf', 'data', 'target']),
     install_requires=[
         'annoy>=1.11.5',