Thinking Process Templates for Constructing Data Stories with SCDNEY

Overview

Journal F1000Res

Specialties Biomedical Engineering
Science

Date 2024 Mar 4

PMID 38434622

Authors

Yue Cao

Andy Tran

Hani Kim

Nick Robertson

Yingxin Lin

Marni Torkel

Pengyi Yang

Ellis Patrick

Shila Ghazanfar

Jean Yang

Affiliations

Soon will be listed here.

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'.

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