In silico tissue generation and power analysis for spatial omics.


Type
Article
Change log
Abstract

As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.

Description

Funder: KMB Foundation


Funder: Royal Swedish Academy of Sciences


Funder: Svenska Läkaresällskapet

Keywords
Proteins, RNA, In Vitro Techniques, Multiomics
Journal Title
Nat Methods
Conference Name
Journal ISSN
1548-7091
1548-7105
Volume Title
20
Publisher
Springer Science and Business Media LLC
Sponsorship
Bundesministerium für Bildung und Forschung (BMBF 01ZZ2004)
Swiss National Science Foundation (181475, P2ZHP3_181475)
U.S. Department of Health &amp (HHSN261201500003I, HHSN261100039)
National Science Foundation (DMS-1638521, DMS-1638352, 1745302)
Damon Runyon Cancer Research Foundation (DRQ-03-20)
U.S. Department of Health &amp (HHSN261100039)
NCI NIH HHS (HHSN261201500003I)