SPCS: A Spatial and Pattern Combined Smoothing Method for Spatial Transcriptomic Expression

Yusong Liu, Tongxin Wang, Ben Duggan, Kun Huang, Jie Zhang, Xiufen Ye, Travis S. Johnson
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Indiana University School of Medicine

If there's any question about this project, please send emails to johnstr@iu.edu or yexiufen@hrbeu.edu.cn.

The method

Spatial and Pattern Combined Smoothing (SPCS) is a novel two-factor smoothing technique, that employs k-nearest neighbor technique to utilize associations from transcriptome and Euclidean space from the Spatial Transcriptomic (ST) data. This reposition is an R implementation of SPCS method, including tutorial and a test slide. The test slide is one of pancreatic ductal adenocarcinoma (PDAC) slide provided in (Moncada R, et.al 2020).

The dependencies

This is an R implementation based on following packages:

• Matrix 1.3.4
• rsvd 1.0.5
• factoextra 1.0.7
• foreach 1.5.1
• dplyr 1.0.7
• doParallel 1.0.16

To draw the heatmap of gene expression, please also include:

• ggplot2 3.3.5

P.S: Other versions of these packages may also work well with our implementation.

The tutorial

We have provided a step-by-step tutorial for our SPCS R implementation. Please see "tutorial.md" for details.

The citation

If any code in this reposition is used in any publishable works, please citing:

• Liu Y, Wang T, Duggan B et al., "SPCS: A Spatial and Pattern Combined Smoothing Method for Spatial Transcriptomic Expression", Briefings in Bioinformatics (2022), bbac116, doi: https://doi.org/10.1093/bib/bbac116.

If the test data in this reposition is used in any publishable works, please citing:

• Moncada R, Barkley D, Wagner F et al., "Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas", Nature Biotechnology (2020), 38:333-342, doi: https://doi.org/10.1038/s41587-019-0392-8.