Python implementation of SMASH

How to install?

We recommend downloading the Github repository as a ZIP file and unpacking it. The users need to change the "path" (currently, path = "/Users/sealso/Documents/GitHub/SMASH-package") mentioned at the start of each notebook to the location of the unpacked SMASH-package-main folder in their system.

Overview of the main functions

• The function SMASH fits our proposed method. Along with the p-values corresponding to SMASH, the function also returns two additional results corresponding to approximate versions of the two methods SPARK-X [1] and SpatialDE [2].

• The function Expression_plot can be used for basic visualization of a gene expression in cells/spots.

Jupyter notebooks with function usage and two real data analyses

• The Jupyter notebook entitled Merfish_analysis.ipynb provides a thorough guide on how to use the package on a mouse cerebellum data collected using the MERFISH platform [3]. This notebook explains all the arguments that can be tweaked in the main functions.

• The Jupyter notebook entitled Human_DLPFC_Visium_analysis.ipynb provides a guide on how to use the package on a human DLPFC data collected using the 10X Visium platform [4].

Python modules required to be installed

• The package and the notebooks provided, requires the following modules to be pre-installed,
  1. matplotlib, install using: "pip install matplotlib" (https://matplotlib.org/)
  2. matplotlib_venn, install using: "pip install matplotlib-venn" (https://pypi.org/project/matplotlib-venn/)
  3. blosc, install using: "pip install blosc" (https://anaconda.org/anaconda/blosc)

• We recommend using Anaconda (https://www.anaconda.com/products/individual) and Python version > 3.9.

Data provided

Two datasets: (1) a mouse cerebellum data collected using the MERFISH platform [3], and (2) a human DLPFC data collected using the 10X Visium platform [4], are provided as pickled python objects in the Data folder.

References

1. Zhu, J., Sun, S., Zhou, X.: Spark-x: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biology 22(1), 1–25 (2021)

2. Svensson, V., Teichmann, S.A., Stegle, O.: Spatialde: identification of spatially variable genes. Nature methods 15(5), 343–346 (2018)

3. Maynard, K.R., Collado-Torres, L., Weber, L.M., Uytingco, C., Barry, B.K., Williams, S.R., Catallini, J.L., Tran, M.N., Besich, Z., Tippani, M., et al.: Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature neuroscience 24(3), 425–436 (2021)

4. Moffitt, J.R., Bambah-Mukku, D., Eichhorn, S.W., Vaughn, E., Shekhar, K., Perez, J.D., Rubinstein, N.D., Hao, J., Regev, A., Dulac, C., et al.: Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362(6416), 5324 (2018)