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

Abstract

The recently developed spatial transcriptomics (ST) technique has made it possible to view spatial transcriptional heterogeneity in a high throughput manner. It is based on highly multiplexed sequence analysis and uses barcodes to split the sequenced reads into respective tissue locations. However, this type of sequencing technique suffers from high noise and drop-out events in the data, which makes smoothing a necessary step before performing downstream analysis. Traditional smoothing methods used in the similar single cell RNA sequencing (scRNA-seq) data are one-factor methods that can only utilize associations in transcriptome space. Since they do not account for associations in the Euclidean space, i.e. tissue location distances on the ST slide, these one-factor methods cannot take full advantage of all the knowledge in ST data. In this study, we present a novel two-factor smoothing technique, Spatial and Pattern Combined Smoothing (SPCS), that employs k-nearest neighbor technique to utilize associations from transcriptome and Euclidean space from the ST data. By performing SPCS on 10 ST slides from pancreatic ductal adenocarcinoma (PDAC), smoothed ST slides have better separability, partition accuracy, and biological interpretability than the ones smoothed by pre-existing one-factor smoothing methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).