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In silico tissue generation and power analysis for spatial omics
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- Baker, Ethan A. G. (författare)
- Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;MIT, Dept Biol, Cambridge, MA 02139 USA.
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- Schapiro, Denis (författare)
- Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;Harvard Med Sch, Lab Syst Pharmacol, Boston, MA 02115 USA.;Heidelberg Univ Hosp, Inst Computat Biomed, Fac Med, Heidelberg, Germany.;Heidelberg Univ, Heidelberg, Germany.;Heidelberg Univ Hosp, Inst Pathol, Fac Med, Heidelberg, Germany.
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- Dumitrascu, Bianca (författare)
- Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;Inst Adv Study, Sch Math, Princeton, NJ USA.;Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England.
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- Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA;MIT, Dept Biol, Cambridge, MA 02139 USA. Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;Harvard Med Sch, Lab Syst Pharmacol, Boston, MA 02115 USA.;Heidelberg Univ Hosp, Inst Computat Biomed, Fac Med, Heidelberg, Germany.;Heidelberg Univ, Heidelberg, Germany.;Heidelberg Univ Hosp, Inst Pathol, Fac Med, Heidelberg, Germany. (creator_code:org_t)
- 2023-03-02
- 2023
- Engelska.
- Ingår i: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 20:3, s. 424-
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- 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. This paper presents a statistical framework for power analysis of spatial omics studies, facilitated by an in silico tissue-generation method.
Ämnesord
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
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