Reference: Xiao Wang1^, William E. Allen1,2^, Matthew A. Wright1,3, Emily L. Sylwestrak1, Nikolay Samusik4, Sam Vesuna1, Kathryn Evans1, Cindy Liu1, Charu Ramakrishnan1, Jia Liu5, Garry P. Nolan4,†, Felice-Alessio Bava4,†, Karl Deisseroth1,3,6,†, "Three-dimensional intact-tissue sequencing of single-cell transcriptional states" Science (2018), DOI: 10.1126/science.aat5691.

1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. 2Neuroscience Program, Stanford University, CA 94305, USA. 3Department of Psychiatry and Behavioral Sciences, Stanford University, CA 94305, USA. 4Baxter Laboratory, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. 5Department of Chemical Engineering, Stanford University, CA 94305, USA. 6Howard Hughes Medical Institute, Stanford University, CA 94305, USA. ↵†Corresponding author. Email: [email protected] (K.D.); [email protected] (F.-A.B.); [email protected] (G.P.N.)

As of 6/21/18, this code is under active development to transition into a user-friendly package. Please contact [email protected] with questions or comments.

Required MATLAB version: R2017B or later

Required Python packages:

• skimage
• scipy
• pandas
• joblib
• seaborn
• numpy
• matplotlib
• tifffile
• cv2
• statsmodels
• umap

We would like to acknowledge the following open source resources:

• SciPy: Jones E., Oliphant E., Peterson P., et al. SciPy: Open Source Scientific Tools for Python, 2001
• Numpy: Oliphant E. A., guide to NumPy, USA: Trelgol Publishing, (2006).
• Matplotlib: Hunter J. D., Matplotlib: A 2D graphics environment, Computing In Science & Engineering, (2007)
• Scikit-learn: Pedregosa et al., Scikit-learn: Machine learning in Python, JMLR 12, pp. 2825-2830, (2011)
• Ilastik: Sommer C., Strähle  C., Köthe U., Hamprecht F. A. , ilastik: Interactive Learning and Segmentation Toolkit, Eighth IEEE International Symposium on Biomedical Imaging (ISBI) Proceedings, (2011)
• UMAP: McInnes L. and Healy J., UMAP:  Uniform Manifold Approximation and Projection for Dimension Reduction https://arxiv.org/abs/1802.03426 (2018)
• Pandas:  Pandas: McKinney W. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010)
• Scikit-image: van der Walt S. et al., scikit-image: Image processing in Python, PeerJ 2:e453 (2014) 
• IPython: Perez F., Granger B.E. IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, 9, 21-29 (2007), 
• Seaborn: https://seaborn.pydata.org/
• Statsmodels: Skipper S., and  Perktold J.. Statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference (2010)
• OpenCV: Bradski G., The OpenCV LibraryDr. Dobb's Journal of Software Tools (2000)