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

Abstract

High dimensional, localized RNA sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffers from high noise and drop-out events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage 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 information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC), and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy, and biological interpretability than the ones smoothed by pre-existing one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).