Thinking process templates for constructing data stories with SCDNEY

Abstract

Background: Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods: We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results: Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions: Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term ‘living’.

A perspective on FAIR quality control in multiplexed imaging data processing

“…Software frameworks that aim to provide technology agnostic APIs for spatial omics image data are crucial for all the relevant data elements to be kept together in an accessible manner. Many existing frameworks offer great starting points (Palla et al, 2022;Righelli et al, 2022;Couto et al, 2023;Marconato et al, 2023;Moses et al, 2023) but lack certain requirements for comprehensive handling of spatial omics data, such as representation of annotation elements like polygons, support for large images and facilitation of data transformation to a common coordinate framework. For example, emObject is a framework that provides the ability to link annotation tables to segmentation masks (Baker et al, 2023b).…”